http://www.iaees.org/publications/journals/nb/nb.asp International Academy of Ecology and Environmental Sciences Network Biology (ISSN 2220-8879); Publisher: International Academy of Ecology and Environmental Sciences; Address: Unit 3, 6/F., Kam Hon Industrial Building, 8 Wang Kwun Road, Kowloon Bay, Hong Kong; Tel: 00852-2138 6086; Fax: 00852-3069 195; E-mail: office@iaees.org RSS is a means of receiving content across the internet without having to visit websites directly. When you see the RSS tag on a web page, then you know that site offers an RSS feed. You may download a RSS reader (e.g., at http://www.iaees.org/tools/RSSOwl-java.zip). Install it on your computer and create a new folder, and then create a new feed with a RSS feed address, e.g., http://www.iaees.org/publications/journals/piaees/rss.xml, then set something. Any update of RSS feed site will automatically reach your RSS reader. If you have subscribed an online journal by RSS, journal contents or articles will reach you once the latest issue is available or the latest article is published. http://www.iaees.org/publications/journals/nb/online-version.asp International Academy of Ecology and Environmental Sciences Networks are mathematically directed (in practical applications also undirected) graphs and a graph is a one-dimensional abstract complex, i.e., a topological space. Network theory focuses on various topological structures and properties, dynamic properties, and functionality-topology relationship, etc. There are some common mathematical foundations, theories and methodology for network analysis, in which graph theory, statistics, and operational research, etc., are the fundamental sciences of network analysis. Various emerging biological networks, at both micro- and macro- levels, will provide numerous sources for the development of general network theory and methodology and also facilitate the development of theory and methodology of biological networks.Biological network analysis is a fast moving science. Many core scientific issues, for example, ecological structure, co-evolution, co-extinction and biodiversity conservation in ecology, and cancer development and metabolic regulation in health science, etc., are expected to be addressed by network approaches and network analysis. Network analysis is becoming the core methodology to treat complex biological systems. As the fast development of this area, more and more papers on biological networks are published. At present explosive numbers of quality papers on biological networks are being published each year. The initiation of the journal, Network Biology, will provide a public and unified platform for the publication of these studies. From this integrated and unique journal, researchers, university teachers and students will thoroughly have an in-depth and complete insight on knowledge, methodology and recent advances of biological networks. In the view of system dynamics, biological networks are always self-organized systems with emergent, autonomous and adaptive properties. Their dynamics can be represented by agent-based modeling, individual-based modeling and some other methodologies like neural network modeling. Therefore, agent-based modeling, individual-based modeling, self-organization of biological systems, and neural network modeling, etc., fall into the aims and scope of the journal, Network Biology. The Network Biology (ISSN 2220-8879) is an open access, peer-reviewed online journal that considers scientific articles in all different areas of network biology (http://www.iaees.org/publications/journals/nb/nb.asp; http://www.iaees.org/publications/journals/nb/online-version.asp). It is the transactions of the International Society of Network Biology.It dedicates to the latest advances in network biology. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to: (1) Theories, algorithms and programs of network analysis; (2) Innovations and applications of biological networks; (3) Ecological networks, food webs and natural equilibrium; (4) Co-evolution, co-extinction, biodiversity conservation; (5) Metabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs; (6) Physiological networks; (7) Network regulation of metabolic processes, human diseases and ecological systems; (8) Social networks, epidemiological networks; (9) System complexity, self-organized systems, emergence of biological systems, agent-based modeling, individual-based modeling, neural network modeling, etc. We are also interested in short communications that clearly address a specific issue or completely present a new ecological network, food web, or metabolic or gene network, etc. Authors can submit their works to the email box of this journal, networkbiology@iaees.org. All manuscripts submitted to Network Biology must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal. In addition to free submissions from authors around the world, special issues are also accepted. The organizer of a special issue can collect submissions (yielded from a research project, a research group, etc.) on a specific topic, or submissions of a conference for publication of special issue. http://www.iaees.org/publications/journals/nb/articles/2011-1(1)/How-to-be-a-specialist.pdf Carsten F. Dormann. Network Biology,2011,1(1):1-20 The analysis of ecological networks has gained a very prominent foothold in ecology over the last years. While many publications try to elucidate patterns about the networks, others are primarily concerned with the role of specific species in the network. The core challenge here is to tell specialists from generalists. While field data and observations can be used to directly assess specialisation levels, the indirect way through networks is burdened with problems. Here, I review eight measures to quantify specialisation in pollination networks (degree, node specialisation, betweenness, closeness, strength, pollination support, Shannon's H and discrimination d'), the first four being based on binary, the others on weighted network data. All data and R-code are available as supplement and can be applied beyond pollination networks. The indices convey different concepts of specialisation and hence quantify different aspects. Still, there is some redundancy, with node specialisation and closeness quantifying the same properties, as do degree, betweenness and Shannon's H. Using artificial and real network data, I illustrate the interpretation of the different indices and the importance of using a null model to correct for expectations given the different observed frequencies of interactions. For a well-described network the distributions of specialisation values do not differ from null model expectations for most indices. Finally, I investigate the effect of cattle grazing on the specialisation of an important pollinator in eight replicated pollination networks as an illustration of how to employ the specialisation indices, null models and permutation-based statistics in the analysis of specialisation in pollination networks. http://www.iaees.org/publications/journals/nb/articles/2011-1(1)/Escherichia-coli-transcriptional-regulatory-network.pdf Agustino Martinez-Antonio. Network Biology,2011,1(1):21-33 Escherichia coli is the most well-know bacterial model about the function of its molecular components. In this review are presented several structural and functional aspects of their transcriptional regulatory network constituted by transcription factors and target genes. The network discussed here represent to 1531 genes and 3421 regulatory interactions. This network shows a power-law distribution with a few global regulators and most of genes poorly connected. 176 of genes in the network correspond to transcription factors, which form a sub-network of seven hierarchical layers where global regulators tend to be set in superior layers while local regulators are located in the lower ones. There is a small set of proteins know as nucleoid-associated proteins, which are in a high cellular concentrations and reshape the nucleoid structure to influence the running of global transcriptional programs, to this mode of regulation is named analog regulation. Specific signal effectors assist the activity of most of transcription factors in E. coli. These effectors switch and tune the activity of transcription factors. To this type of regulation, depending of environmental signals is named the digital-precise-regulation. The integration of regulatory programs have place in the promoter region of transcription units where it is common to observe co-regulation among global and local TFs as well as of TFs sensing exogenous and endogenous conditions. The mechanistic logic to understand the harmonious operation of regulatory programs in the network should consider the globalism of TFs, their signal perceived, coregulation, genome position, and cellular concentration. Finally, duplicated TFs and their horizontal transfer influence the evolvability of members of the network. The most duplicated and transferred TFs are located in the network periphery. http://www.iaees.org/publications/journals/nb/articles/2011-1(1)/Characterization-in-silico-of-flavonoids-biosynthesis.pdf Arsenio Rodriguez, Diogenes Infante. Network Biology,2011,1(1):34-45 A detailed and curated map of molecular interactions taking place in the polyphenols (flavonoids) biosynthesis in Theobroma cacao L. is presented. The map was created using the software Cytoscape v.2.7 and the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. The statistical parameters of the network were determined with CentiScape v.1.1 and MetaAnalyzer v.2.6.1. A preliminary theoretical map containing 1024 chemical species and 1099 chemical reactions was built, then a second map that was curated and annotated with the biological facts obtained from approximately 85 publications in T. cacao, with 653 chemical species and 706 chemical reactions. Structural analysis of this interaction network revealed similitude with other biological networks. The study of complex networks opens the way for creating realistic computational models of flavonoid biosynthesis metabolic pathways in cacao. http://www.iaees.org/publications/journals/nb/articles/2011-1(1)/Mathematical-modeling-deciphering-balance.pdf Shruti Jain, Sunil V. Bhooshan, Pradeep K. Naik. Network Biology,2011,1(1):46-58 When modelling cell signalling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a deterministic numerical method to the analysis of a large, systematic dataset describing the dynamics of cell signalling downstream of, tumor necrosis factor-a (TNF), epidermal growth factor (EGF), and insulin receptors in human colon carcinoma cells. Deterministic modeling is useful as a means to assemble and test what we know about proteins and networks. We extensively study the space of parameters to show that the model is structurally stable and robust over a broad range of parameter values. We have made the biological view of the main paths of the insulin input showing cell survival and cell death. Than with the help of different parameters relating to that protein present in the model we have designed scheme of the biochemical paths/deterministic model. With those parameters equations were formed which vary with time i.e. differential equation. Thus, our model is suitable for implementation in multi-scale simulation programs that are presently under development to study the behavior of large tumor cell populations. http://www.iaees.org/publications/journals/nb/articles/2011-1(1)/The-role-of-protein-interaction-domains.pdf Shady S. Ibrahim, Maha AR. Eldeeb, Mona AH. Rady, Karim M. Abdel Hady, Mohamed S. Lotfy, Noha S. Farag, Stephan Verleysdonk and Christoph P. Bagowski. Network Biology,2011,1(1):59-71 Protein-protein interaction networks provide a global picture of cellular function and biological processes. Proteins interact largely through specific domains which constitute the main building blocks of an interaction network. Perturbed or dysfunctional protein interactions are linked to many diseases, including cancer. In this study we describe the major pathways and connections within the human cancer network by a novel approach in which we overlay the human cancer network with all protein interaction domain (PID) superfamilies. Based on 38,777 experimentally derived interactions, we constructed a cancer network with 8 different levels and identified all major protein hubs within this cancer interactome. Only one percent of the cancer genes constitute over 50 percent of all interactions within the network. In addition, we mapped 56 PID superfamilies onto the cancer network, and discovered that over 10% of protein interaction domains are overrepresented within the cancer interactome when compared to the normal protein network. We present here a comprehensive list of all PIDs in the cancer network, identify the most important hubs within it and discover several individual genes which had previously not been linked to cancer. These proteins constitute excellent targets for the development of novel cancer therapeutics. Our results further hint to a partial molecular commonality between cancer and neurodegenerative diseases such as Alzheimer's and Huntington's. http://www.iaees.org/publications/journals/nb/articles/2011-1(1)/Some-steps-forward-in-semi-quantitative-networks-modelling.pdf Alessandro Ferrarini. Network Biology,2011,1(1): 72-78 System dynamics is an umbrella term for those approaches aiming to understand the behaviour of network-like systems over time. What makes system dynamics different from other methods about complex systems is the use of feedback loops, stocks and ?ows which allow to model how network-like systems can display strong nonlinear behaviours. Fuzzy Cognitive Maps (FCM) are semi-quantitative networks that can be regarded as a system dynamics method. Here I suggest 4 kinds of modifications to FCM: (1) if-then-else stop option; (2) piecewise option; (3) non-monotonicity option; (4) non-linearity option. These improvements to FCM might allow for fitter simulations of ecological and biological systems over time. I also present an applicative example to illustrate the proposed modifications. http://www.iaees.org/publications/journals/nb/articles/2011-1(1)/Network-Biology-an-exciting-frontier-science.pdf WenJun Zhang. Network Biology,2011,1(1): 79-80 Network biology is an exciting frontier science which uses network theory and methodology to address biological problems as regulatory networks, cancer, brain operation, food webs, ecosystems, etc. In this article the aims, scope, theoretical basis, and methodology of network biology were clearly described. http://www.iaees.org/publications/journals/nb/articles/2011-1(2)/Constructing-ecological-interaction-networks-by-correlation-analysis.pdf.pdf WenJun Zhang. Network Biology,2011,1(2): 81-98 A set of methodology for constructing ecological interaction networks by correlation analysis of community sampling data was presented in this study. Nearly 30 data sets at different levels of taxa for different sampling seasons and locations were used to construct networks and find network properties. I defined the network constructed by Pearson linear correlation is the linear network, and the network constructed by quasi-linear correlation measure (e.g., Spearman correlation) is the quasi-linear network. Two taxa with statistically significant linear or quasi-linear correlation are determined to interact. The quasi-linear network is more general than linear network. The results reveled that correlation distributions of Pearson linear correlation and partial linear correlation constructed networks are unimodal functions and most of them are short-head (mostly negative correlations) and long-tailed (mostly positive correlations). Spearman correlation distributions are either long-head and short-tailed unimodal functions or monotonically increasing functions. It was found that both mean partial linear correlation and mean Pearson linear correlation were approximately 0. The proportion of positive (partial) linear correlations declined significantly with the increase in taxa. The mean (partial) linear correlation declined significantly with the increase of taxa. More than 90% of network interactions are positive interactions. The average connectance was 9.8% (9.3%) for (partial) linear correlation constructed network. The parameter r in power low distribution (L(x)=x^(-r)) increased as the decline of taxon level (from functional group to species) for the partial linear correlation constructed network. r is in average 0.8 to 0.9. The number of (positive) interactions increased with the number of taxa for both linear and partial linear correlations constructed networks. The addition of a taxon would result in an increase of 0.4 (0.3) interactions (positive interactions) in the partial linear correlation constructed network. And the addition of a taxon would result in an increase of 3 interactions (positive interactions) in the linear correlation constructed network. For partial linear correlation constructed network, the network connectance decreased as the number of taxa. The constant connectance hypothesis did not hold for our networks. It was found that network structure changed with season and location. The same taxon in the network would connect to different taxa as the change of season and location. A higher level of species aggregation may used to find a more stable network structure. Positive interactions were considered to be caused mainly by mutualism, predation/parasitism, etc. the number and portion of positive interactions may be the most important indices for community stability and functionality. Mutualism is the most significant trophic relationship, seconded by predation/parasitism, and competition is the worst for community stability. http://www.iaees.org/publications/journals/nb/articles/2011-1(2)/Diffusion-limited-aggregation-fractal-evolution-gene-promoter.pdf Preston R. Aldrich. Network Biology,2011,1(2): 99-111 Gene promoter networks (GPNs) are systems-level representations of the base pair-sharing relationships (graph edges) among promoters (graph nodes). It has been shown in the bacterium E. coli that these networks can contain a fractal nucleus of strong associations suggesting a self-organizing complexity. Here I report results of twenty seven in silico simulations for a diffusion limited aggregation model which accounts for much of the fractal structure previously observed in GPNs. Parameters varied in the model included (a) the frequency of gene duplication events, and the extent of (b) attraction and (c) repulsion presented by the DNAprotein binding chemistry. Both duplication and attraction had significant effects on fractal topology of the GPN nucleus, whereas repulsion due to DNA-protein binding chemistry did not, at least for the levels explored in these simulations. Since repulsion is thought to be a key feature of fractal networks, it is likely that the repulsion in GPNs arises from the sparseness of the promoter space. The generation of a finite random set of promoters leads to sparse occupancy of promoter space which itself presents a considerable repulsion away from the consensus motif, working against the DNA-binding protein's efforts to organize the system of promoters over evolutionary time. This interplay between attractive and repulsive forces in a GPN is sufficient to generate a fractal topology. http://www.iaees.org/publications/journals/nb/articles/2011-1(2)/Immunoregulatory-network-and-cancer-associated-genes.pdf Robi Tacutu, Arie Budovsky, Hagai Yanai, Marina Wolfson, Vadim Fraifeld. Network Biology,2011,1(2): 112-120 Although different aspects of cancer immunity are a subject of intensive investigation, an integrative view on the possible molecular links between immunoregulators and cancer-associated genes has not yet been fully considered. In an attempt to get more insights on the problem, we analyzed these links from a network perspective. We showed that the immunoregulators could be organized into a miRNA-regulated PPI network-the immunoregulatory network. This network has numerous links with cancer, including (i) cancerassociated immunoregulators, (ii) direct and indirect protein-protein interactions (through the common protein partners), and (iii) common miRNAs. These links may largely determine the interactions between the host's immunity and cancer, supporting the possibility for co-expression and post-transcriptional co-regulation of immunoregulatory and cancer genes. In addition, the connection between immunoregulation and cancer may lie within the realm of cancer-predisposing conditions, such as chronic inflammation and fibroproliferative repair. A gradual, age-related deterioration of the integrity and functionality of the immunoregulaory network could contribute to impaired immunity and generation of cancer-predisposing conditions. http://www.iaees.org/publications/journals/nb/articles/2011-1(2)/Network-graphs-unveil-landscape-structure-and-changes.pdf Alessandro Ferrarini. Network Biology,2011,1(2): 121-126 Landscape (i.e., land cover, land use or vegetation maps) is a very complex mosaic of thousands of patches, and this makes its interpretation very challenging. Class areas and shared perimeters between classes are two pivotal properties of its structure. In addition, landscape structure changes over time as a consequence of many interacting processes. Hence, there's an urgent need for a synthetic and intuitive representation of its structural attributes. I advocate here network graphs as an aid to interpreting and checking temporal and spatial properties of landscapes. I also suggest several hints to fitter use network graphs in landscape representation. As a case study, I apply network graphs to the Ceno valley (Parma, Italy), but the proposed approach is suitable for any landscape maps. http://www.iaees.org/publications/journals/nb/articles/2011-1(2)/Java-program-to-test-homogeneity-of-samples.pdf WenJun Zhang. Network Biology,2011,1(2): 127-129 A Java program to test the homogeneity of samples and examine sampling completeness was presented in this study. The program was based on the model of Coleman et al. (1982) for random placement hypothesis and the algorithm of Zhang et al. (1999). The program was used to test samples' homogeneity and examine sampling completeness for four arthropod sampling data sets. http://www.iaees.org/publications/journals/nb/articles/2011-1(2)/Java-algorithm-non-parametric-statistic-comparison-network-structure.pdf WenJun Zhang. Network Biology,2011,1(2): 130-133 A Java algorithm to statistically compare between-network structure difference was developed. In this algorithm, Euclidean distance, Manhattan distance, Chebyshov distance, and Pearson correlation were available to measure between-network difference. The algorithm was tested and applied for its effectiveness with some arthropod and weed networks. http://www.iaees.org/publications/journals/nb/articles/2011-1(3-4)/topological-peculiarities-of-mammalian-networks.pdf Bjorn Goemann, Edgar Wingender, Anatolij P. Potapov. Network Biology, 2011, 1(3-4):134-148 We have comparatively investigated three different mammalian networks - on transcription, signal transduction and metabolic processes - with respect to their common and individual topological traits. The networks have been constructed based on genome- wide data collected from human, mouse and rat. None of these three networks exhibits a pure power-law degree distribution and, therefore, could be considered scalefree. Rather, the degree distributions of all three networks were best fitted by mixed models of a power law with an exponential tail. The networks differ from one another in the quantitative parameters of the models. Moreover, the transcription network can also be very well approximated by an exponential law. The connectivity within each network is rather robust, as is seen when removing individual nodes and computing the values of their pairwise disconnectivity index (PDI), which characterizes the topological significance of each node v by the number of direct or indirect connections in the network that critically depend on the presence of v. The results evidence that the networks are not centralized: none of nodes globally controls the integrity of each network. Just a few vertices appeared to strongly affect the coherence of the networks. These nodes are characterized by a broad range of degrees, thereby indicating that the degree alone is not the decisive criteria of a node's importance. The networks reveal distinct architectures: The transcriptional network exhibits a hierarchical modularity, whereas the signaling network is mainly comprised of semi-autonomous modules. The metabolic network seems to be made by a more complex mixture of substructures. Thus, despite being encoded by the same genomes, the networks significantly differ from one another in their general architectural design. Altogether, our results indicate that the subsets of genes and relationships that constitute these networks have co-evolved very differently and through multiple mechanisms. http://www.iaees.org/publications/journals/nb/articles/2011-1(3-4)/the-polarity-sub-network-in-the-yeast-network.pdf Luca Paris, Gianfranco Bazzoni. Network Biology, 2011, 1(3-4):149-158 Rare, but highly connected, hub proteins subdivide hierarchically global networks of interacting proteins into modular clusters. Most biological research, however, focuses on functionally defined sub-networks. Thus, it is important to know whether the sub-networks retain the same topology of the global networks, from which they derive. To address this issue, we have analyzed the protein-protein interaction sub-network that participates in the polarized growth of the budding yeast Saccharomyces cerevisiae and that is derived from the global network of this model organism. We have observed that, in contrast to global networks, the distribution of connectivity k (i.e., the number of interactions per protein) does not follow a power law, but decays exponentially, which reflects the local absence of hub proteins. Nonetheless, far from being randomly organized, the polarity sub-network can be subdivided into functional modules. In addition, most non-hub connector proteins, besides ensuring communications among modules, are linked mutually and contribute to the formation of the polarisome, a structure that coordinates actin assembly with polarized growth. These findings imply that identifying critical proteins within sub-networks (e.g., for the aim of targeted therapy) requires searching not only for hubs but also for key non-hub connectors, which might remain otherwise unnoticed due to their relatively low connectivity. http://www.iaees.org/publications/journals/nb/articles/2011-1(3-4)/an-algorithm-for-calculation-of-degree-distribution.pdf WenJun Zhang, ChenYuan Zhan. Network Biology, 2011, 1(3-4):159-170 In present study a Java algorithm to calculate degree distribution and detect network type was presented. Some indices, e.g., aggregation index, coefficient of variation, skewness, etc., were first suggested for detecting network type. Network types of some food webs reported in Interaction Web Database were determined using the algorithm. The results showed that the degree of most food webs was power law or exponentially distributed and they were complex networks. Different from classical distribution patterns (bionomial distribution, Poisson distribution, and power law distribution, etc.), both network type and network complexity can be calculated and compared using the indices above. We suggest that they should be used in the network analysis. In addition, we defined E, E=s2-u, where u and s2 is mean and variance of degree respectively, as the entropy of network. A more complex network has the larger entropy. If E is not greater than 0, the network is a random network and, it is a complex network if E is greater than 0. http://www.iaees.org/publications/journals/nb/articles/2011-1(3-4)/some-effects-of-parasitism-on-food-web-structure.pdf WeiPeng Kuang, WenJun Zhang. Network Biology, 2011, 1(3-4):171-185 So far most of the food webs lack parasitism. It has been found that parasites can profoundly affect food web properties. In this study we tried to consider parasitism in the food web analysis in order to provide a basis for further and more complete theory development. The data for topological analysis of food webs was from the food web studies of Lafferty et al. Pajek software was used to conduct topological analysis on food webs. The results revealed that in the food web the number of base species kept to be constant but the number of top species declined remarkably and the number of intermediate species increases sharply when parasitism was considered. Parasitism increased the food chain cycles. There were 508 cycles in the parasite-parasite sub-web but not any cycle was found in the predator-prey sub-web. The connectance and link density increased after parasitism was added. The links between predators and parasites were greater than the links between predators and preys. The connectance of predator-prey sub-web, predator-parasite sub-web, parasite-host sub-web, and parasite-parasite sub-web is 0.29, 0.16, 0.24, and 0.34, respectively. The link density of predator-prey sub-web, predator-parasite sub-web, parasite-host sub-web, and parasite-parasite sub-web is 11.95, 9.84, 15.5, and 7.64, respectively. Chain length increased slightly and omnivorous species and omnivory increased also. The present study revealed that parasitism would yield substantial effects on food web structure. http://www.iaees.org/publications/journals/nb/articles/2011-1(3-4)/some-thoughts-on-the-control-of-network-systems.pdf Alessandro Ferrarini. Network Biology, 2011, 1(3-4):186-188 The controllability of network-like systems is becoming a trendy key-issue in many disciplines, including ecology and biology. To control a biological, ecological or economic system is to make it behave according to our wishes, at the least possible cost. In this paper, I propose some ideas on networks control that do not precisely follow recent papers on the argument. By the way, since this scientific topic is still in open evolution, discordant thoughts might be helpful to the debate. http://www.iaees.org/publications/journals/nb/articles/2012-2(1)/small-genetic-world-of-Seriatopora-hystrix.pdf Stuart Kininmonth, Madeleine van Oppen, Sarah Castine, Lesa Peplow, Adrian Lutz.Network Biology,2012,2(1):1-15 The exchange of genetic information among coral reefs, through the transport of larvae, is critical to the function of Australia's Great Barrier Reef because it influences recruitment rates and resilience to disturbance. For many species the genetic composition is not homogeneous and is determined, in part, by the character of the complex dispersal pathways that connect the populations situated on each coral reef. One method of measuring these genetic connections is to examine the microsatellite composition of individual corals and then statistically compare populations across the region. We use these connection strengths, derived from a population similarity measure, to create complex networks to describe and analyse the genetic exchange of the brooding coral, Seriatopora hystrix. The network, based on determining the putative parental origin of individual coral colonies, involved sampling 2163 colonies from 47 collection sites and examining 10 microsatellites. A dispersal network was created from the genetic distance DLR values that measure the genetic similarity of each population (defined by the local sampling effort) to every other sampled population based on the microsatellite composition. Graph theory methods show that this network exhibited infrequent long distance links and population clustering which is commonly referred to as small world topology. Comparison with a hydrodynamic based network indicates that the genetic population network topology is similar. This approach shows the genetic structure of the S. hystrix coral follows a small world pattern which supports the results derived from previous hydrodynamic modelling. http://www.iaees.org/publications/journals/nb/articles/2012-2(1)/network-view-on-Schizophrenia-related-genes.pdf Sreedevi Chandrasekaran, Danail G. Bonchev.Network Biology,2012,2(1):16-25 This study is a part of a project investigating the molecular determinants of neurological diseases. To account for the systemic nature of these diseases we proceeded from a well established list of 38 schizophrenia-related genes (Allen et al., 2008; Ross et al., 2006) and investigated their closest network environment. The created networks were compared to recently proposed list of 173 schizophrenia related genes (Sun et al., 2009). 115 genes were predicted as potentially related to schizophrenia and subjected to GSEA. The enriched groups of proteins included neuromodulators, neurotransmitters and lipid transport. Over 100 signaling pathways were found significantly involved, signal transduction emerging as the most highly significant biological process. Next, we analyzed two microarray expression datasets derived from olfactory mucosa biopsies of schizophrenic patients and postmortem brain tissue samples from SMRIDB. The systems biology analysis resulted in a number of other genes predicted to be potentially related to schizophrenia, as well as in additional information of interest for elucidating molecular mechanisms of schizophrenia. http://www.iaees.org/publications/journals/nb/articles/2012-2(1)/GKIN-tool-for-drawing-genetic-networks.pdf Jonathan Arnold, Thiab R. Taha, Leonidas Deligiannidis.Network Biology,2012,2(1):26-37 We present GKIN, a simulator and a comprehensive graphical interface where one can draw the model specification of reactions between hypothesized molecular participants in a gene regulatory and biochemical reaction network (or genetic network for short). The solver is written in C++ in a nearly platform independent manner to simulate large ensembles of models, which can run on PCs, Macintoshes, and UNIX machines, and its graphical user interface is written in Java which can run as a standalone or WebStart application. The drawing capability for rendering a network significantly enhances the ease of use of other reaction network simulators, such as KINSOLVER (Aleman-Meza et al., 2009) and enforces a correct semantic specification of the network. In a usability study with novice users, drawing the network with GKIN was preferred and faster in comparison with entry with a dialog-box guided interface in COPASI (Hoops, et al., 2006) with no difference in error rates between GKIN and COPASI in specifying the network. GKIN is freely available at http://faculty.cs.wit.edu/~ldeligia/PROJECTS/GKIN/. http://www.iaees.org/publications/journals/nb/articles/2012-2(1)/a-Java-software-for-drawing-graphs.pdf WenJun Zhang.Network Biology,2012,2(1):38-44 In this study the software for drawing graphs, which is run as a Java application, was described. It can be freely downloaded and run on Windows platforms. The software can be used to draw directed, undirected, cyclic and acyclic graphs. http://www.iaees.org/publications/journals/nb/articles/2012-2(2)/identifying-the-common-interaction-networks.pdf Ahmed H. Zeitoun, Shady S. Ibrahim, Christoph P. Bagowski.Network Biology,2012,2(2):45-56 The recently analyzed genome of Naegleria gruberi, a free-living amoeboflagellate of the Heterolobosea clade, revealed a remarkably complex ancestral eukaryote with a rich repertoire of cytoskeletal-, motility- and signaling-genes. This protist, which diverged from other eukaryotic lineages over a billion years ago, possesses the ability for both amoeboid and flagellar motility. In a phylogenomic comparison of two free living eukaryotes with large proteomic datasets of three metastatic tumour entities (malignant melanoma, breast- and prostate-carcinoma), we find common proteins with potential importance for cell motility and cancer cell metastasis. To identify the underlying signaling modules, we constructed for each tumour type a protein-protein interaction network including these common proteins. The connectivity within this interactome revealed specific interactions and pathways which constitute prospective points of intervention for novel anti-metastatic tumour therapies. http://www.iaees.org/publications/journals/nb/articles/2012-2(2)/how-to-construct-the-statistic-network.pdf WenJun Zhang.Network Biology,2012,2(2):57-68 In present study I defined a new type of network, the statistic network. The statistic network is a weighted and non-deterministic network. In the statistic network, a connection value, i.e., connection weight, represents connection strength and connection likelihood between two nodes and its absolute value falls in the interval (0, 1]. The connection value is expressed as a statistical measure such as correlation coefficient, association coefficient, or Jaccard coefficient, etc. In addition, all connections of the statistic network can be statistically tested for their validity. A connection is true if the connection value is statistically significant. If all connection values of a node are not statistically significant, it is an isolated node. An isolated node has not any connection to other nodes in the statistic network. Positive and negative connection values denote distinct connection types (positive or negative association or interaction). In the statistic network, two nodes with the greater connection value will show more similar trend in the change of their states. At any time we can obtain a sample network of the statistic network. A sample network is a non-weighted and deterministic network. The statistic network, in particular the plant association network that constructed from field sampling, is mostly an information network. Most of the interspecific relationships in plant community are competition and cooperation. Therefore in comparison to animal networks, the methodology of statistic network is more suitable to construct plant association networks. Some conclusions were drawn from this study: (1) in the plant association network, most connections are weak and positive interactions. The association network constructed from Spearman rank correlation has most connections and isolated taxa are fewer. From net linear correlation, linear correlation, to Spearman rank correlation, the practical number of connections and connectance in the constructed network increases. Network compactness also follows the trend. In addition, as the increase of network compactness and connectance, the portion and number of negative association declines dramatically. (2) In an association (interaction) network, only a few connections follow the linear relationship. Most connections follow the quasi-linear or non-linear relationships. (3) The association networks constructed from partial linear correlation and linear correlation measures are generally scale-free complex networks. The degree of these networks is power low distributed. (4) Isolated species (families, etc.) are likely important in the statistic network. They are the sink species for shaping new network after a community is seriously disturbed. (5) Beween-taxa connections at higher taxonomic level are generally weaker than that at lower taxonomic level. http://www.iaees.org/publications/journals/nb/articles/2012-2(2)/modeling-community-succession-and-assembly.pdf WenJun Zhang.Network Biology,2012,2(2):69-78 The process of modeling community succession and assembly is in some sense a method for network evolution, as done by Barabasi and Albert (1999). It is also one of the methods to create a sample network from the statistic network I proposed earlier. I think that the mechanism of network evolution supposed by Barabasi and Albert is most likely applicable to the natural phenomena with emergency property. For natural phenomena without emergency property, the present study indicated that a scale-free network may be produced through a new mechanism, i.e., whether the connection of a taxon x occurs, dependent on the type and property of taxon y (in particular, the degree of its direct correlation with x) to be connected but not necessarily the existing number of connections of taxon y, as proposed in present study. http://www.iaees.org/publications/journals/nb/articles/2012-2(2)/why-excluding-H2O-from-metabolic-networks.pdf DeWu Ding.Network Biology,2012,2(2):79-81 This study provides a novel perspective on the reason for excluding H2O from metabolic networks when complex network theory is used. http://www.iaees.org/publications/journals/nb/articles/2012-2(2)/book-review.pdf Na Li.Network Biology,2012,2(2):82-83 A book, Computational Ecology: Graphs, Networks and Agent-based Modeling, published in 2012, was introduced and reviewed. http://www.iaees.org/publications/journals/nb/articles/2012-2(3)/medicinal-plants-growing-in-the-Judea-region.pdf Arie Budovsky, Vadim E. Fraifeld.Network Biology,2012,2(3):84-94 Plants growing in the Judea region are widely used in traditional medicine of the Levant region. Nevertheless, they have not so far been sufficiently analyzed and their medicinal potential has not been evaluated. This study is the first attempt to fill the gap in the knowledge of the plants growing in the region. Comprehensive data mining of online botanical databases and peer-reviewed scientific literature including ethno-pharmacological surveys from the Levant region was applied to compile a full list of plants growing in the Judea region, with the focus on their medicinal applications. Around 1300 plants growing in the Judea region were identified. Of them, 25% have medicinal applications which were analyzed in this study. Screening for chemical-protein interactions, together with the network-based analysis of potential targets, will facilitate discovery and therapeutic applications of the Judea region plants. Such an approach could also be applied as an integrative platform for further searching the potential therapeutic targets of plants growing in other regions of the world. http://www.iaees.org/publications/journals/nb/articles/2012-2(3)/analysis-on-degree-distribution-of-tumor-signaling-networks.pdf JingQiu Huang, WenJun Zhang.Network Biology,2012,2(3):95-109 Tumorigenesis is a multi-factorial and multi-step process, among which the changes in cell signaling pathways play a key role. Up till now there are fewer studies on network structure of tumor signaling pathways. In present study the degree distribution was analyzed based on thirty kinds of tumor signaling networks, including VEGF-pathway, JNK-pathway, p53-signaling, etc. The results showed that almost all of them were scale-free complex networks. Key metabolites in some tumor networks were also described. http://www.iaees.org/publications/journals/nb/articles/2012-2(3)/creating-real-network-with-expected-degree-distribution.pdf WenJun Zhang, GuangHua Liu.Network Biology,2012,2(3):110-117 The degree distribution of known networks is one of the focuses in network analysis. However, its inverse problem, i.e., to create network from known degree distribution has not yet been reported. In present study, a statistical simulation algorithm was developed to create real network with expected degree distribution. It is an iteration procedure in which a real network, with the least deviation of actual degree distribution to expected degree distribution, was created. Random assignment was used in the creation of connections. The Java program was designed. It may produce adjacency matrix, connection details, and actual degree distribution of the network created. http://www.iaees.org/publications/journals/nb/articles/2012-2(3)/identification-of-crucial-nodes-in-biological-networks.pdf DeWu Ding.Network Biology,2012,2(3):118-120 This study showed that the crucial nodes in biological networks could be identified with network communities. http://www.iaees.org/publications/journals/nb/articles/2012-2(4)/mathematical-methods-for-identifying-crucial-nodes-in-networks.pdf WenJun Zhang.Network Biology,2012,2(4):121-126 Crucial nodes in a network refer to those nodes that their existence is so important in preserving topological structure of the network and they independently determine the network structure. In this study I introduced and proposed several mathematical methods for identifying crucial nodes in networks. They fall into three categories, node perturbation, network analysis, and network dynamics. Node perturbation methods include adjacency matrix index, degree or flow change index, node perturbation index, etc. Network dynamics methods include network evolution modeling, etc. Network analysis methods include node degree, criticality index, branch flourishing index, node importance index, etc. Advantages and advantages of these methods were discussed. Finally, I suggested that some of these methods may also be used to identify crucial links (connections) in networks. In this case, the change of a link refers to presence/absence of a link, or change of flow in the link, etc. http://www.iaees.org/publications/journals/nb/articles/2012-2(4)/tolerances-of-symbiotic-and-nonsymbiotic-ant-plant-networks-to-species-extinctions.pdf Wesley Dattilo.Network Biology,2012,2(4):127-138 The knowledge of the mechanisms that shape biodiversity-stability relationships is essential to understand ecological and evolutionary dynamics of interacting species. However, most studies focus only on species loss and ignore the loss of interactions. In this study, I evaluated the topological structure of two different ant-plant networks: symbiotic (ants and myrmecophytes) and nonsymbiotic (ants and plants with extrafloral nectaries). Moreover, I also evaluated in both networks the tolerance to plant and ant species extinction using a new approach. For this, I used models based on simulations of cumulative removals of species from the network at random. Both networks were fundamentally different in the interaction and extinction patterns. The symbiotic network was more specialized and less robust to species extinction. On the other hand, the nonsymbiotic network tends to be functionally redundant and more robust to species extinction. The difference for food resource utilization and ant nesting in both ant-plant interactions can explain the observed pattern. In short, I contributed in this manner to our understanding of the biodiversity maintenance and coevolutionary processes in facultative and obligate mutualisms. http://www.iaees.org/publications/journals/nb/articles/2012-2(4)/continuous-discrete-model-of-population-dynamics-with-time-lag.pdf L.V. Nedorezov.Network Biology,2012,2(4):139-147 Continuous-discrete model of population dynamics is considered in current publication. It is assumed that death process of individuals has a continuous nature, and appearences of individuals of new generations are observed at fixed time moments. It is also assumed that population has non-overlapping generations, and for every generation self-regulative mechanisms have distributed time lag in reaction on population size changing. For particular case when death rate of individuals between fixed time moments corresponds to Verhulst's law, it was obtained that various cyclic regimes can be observed in phase space. For various values of model parameters the structure of domain in space of parameters, where chaotic dynamic regimes can be realized, is described. http://www.iaees.org/publications/journals/nb/articles/2012-2(4)/application-of-hierarchical-local-modularity-maximum-method-to-biological-networks.pdf DeWu Ding.Network Biology,2012,2(4):148-150 This study showed that the network communities with biological significance could be identified by using hierarchical local modularity maximum method. http://www.iaees.org/publications/journals/nb/articles/2013-3(1)/selforganizology-a-science-that-deals-with-self-organization.pdf WenJun Zhang.Network Biology,2013,3(1):1-14 Self-organization is a universe mechanism in nature. In a self-organizing system, the system evolves spontaneously to form an order structure based on some compatible rules. Without external instructions and forces, the self-organizing system arises only from the interactions between the basic components of the system. Although numerous theories and methods were established to describe self-organization, there are still many problems in this area. We still lack of unified theories and thoughts on self-organization. Also, we lack of universal basis of methodology in the modeling and simulation of self-organization. Self-organization is classified into a research area in complexity science. So far it is not an independent science. For this reason, a fundamental science, selforganizology, is proposed for finding and creating theories and methods from self-organization phenomena in nature, simulating and reconstructing self-organization phenomena, exploring mechanisms behind numerous self-organization phenomena, and promoting the applications of self-organization theories methods in science and industry. Existing theories and methods of self-organization are overviewed. Methodological basis of selforganizology is shortly discussed. http://www.iaees.org/publications/journals/nb/articles/2013-3(1)/changes-in-protein-interaction-networks.pdf K. M. Taufiqur Rahman, Md. Fahmid Islam, Rajat Suvra Banik, Ummay Honi, Farhana Sharmin Diba, Sharmin Sultana Sumi, Shah Md. Tamim Kabir, Md. Shamim Akhter.Network Biology,2013,3(1):15-28 New insights to understand the dynamics of enormous modifications during cancer in comparison to healthy condition have made the ground for the emergence of sophisticated systemic approaches like Network Systems Biology in the twenty first century which is potentially effective to model different biological phenomena such as regulation of gene-expression and protein-protein interaction. In the current study, the construction and computational analysis of protein interaction networks (PINs) based on expression data of proteins involved in 10 major cancer signal transduction pathways were done in case of five different tissues e.g. bone, breast, colon, kidney and liver for both normal and cancer conditions. Differential expression database GeneHubs-Gepis, and protein-protein interaction prediction tools PIPs and STRING were applied for primary data retrieval. Upregulation and downregulation of proteins in various cancers were analyzed to identify patterns in PINs during cancer signaling. Different network parameters were evaluated and comparisons were made among normal and cancer networks for each tissue and for different cancer based on Cytoscape software package. The networks for cancer show notable differences and fluctuations from normal ones for various network parameters. A cluster of 34 upregulated proteins with 76 relevant interactions was also found to be conserved in all five cancerous tissues. http://www.iaees.org/publications/journals/nb/articles/2013-3(1)/predators-alternative-food-sources-do-not-support-ecoepidemics.pdf Elisa Elena, Maria Grammauro, Ezio Venturino.Network Biology,2013,3(1):29-44 An ecoepidemic model is presented, in which two diseases affect the prey. Predators are allowed to have other food sources. Equilibria are analyzed for feasibility and stability. The most striking result is that in these conditions the two strains cannot both survive in the system, contrary to what is possible to obtain, under suitable assumptions, in standard epidemic models. http://www.iaees.org/publications/journals/nb/articles/2013-3(1)/organizational-theory-with-its-applications.pdf Yue Zhao, WenJun Zhang.Network Biology,2013,3(1):45-53 Organizations are goal-directed entities which have been designed as deliberately structured and coordinated dynamic systems that connect with the external environment. Organizational theory is the study of structure, function and design of organization. It aims to solve practical problems, maximize production efficiency and make organization better function and develop. Organizational theory contains various aspects. The history, development, and thoughts of organizational theory and its applications in biology and ecology were described in present paper. We held that more studies should be conducted to apply organizational theory in natural sciences as biology and ecology. http://www.iaees.org/publications/journals/nb/articles/2013-3(2)/cryptic-successors-unrevealed-even-by-network-analysis.pdf Anindita Bhadra, Ferenc Jordan.Network Biology,2013,3(2):54-66 Understanding queen succession could be a key contribution to the better understanding of the origins and evolution of eusociality. In order to investigate the nature of organizational changes during queen succession, we analyzed two closely related paper wasp species (Ropalidia cyathiformis and Ropalidia marginata). We compared the effects of in vivo and in silico queen removal on the structure of their interaction networks (the former resulting in queenless colonies with potential queens). We studied several structural measures. There is no major structural difference between full (queenright) and in silico queen-removed colonies but there are major differences between queenless and in silico queen-removed ones. This suggests that queen succession is accompanied by a major reorganization of the society, in Rm but not so much in Rc. We also analysed the centrality ranks of potential queens and found that their positional importance changes a lot during queen succession in R. marginata, as they are processed in the colony. In the queenright colonies of R. marginata, the direction of links is a better predictor of the identity of the potential queen than the strength of links. http://www.iaees.org/publications/journals/nb/articles/2013-3(2)/network-modelling-for-predicting-climate-change-and-biodiversity.pdf Alessandro Ferrarini.Network Biology,2013,3(2):67-73 Robust models are pivotal to the prediction of future climate change impacts on biodiversity. A move must be made away from individualistic models of single species toward the implication of synergistically interacting species. The focus should be on the indirect effects due to biotic interactions. Thanks to these kinds of models, counterintuitive results for species could be achieved, emerging from complex biotic feedbacks involving that species-specific expectations are not of necessity consistent with those of their community. In this paper, the proposed approaches can tackle some important limitations of commonly-used individualistic models, as they can: a) deal with an optionally large number of species, b) take into account biotic interactions, c) forecast indirect effects caused by climate change. http://www.iaees.org/publications/journals/nb/articles/2013-3(2)/3D-structure-prediction-of-replication-factor-C-subunits.pdf Mohamed Ragab Abdel Gawwad, Jasmin Sutkovic, Emina Zahirovic, Faruk Berat Akcesme, Betul Akcesme, Lizhi Zhang.Network Biology,2013,3(2):74-86 DNA stress can causes potentially spontaneous genome damage during DNA replication process. Proteins involved in this process are DNA-dependent ATPases, required for replication and repair. In this study the 3-D structure of RFC protein subunits in Arabidopsis thaliana: RFC1, RFC2, RFC3, RFC4 and RFC5 are predicted and confirmed by Ramachadran plot. The amino acid sequences are highly similar to the sequences of the homologous human RFC 140-, 37-, 36-, 40-, and 38 kDa subunits, respectively, and also show amino acid sequence similarity to functionally homologous proteins from E. coli. All five subunits show conserved regions characteristic of ATP/GTP-binding proteins and have significant degree of similarity among each other. The segments of conserved amino acid sequences that define a family of related proteins have been identified. RFC1 is identical to CDC44, a gene identified as a cell division cycle gene encoding a protein involved in DNA metabolism. Subcellular localization and interactions of each protein RFC protein subunit is determined. It subsequently became clear that RFC proteins and their interactome have functions in cell cycle regulation and/or DNA replication and repair processes. In addition, AtRFC subunits are controlling the biosynthesis of salicylic and salicylic acid-mediated defense responses in Arabidopsis. http://www.iaees.org/publications/journals/nb/articles/2013-3(3)/functional-interactome-of-aquaporin-1-sub-family.pdf Mohamed Ragab Abdel Gawwad, Jasmin Sutkovic, Lavinija Matakovic, Mohamed Musrati, Lizhi Zhang.Network Biology,2013,3(3):87-96 Aquaporins are channel proteins found in plasma membranes and intercellular membranes of different cellular compartments, facilitate the water flux, solutes and gases across the cellular plasma membranes. The present study highlights the sub-family plasma membrane intrinsic protein (PIP) predicting the 3-D structure and analyzing the functional interactome of it homologs. PIP1 homologs integrate with many proteins with different plant physiological roles in Arabidopsis thaliana including; PIP1A and PIP1B: facilitate the transport of water, diffusion of amino acids and/or peptides from the vacuolar compartment to the cytoplasm, play a role in the control of cell turgor and cell expansion and involved in root water uptake respectively. In addition we found that PIP1B plays a defensive role against Pseudomonas syringae infection through the interaction with the plasma membrane Rps2 protein. Another substantial function of PIP1C via the interaction with PIP2E is the response to nematode infection. Generally, PIP1 sub-family interactome controlling many physiological processes in plant cell like; osmoregulation in plants under high osmotic stress such as under a high salt, response to nematode, facilitate the transport of water across cell membrane and regulation of floral initiation in Arabidopsis thaliana. http://www.iaees.org/publications/journals/nb/articles/2013-3(3)/controlling-ecological-and-biological-networks-via-evolutionary-modelling.pdf Alessandro Ferrarini.Network Biology,2013,3(3):97-105 The controllability of network-like systems is a topical key-issue in many disciplines, including ecology and biology. It relies on the ability to guide a system's behaviour towards the desired state through the appropriate handling of a few input variables. To date, controllability of networks is based on the identification of the set of driver nodes that can guide the system's dynamics. I introduce here a new framework for the controllability of both network nodes and edges based on the use of evolutionary modelling, and provide an exemplification of its application. http://www.iaees.org/publications/journals/nb/articles/2013-3(3)/hierarchicalization-of-chaotic-food-webs-using-ISM.pdf Ping Liang, WenJun Zhang.Network Biology,2013,3(3):106-114 Ecologists always meet complex food webs without clear hierarchical structure. At certain degree it will retard further analysis of food webs. In present study we transferred chaotic food webs into hierarchicalized food webs using Interpretive Structural Modeling (ISM). As an example, the hierarchical structure of seven food webs was clearly identified and defined using ISM. ISM was thus proven to be effective. http://www.iaees.org/publications/journals/nb/articles/2013-3(4)/networks-control-introducing-degree-of-success-and-feasibility.pdf Alessandro Ferrarini.Network Biology,2013,3(4):115-120 Taming ecological and biological networks is a key-issue. It could be used to: a) neutralize damages to ecological and biological networks, b) safeguard rare and endangered species, c) manage ecological systems at the least possible cost, and d) counteract the impacts of climate change. While I recently showed that ecological and biological networks can be efficaciously controlled both from inside (inside-control model) and outside (outside-control model), here I propose a solution to the choice of the most feasible solution to network control. To do this, I introduce the concepts of control success and feasibility. http://www.iaees.org/publications/journals/nb/articles/2013-3(4)/crucial-metabolites-reactions-in-tumor-signaling.pdf JingRon Li, WenJun Zhang.Network Biology,2013,3(4):121-132 Changes in metabolites/reactions of cell signaling pathways play a key role in tumorigenesis. In present study, betweenness centrality, degree and k-core value of every metabolite/reaction in tumor signaling pathways p53, AKT, Ras, JAK-STAT, TNF, and VEGF were calculated. Crucial metabolites/reactions in these tumor signaling networks were identified using betweenness centrality. The p53-P-P was identified as the most important metabolite/reaction in p53 signaling pathway, followed by (Ac-p53-P)2 and DNA damage; Akt is the most important metabolite/reaction in AKT signaling pathway, followed by PI3K and PIP3; Ras-GTP is the most important metabolite/reaction in TNF signaling pathway, followed by MEKK1, JNKK and Ras-GDP. The k-core analysis showed that VEGF signaling pathway is the most compact network among these signaling pathways. http://www.iaees.org/publications/journals/nb/articles/2014-4(1)/meta-analysis-of-cancer-transcriptomes.pdf Sundus Iqbal, Hira Ejaz, Muhammad Sulaman Nawaz, Juan J Loor, Aisha Naeem.Network Biology,2014,4(1):1-20 To explore secrets of metastatic cancers, individual expression of true sets of respective genes must spread across the tissue. In this study, meta-analysis for transcriptional profiles of oncogenes was carried out to hunt critical genes or networks helping in metastasizing cancers. For this, transcriptomic analysis of different cancerous tissues causing leukemia, lung, liver, spleen, colorectal, colon, breast, bladder, and kidney cancers was performed by extracting microarray expression data from online resource; Gene Expression Omnibus. A newly developed bioinformatics technique; Dynamic Impact Approach (DIA) was applied for enrichment analysis of transcriptional profiles using Database for Annotation Visualization and Integrated Discovery (DAVID). Furthermore, oPOSSUM (v. 2.0) and Cytoscape (v. 2.8.2) were used for in-depth analysis of transcription factors and regulatory gene networks respectively. DAVID analysis uncovered the most significantly enriched pathways in molecular functions that were 'Ubiquitin thiolesterase activity' up regulated in blood, breast, bladder, colorectal, lung, spleen, prostrate cancer. 'Transforming growth factor beta receptor activity' was inhibited in all cancers except leukemia, colon and liver cancer. oPOSSUM further revealed highly over-represented Transcription Factors (TFs); Broad-complex_3, Broad-complex_4, and Foxd3 except for leukemia and bladder cancer. From these findings, it is possible to target genes and networks, play a crucial role in the development of cancer. In the future, these transcription factors can serve as potential candidates for the therapeutic drug targets which can impede the deadly spread. http://www.iaees.org/publications/journals/nb/articles/2014-4(1)/local-and-global-control-of-ecological-and-biological-networks.pdf Alessandro Ferrarini.Network Biology,2014,4(1):21-30 Recently, I introduced a methodological framework so that ecological and biological networks can be controlled both from inside and outside by coupling network dynamics and evolutionary modelling. The endogenous control requires the network to be optimized at the beginning of its dynamics (by acting upon nodes, edges or both) so that it will then go inertially to the desired state. Instead, the exogenous control requires that exogenous controllers act upon the network at each time step. By the way, all my previous works dealt with the goal of the global optimization of ecological and biological networks, i.e. how to drive them to a desired final state. Here I face another pivotal question: how can we locally (step-by-step) drive ecological and biological networks, so that also intermediate steps (not only the final state) are under our strict control? The ratio behind this question is that intermediate dynamics could potentially go below or above critical ecological-biological thresholds, hence invalidating the final global control. To this purpose, I introduce here a modelling solution to the complete control of ecological and biological networks that couples local and global control. http://www.iaees.org/publications/journals/nb/articles/2014-4(2)/fibrillar-organization-in-tendons.pdf Daniel Andres Dos Santos, Maria Laura Ponssa, Maria Jose Tulli, Virginia Abdala.Network Biology,2014,4(2):31-46 Since the tendon is composed by collagen fibrils of various sizes connected between them through molecular cross-links, it sounds logical to model it via a heterogeneous network of fibrils. Using cross sectional images, that network is operatively inferred from the respective Gabriel graph of the fibril mass centers. We focus on network percolation characteristics under an ordered activation of fibrils (progressive recruitment going from the smallest to the largest fibril). Analyses of percolation were carried out on a repository of images of digital flexor tendons obtained from samples of lizards and frogs. Observed percolation thresholds were compared against values derived from hypothetical scenarios of random activation of nodes. Strikingly, we found a significant delay for the occurrence of percolation in actual data. We interpret this finding as the consequence of some non-random packing of fibrillar units into a size-constrained geometric pattern. We erect an ideal geometric model of balanced interspersion of polymorphic units that accounts for the delayed percolating instance. We also address the circumstance of being percolation curves mirrored by the empirical curves of stress-strain obtained from the same studied tendons. By virtue of this isomorphism, we hypothesize that the inflection points of both curves are different quantitative manifestations of a common transitional process during mechanical load transference. http://www.iaees.org/publications/journals/nb/articles/2014-4(2)/prediction-of-three-dimensional-structure-and-interactome-analysis.pdf Jasmin Sutkovic, Mohamed Ragab Abdel Gawwad.Network Biology,2014,4(2):47-57 Microtubules are essential components of cytoskeleton, rigid hollow rods approximately 25 nm in diameter. Microtubules are dynamic structures being continuously assembled and disassembled within the cell. The basic building blocks of microtubules are heterodimers of globular alpha- and beta-tubulin subunits In Arabidopsis thaliana tubulin subunits are encoded by small gene families, six for alpha-tubulin and nine for beta-tubulin.Both alpha- and beta-tubulin bind GTP, which functions analogously to the ATP bound to actin to regulate polymerization. It is shown that tubulin alpha forms hydrogen bonds with the GTPase domain of b-tubulin. Multiple sequence alignment revealed high similarity between the family subunits. Due to the missing of three dimensional structuresin A. thaliana, structural models were predicted and validated. Additionally, protein domains search revealed that all tubulin alpha family subunits contain GTPase domain as the tubulin C terminal domain, confirming previous research. Finally the interactome analysis revealed several interactomes. AtTUA6 shows strong interaction with embryosac development arrest 10 protein (EDA10), involved in stimulating the exchange of guanyl nucleotides, enabling the replacement of GDP by GTP in association with a GTPases. http://www.iaees.org/publications/journals/nb/articles/2014-4(2)/comparative-structural-analysis-of-HAC1-in-Arabidopsis-thaliana.pdf Amar Cemanovic, Jasmin Sutkovic, Mohamed Ragab Abdel Gawwad.Network Biology,2014,4(2):67-73 Histone acetylation is an important posttranslational modification correlated with gene activation. In Arabidopsis thaliana, the histone acetyltransferase 1 (AtHAC1) is homologous to animal p300/CREB (cAMPresponsive element-binding protein)-binding proteins, which are the main histone acetyltransferases participating in many physiological processes, including proliferation, differentiation, and apoptosis. In this study the 3-D structure of the HAC1 protein in Arabidopsis thaliana was predicted using 4 homology-based prediction servers: ESyPred3D, 3D-JIGSAW, SWISS-MODEL and PHYRE2. The homology modeled structures were evaluated and stereochemical analysis done by Ramachadran plot analysis. The amino acid sequences of Arabidopsis thaliana HAC1 protein are highly similar to the sequence of the homologous human p300/CREB. SWISS MODEL and Phyre2 servers computed the identical 3D structures. Validation and verification methods, using Z-score and 3D-1D score, showed that these 3D models are of good and acceptable quality. http://www.iaees.org/publications/journals/nb/articles/2014-4(3)/network-properties-to-evaluate-targeted-immunization-algorithms.pdf Bita Shams, Mohammad Khansari.Network Biology,2014,4(3):74-94 Immunization of complex network with minimal or limited budget is a challenging issue for research community. In spite of much literature in network immunization, no comprehensive research has been conducted for evaluation and comparison of immunization algorithms. In this paper, we propose an evaluation framework for immunization algorithms regarding available amount of vaccination resources, goal of immunization program, and time complexity. The evaluation framework is designed based on network topological metrics which is extensible to all epidemic spreading model. Exploiting evaluation framework on well-known targeted immunization algorithms shows that in general, immunization based on PageRank centrality outperforms other targeting strategies in various types of networks, whereas, closeness and eigenvector centrality exhibit the worst case performance. http://www.iaees.org/publications/journals/nb/articles/2014-4(3)/regulatory-switches-for-hierarchical-use-of-carbon-sources.pdf Ruth S. Perez-Alfaro, Moises Santillan, Edgardo Galan-Vasquez, Agustino Martinez-Antonio.Network Biology,2014,4(3):95-108 In this work we study the preferential use of carbon sources in the bacterium Escherichia coli. To that end we engineered transcriptional fusions of the reporter gene gfpmut2, downstream of transcription-factor promoters, and analyzed their activity under several conditions. The chosen transcription factors are known to regulate catabolic operons associated to the consumption of alternative sugars. The obtained results indicate the following hierarchical order of sugar preference in this bacterium: glucose > arabinose > sorbitol > galactose. Further dynamical results allowed us to conjecture that this hierarchical behavior might be operated by at least the following three regulatory strategies: 1) the coordinated activation of the corresponding operons by the global regulator catabolic repressor protein (CRP), 2) their asymmetrical responses to specific and unspecific sugars and, 3) the architecture of the associated gene regulatory networks. http://www.iaees.org/publications/journals/nb/articles/2014-4(3)/3D-structure-prediction-of-histone-acetyltransferase.pdf Amar Cemanovic, Jasmin Sutkovic, Rabab Elamawi, Waleed Elkhoby, Mohamed Ragab Abdel Gawwad.Network Biology,2014,4(3):109-122 Histone acetylation is an important posttranslational modification correlated with gene activation. In Arabidopsis thaliana the histone acetyltransferase (HAC) proteins of the CBP family are homologous to animal p300/CREB (cAMP-responsive element-binding proteins, which are important histone acetyltransferases participating in many physiological processes, including proliferation, differentiation, and apoptosis. In this study the 3-D structure of all HAC protein subunits in Arabidopsis thaliana: HAC1, HAC2, HAC4, HAC5 and HAC12 is predicted by homology modeling and confirmed by Ramachandran plot analysis. The amino acid sequences HAC family members are highly similar to the sequences of the homologous human p300/CREB protein. Conservation of p300/CBP domains among the HAC proteins was examined further by sequence alignment and pattern search. The domains of p300/CBP required for the HAC function, such as PHD, TAZ and ZZ domains, are conserved in all HAC proteins. Interactome analysis revealed that HAC1, HAC5 and HAC12 proteins interact with S-adenosylmethionine-dependent methyltransferase domaincontaining protein that shows methyltransferase activity, suggesting an additional function of the HAC proteins. Additionally, HAC5 has a strong interaction value for the putative c-myb-like transcription factor MYB3R-4, which suggests that it also may have a function in regulation of DNA replication. http://www.iaees.org/publications/journals/nb/articles/2014-4(3)/stability-analysis-of-a-biological-network.pdf Q. Din.Network Biology,2014,4(3):123-129 In this paper, we study qualitative behavior of a network of two genes repressing each other. More precisely, we investigate the boundedness character and persistence, existence and uniqueness of positive steady-state, local asymptotic stability and global behavior of unique positive equilibrium point of this model. http://www.iaees.org/publications/journals/nb/articles/2014-4(3)/quasi-chemical-approach-for-modeling-predator-prey-interactions.pdf Muhammad Shakil, H. A. Wahab, Muhammad Naeem, Saira Bhatti.Network Biology,2014,4(3):130-150 We aim to develop the reaction diffusion equation for different types of mechanism of the predator-prey interactions with quasi chemical approach. The chemical reactions representing the interactions obey the mass action law. Since the cell-jump models may be considered as the proper diffusion models by themselves, the territorial animal like fox is given a simple cell as its territory. Under the proper relations between coefficients, like complex balance or detailed balance, this system demonstrated globally stable dynamics. http://www.iaees.org/publications/journals/nb/articles/2014-4(3)/book-review.pdf GuangHua Liu.Network Biology,2014,4(3):151-154 The book, Network Biology: Theories, Methods and Applications, edited by WenJun Zhang and published by Nova Science Publishers, USA, was briefly reviewed in present report. http://www.iaees.org/publications/journals/nb/articles/2014-4(4)/network-motif-identification-and-structure-detection.pdf Munni Begum, Jay Bagga, Ann Blakey, Sudipta Saha.Network Biology,2014,4(4):155-169 Local regulatory motifs are identified in the transcription regulatory network of the most studied model organism Escherichia coli (E. coli) through graphical models. Network motifs are small structures in a network that appear more frequently than expected by chance alone. We apply social network methodologies such as p-star models, also known as Exponential Random Graph Models (ERGMs), to identify statistically significant network motifs. In particular, we generate directed graphical models that can be applied to study interaction networks in a broad range of databases. The Markov Chain Monte Carlo (MCMC) computational algorithms are implemented to obtain the estimates of model parameters to the corresponding network statistics. A variety of ERGMs are fitted to identify statistically significant network motifs in transcription regulatory networks of E. coli. A total of nine ERGMs are fitted to study the transcription factor - transcription factor interactions and eleven ERGMs are fitted for the transcription factor-operon interactions. For both of these interaction networks, arc (a directed edge in a directed network) and k-istar (or incoming star structures), for values of k between 2 and 10, are found to be statistically significant local structures or network motifs. The goodness of fit statistics are provided to determine the quality of these models. http://www.iaees.org/publications/journals/nb/articles/2014-4(4)/effect-of-parasitism-on-food-webs.pdf WenJun Zhang, LiQin Jiang, WenJin Chen.Network Biology,2014,4(4):170-178 In this study, we used Pajek to analyze the effect of parasitism on food webs collected from Carpinteria Salt Marsh (CSM). Results showed that median and mean of generality and vulnerability for predator-prey and parasite-host sub-webs were greater than the reported previously. Inclusion of parasites significantly increased the mean generality and vulnerability of the full food web. Effectiveness of cascade model was tested using CSM and arthropod food webs. The results demonstrated that fitting goodness on the predator-prey sub-web without parasites was lower than that on the full CSM food web. Also, cascade model performed worse in fitting arthropod food webs. http://www.iaees.org/publications/journals/nb/articles/2014-4(4)/test-case-prioritization-using-Cuscuta-search.pdf Mukesh Mann, Om Prakash Sangwan.Network Biology,2014,4(4):179-192 Most companies are under heavy time and resource constraints when it comes to testing a software system. Test prioritization technique(s) allows the most useful tests to be executed first, exposing faults earlier in the testing process. Thus makes software testing more efficient and cost effective by covering maximum faults in minimum time. But test case prioritization is not an easy and straightforward process and it requires huge efforts and time. Number of approaches is available with their proclaimed advantages and limitations, but accessibility of any one of them is a subject dependent. In this paper, artificial Cuscuta search algorithm (CSA) inspired by real Cuscuta parasitism is used to solve time constraint prioritization problem. We have applied CSA for prioritizing test cases in an order of maximum fault coverage with minimum test suite execution and compare its effectiveness with different prioritization ordering. Taking into account the experimental results, we conclude that (i) The average percentage of faults detection (APFD) is 82.5 percent using our proposed CSA ordering which is equal to the APFD of optimal and ant colony based ordering whereas No ordering, Random ordering and Reverse ordering has 76.25 percent, 75 percent, 68.75 percent of APFD respectively. http://www.iaees.org/publications/journals/nb/articles/2015-5(1)/computational-methods-for-fitting-an-ERGM-to-biological-network-data.pdf Sudipta Saha, Munni Begum.Network Biology,2015,5(1):1-12 Exponential random graph models (ERGM) based on graph theory are useful in studying global biological network structure using its local properties. However, computational methods for fitting such models are sensitive to the type, structure and the number of the local features of a network under study. In this paper, we compared computational methods for fitting an ERGM with local features of different types and structures. Two commonly used methods, such as the Markov Chain Monte Carlo Maximum Likelihood Estimation and the Maximum Pseudo Likelihood Estimation are considered for estimating the coefficients of network attributes. We compared the estimates of observed network to our random simulated network using both methods under ERGM. The motivation was to ascertain the extent to which an observed network would deviate from a randomly simulated network if the physical numbers of attributes were approximately same. Cut-off points of some common attributes of interest for different order of nodes were determined through simulations. We implemented our method to a known regulatory network database of Escherichia coli (E. coli). http://www.iaees.org/publications/journals/nb/articles/2015-5(1)/determination-of-keystone-species-in-CSM-food-web.pdf LiQin Jiang, WenJun Zhang.Network Biology,2015,5(1):13-33 The importance of a species is correlated with its topological properties in a food web. Studies of keystone species provide the valuable theory and evidence for conservation ecology, biodiversity, habitat management, as well as the dynamics and stability of the ecosystem. Comparing with biological experiments, network methods based on topological structure possess particular advantage in the identification of keystone species. In present study, we quantified the relative importance of species in Carpinteria Salt Marsh food web by analyzing five centrality indices. The results showed that there were large differences in rankings species in terms of different centrality indices. Moreover, the correlation analysis of those centralities was studied in order to enhance the identifying ability of keystone species. The results showed that the combination of degree centrality and closeness centrality could better identify keystone species, and the keystone species in the CSM food web were identified as, Stictodora hancocki, small cyathocotylid, Pygidiopsoides spindalis, Phocitremoides ovale and Parorchis acanthus. http://www.iaees.org/publications/journals/nb/articles/2015-5(1)/evolutionary-network-control-holds-for-nonlinear-networks.pdf Alessandro Ferrarini.Network Biology,2015,5(1):34-42 The proof of our understanding of ecological and biological systems is measured by our skill to rule them, i.e. to channelize them towards a desired state. Control is a cardinal issue in most complex systems, but because a general theory to apply it in a quantitative manner has been absent so far, little was known about how we can rule weighted, directed networks that represent the most common configuration of real systems. To this purpose, Evolutionary Network Control (ENC) has been developed as a theoretical and methodological framework aimed to the control of ecological and biological networks by coupling network dynamics and evolutionary modelling. ENC is a tools to address controllability for arbitrary network topologies and sizes. ENC has proven to cover several topics of network control, e.g. a) the global control from inside and b) from outside, c) the local (step-by-step) control, and the computation of: d) control success, e) feasibility, and f) degree of uncertainty. Taken together, these results indicate that many aspects of controllability can be explored exactly and analytically for arbitrary networks, opening new avenues to deepening our understanding of complex systems. As yet, I have applied ENC only to linear ecological and biological networks. In this work, I show that ENC also holds for any kind of nonlinear networks, and provide an applicative example based on the nonlinear, widely-used, Lotka-Volterra model. http://www.iaees.org/publications/journals/nb/articles/2015-5(2)/network-theory-to-mark-recapture-data.pdf Luciana L.F. de Lima, Gilberto Corso, Marcio Z. Cardoso.Network Biology,2015,5(2):43-54 By noting the spatial location of captured individuals mark-recapture studies create a collection of discrete events spread in space and time. This setup is appropriate for network modeling where the vertices (or nodes) are the points of capture and links are established whenever a recapture occurs. Applying network analytical tools, it is possible to ascertain aspects of spatial structure and generate predictions regarding the likely causes of structure in the network. We studied the spatial network of two tropical butterfly species, Heliconius erato and H. melpomene, using a mark-recapture database from a 2-year survey in an Atlantic Forest remnant in Brazil. The overall network structure of both species was similar in number of vertices and average connectivity. Heliconius erato had a smaller, more disconnected network structure, suggesting shorter traveling paths. The distribution of connectivity of both species was better adjusted by a power-law distribution. We found hubs in both species; hubs are points of high capture and their location is correlated with the location of flowering plants visited by adults. In complex systems, hub elimination can have a notable collapsing effect in network structure. Because resource hubs are important for butterfly network organization we suggest management as well as experimental tests with regards to the role of resource hotspots for population structure. http://www.iaees.org/publications/journals/nb/articles/2015-5(2)/WRKY-transcription-factors-network-in-Arabidopsis-Thaliana.pdf Mouna Choura, Ahmed Rebai, Khaled Masmoudi.Network Biology,2015,5(2):55-61 The WRKY transcription factors superfamily are involved in diverse biological processes in plants including response to biotic and abiotic stresses and plant immunity. Protein-protein interaction network is a useful approach for understanding these complex processes. The availability of Arabidopsis Thaliana interactome offers a good opportunity to do get a global view of protein network. In this work, we have constructed the WRKY transcription factor network by combining different sources of evidence and we characterized its topological features using computational tools. We found that WRKY network is a hub-based network involving multifunctional proteins denoted as hubs such as WRKY 70, WRKY40, WRKY 53, WRKY 60, WRKY 33 and WRKY 51. Functional annotation showed seven functional modules particularly involved in biotic stress and defense responses. Furthermore, the gene ontology and pathway enrichment analysis revealed that WRKY proteins are mainly involved in plant-pathogen interaction pathways and their functions are directly related to the stress response and immune system process. http://www.iaees.org/publications/journals/nb/articles/2015-5(2)/JNK-pathway-leading-to-cell-survival-death.pdf Shruti Jain, D.S. Chauhan.Network Biology,2015,5(2):62-70 In this paper a well defined method for the design of JNK pathway for epidermal growth factor/ insulin using fuzzy system using operational transconductance amplifier was discussed. Fuzzy system includes fuzzification of the input variables, application of the fuzzy operator (AND or OR) in the antecedent, implication from the antecedent to the consequent, aggregation of the consequents across the rules, and defuzzfication. Fuzzy system with various electrical parameters for different voltages of OTA with different membership function was found. Results with 3V were the best. http://www.iaees.org/publications/journals/nb/articles/2015-5(2)/modeling-predator-prey-interactions.pdf Muhammad Shakil, H. A. Wahab, Muhammad Naeem, Saira Bhatti, Muhammad Shahzad.Network Biology,2015,5(2):71-81 In this paper, we aim to study the interactions between the territorial animals like foxes and the rabbits. The territories for the foxes are considered to be the simple cells. The interactions between predator and its prey are represented by the chemical reactions which obey the mass action law. In this sense, we apply the mass action law for predator prey models and the quasi chemical approach is applied for the interactions between the predator and its prey to develop the modeled equations for different possible mechanisms of the predator prey interactions. http://www.iaees.org/publications/journals/nb/articles/2015-5(3)/a-genetic-regulatory-circuit-controls-growth-phases.pdf Agustino Martinez-Antonio.Network Biology,2015,5(3):82-94 Bacterial populations transition between growing and non-growing phases, based on nutrient availability and stresses conditions. The hallmark of a growing state is anabolism, including DNA replication and cell division. In contrast, bacteria in a growth-arrested state acquire a resistant physiology and diminished metabolism. However, there is little knowledge on how this transition occurs at the molecular level. Here, we provide new evidence that a multi-element genetic regulatory circuit might work to maintain genetic control among growth-phase transitions in Escherichia coli. This work contributes to the discovering of design principles behind the performance of biological functions, which could be of relevance on the new disciplines of biological engineering and synthetic biology. http://www.iaees.org/publications/journals/nb/articles/2015-5(3)/topological-properties-of-arthropod-food-webs-in-paddy-fields.pdf LiQin Jiang, WenJun Zhang, Xin Li.Network Biology,2015,5(3):95-112 To explore the topological properties of paddy arthropod food webs is of significance for understanding natural equilibrium of rice pests. In present study, we used Pajek software to analyze the topological properties of four full arthropod food webs in South China. The results showed that predators were significantly abundant than preys, and the proportion of predators to preys (3.07) was significantly higher than previously reported by Cohen in 1977 (1.33). In the food webs, the number of top species was the largest, accounted for about 50 percent of the total. The number of intermediate-intermediate links was far greater than the other three links. The average degree of paddy arthropod food webs is 6.0, 6.04, 5.74 and 7.75, respectively. Average degree and link density did not change significantly with the change of the number of species, but the connectance reduced significantly. In the paddy ecosystems, the increase of species diversity does not lead to an increase proportionally to the links among species. The link density and connectance of food webs of early season rice field were less than that from late season rice field. Cycles of all food webs cycles were 0. The maximum chain length of the basal species was 3, and the largest chain length of the top species was typically 2 or 3. Neutral insects were found to play a very important role in the paddy ecosystem. Nilaparvata lugens and Sogatella furcifera were found to be the dominant species of rice pests. Pardosa pseudoannulata, Tetragnatha maxillosa, Pirata subparaticus, Arctosa stigmosa and Clubiona corrugate were identified as the important predatory species that may effectively control the pest population. The keystone species calculated from keystone index and network analysis are analogous, indicating either keystone index or network analysis can be used in the analysis of keystone species. http://www.iaees.org/publications/journals/nb/articles/2015-5(3)/dynamics-of-fractional-order-modified-Morris-Lecar-neural-model.pdf Ranjit Kumar Upadhyay, Argha Mondal.Network Biology,2015,5(3):113-136 Most of the beautiful biological functions in neural systems are expected to happen considering the system with memory effect. Fractional differential equations are very useful to investigate long-range interacting systems or systems with memory effect. In this paper, a fractional order nonlinear three dimensional modified Morris-Lecar neural system (M-L system) has been studied. The fractional order M-L system is a generalization of the integer order M-L system. The paper presents an approximate analytical solution of the fractional order M-L system, using Homotopy Perturbation Method (HPM) and Variational Iteration Method (VIM). The fractional derivatives are described in the Caputo sense. We have used the above methods as they show very efficient result for very small time region. Solutions are obtained in the form of rapidly convergent infinite series and only a few iterations are needed to obtain the approximate solutions. Comparison of both HPM and VIM reveals that the two present methods of solution are elegant and powerful for solving the nonlinear fractional order biological as well as neural systems. http://www.iaees.org/publications/journals/nb/articles/2015-5(4)/a-hierarchical-method-for-finding-interactions.pdf WenJun Zhang.Network Biology,2015,5(4):137-145 In the earlier studies, I pointed out that a network changed in a local domain can be approximated as a linear network, i.e., all between-node (or -taxon, -component, etc) changes in the local domain are treated as linear ones and Pearson linear correlation measure can be used. For a little wider domain, the quasi-linear measure, Spearman rank correlation can be used also. In present study, I jointly use Pearson linear correlation measure and Spearman rank correlation measure and their partial correlations to find interactions. First, I define some hierarchical principles for finding interactions. Reliability levels are then defined using set operations. The full algorithm and Matlab codes for finding interactions are given. http://www.iaees.org/publications/journals/nb/articles/2015-5(4)/commonality-in-structure-among-food-web-networks.pdf Carrie J. Byron, Craig Tennenhouse.Network Biology,2015,5(4):146-162 A goal of this study was to determine similarities in structure among food webs that are otherwise disparate with regard to species, population, and size. Food webs were examined as directed, unweighted graphs in order to normalize food webs with regard to biomass and population/species distinctions. The graphs were further normalized with regard to topological size and existence of circuits through the reduction of each strongly connected component to a single node. This had the added benefit of resulting in networks with more clear delineation between trophic levels. Finally, common induced subgraphs were considered for their obvious value in characterizing network structure. Through this study we determined not only that there are pairs of systems that are highly similar in structure once appropriately normalized for size, makeup, and geographical location, but also that a majority of food webs have similar structural components when compared with random food webs. http://www.iaees.org/publications/journals/nb/articles/2015-5(4)/partial-correlation-analysis-in-finding-interactions.pdf WenJun Zhang.Network Biology,2015,5(4):163-168 Between-taxon interactions can be detected by calculating the sampling data of taxon sample type. In present study, Spearman rank correlation and proportion correlation are chosen as the general correlation measures, and their partial correlations are calculated and compared. The results show that for Spearman rank correlation measure, in all predicted candidate direct interactions by partial correlation, about 16.77% (x, 0-45.4%) of them are not successfully detected by Spearman rank correlation. In all predicted interactions by Spearman rank correlation, 47.56% (y, 0-100%) of them are undeterministic interactions, i.e., not successfully detected by partial correlation. In all predicted interactions by Spearman rank correlation, 53.45% (z, 0-100%) of them are candidate interactions, i.e., successfully detected by partial correlation. The regression relationship between Spearman rank correlation (r) and its partial correlation (pr) is pr=0.0102+0.1085r (R2=0.0181, p less than 0.00001, n=1004). For proportion correlation measure, in all predicted candidate interactions by partial correlation, about 6.82% (x, 0-28.64%) of them are not successfully detected by proportion correlation. In all predicted interactions by proportion correlation, 72.24% (y, 28.01-100%) of them are undeterministic interactions. In all predicted interactions by proportion correlation, 27.76% (z, 0-71.99%) of them are candidate interactions. The regression relationship between proportion correlation and its partial correlation is pr=0.07+0.0592r (R2=0.0213, p less than 0.00001, n=1447). The proportion of missed (x), mis-predicted (y) and precisely predicted candidate direct interactions (z) by general correlation analysis increases, increases, and decreases with the number of taxa respectively. Relationships between general correlation (r) and partial correlation (pr) mean that indirect interactions increase mean interaction strength of taxa. The precisely predicted (z) candidate direct interactions by Spearman rank correlation and proportion correlation analysis are not necessarily those with the highest Spearman rank correlations and proportion correlations. Jointly using correlation and partial correlation measures to analyze various interactions is the most reliable choice. Candidate direct interactions detected by both correlation and partial correlation measures should be the most focused interactions, seconded by those interactions detected by partial correlation only and by correlation only. http://www.iaees.org/publications/journals/nb/articles/2015-5(4)/predator-prey-models-for-mechanism-of-autocatalysis.pdf Muhammad Shakil, H. A. Wahab, Muhammad Naeem, Saira Bhatti, Muhammad Shahzad.Network Biology,2015,5(4):169-179 In this paper we aim to develop the modeled equations for different types of mechanism of the predator-prey interactions with the help of a quasi chemical approach while taking a special study case of foxes and rabbits, these mechanisms include autocatalysis mechanism, pair wise interactions and the mechanism of their movements to some free places. The chemical reactions representing the interactions obey the mass action law. The territorial animal like fox is assigned a simple cell as its territory. Under the proper relations between coefficients, this system may demonstrate globally stable dynamics. http://www.iaees.org/publications/journals/nb/articles/2015-5(4)/simple-and-easy-estimation-of-network-properties.pdf Yanhong Qi.Network Biology,2015,5(4):180-184 An ecological network can be constructed by calculating the sampling data of taxon by sample type. A statistically significant Pearson linear correlation means an indirect or direct linear interaction between two taxa, and a statistically significant partial (net, or pure) correlation based on Pearson linear correlation means a candidate direct linear interaction between two taxa. In many cases, statistically significant partial correlations are not available, or we only need to estimate some of network properties. Based on sampling data of arthropods in different countries and periods, in present study I proved that the number of candidate direct linear interactions (y) increases with the number of indirect + direct linear interactions (x) calculated by Pearson linear correlation (y=-0.2757+0.5343x, r2=0.859, p less than 0.00001), and the former is approximately half of the later. The proportion of candidate direct interactions in possible maximum interactions (y percent) is approximately two-thirds of mean Pearson linear correlation (x) (y=1.9060+64.6084x, r2=0.339, p=0.023). These conclusions are expected to provide simple and easy quantities to estimate some of network properties. http://www.iaees.org/publications/journals/nb/articles/2016-6(1)/perturbation-method-for-prediction-of-missing-links.pdf WenJun Zhang.Network Biology,2016,6(1):1-11 In present study, I proposed a node degree dependent random perturbation algorithm for prediction of missing links in the network. In the algorithm, I assume that a node with more existing links harbors more missing links. There are two rules. Rule 1 means that a randomly chosen node tends to connect to the node with greater degree. Rule 2 means that a link tends to be created between two nodes with greater degrees. Missing links of some tumor related networks (pathways) are predicted. The results prove that the prediction efficiency and percentage of correctly predicted links against predicted missing links with the algorithm increases as the increase of network complexity. The required number for finding true missing links in the predicted list reduces as the increase of network complexity. Prediction efficiency is complexity-depedent only. Matlab codes of the algorithm are given also. Finally, prospect of prediction for missing links is briefly reviewed. So far all prediction methods based on static topological structure only (represented by adjacency matrix) seems to be low efficient. Network evolution based, node similarity based, and sampling based (correlation based) methods are expected to be the most promising in the future. http://www.iaees.org/publications/journals/nb/articles/2016-6(1)/centrality-measures-for-immunization-of-weighted-networks.pdf Mohammad Khansari, Amin Kaveh, Zainabolhoda Heshmati, Maryam Ashkpoor Motlaq.Network Biology,2016,6(1):12-27 Effective immunization of individual communities with minimal cost in vaccination has made great discussion surrounding the realm of complex networks. Meanwhile, proper realization of relationship among people in society and applying it to social networks brings about substantial improvements in immunization. Accordingly, weighted graph in which link weights represent the intensity and intimacy of relationships is an acceptable approach. In this work we employ weighted graphs and a wide variety of weighted centrality measures to distinguish important individuals in contagion of diseases. Furthermore, we propose new centrality measures for weighted networks. Our experimental results show that Radiality-Degree centrality is satisfying for weighted BA networks. Additionally, PageRank-Degree and Radiality-Degree centralities showmoreacceptable performance in targeted immunization of weighted networks. http://www.iaees.org/publications/journals/nb/articles/2016-6(1)/common-disease-regulatory-network-for-metabolic-disorders.pdf Tasnuba Jesmin, Sajjad Waheed, Abdullah-Al-Emran.Network Biology,2016,6(1):28-36 Metabolic disorder causes the failure of metabolism process is growing concern worldwide. This research predicts a common metabolic pathway that is shared by Obesity, Type-2 Diabetes, Hypertension and Cardiovascular diseases due to metabolic disorder. A protein-protein interaction network is created to show the protein co-expression, co-regulations and interactions among gene and diseases. Genes whose are associated with metabolic diseases have been accumulated from different gene databases with verification and 'mined' them to establish gene interaction network models for expressing the molecular linkages among genes and diseases which affect disease progression. The number of associated genes identified for Type 2 Diabetes (T2D) is 250, Hypertension (HT) is 156, Obesity (OB) is 185 and cardiovascular disease (CVD) is 178. Among the sorted candidate gene 10 common genes are identified whose are directly or indirectly associated with four diseases by doing linkage filtering. By analysing the gene network model and PPI network a common metabolic pathway among metabolic diseases has been investigated. http://www.iaees.org/publications/journals/nb/articles/2016-6(1)/network-chemistry-network-informatics-network-behavioristics.pdf WenJun Zhang.Network Biology,2016,6(1):37-39 In present study, I proposed some new sciences: network chemistry, network toxicology, network informatics, and network behavioristics. The aims, scope and scientific foundation of these sciences are outlined. http://www.iaees.org/publications/journals/nb/articles/2016-6(2)/regression-analysis-on-different-mitogenic-pathways.pdf Shruti Jain.Network Biology,2016,6(2):40-46 In this paper different regression analysis methods were discussed on three different mitogenic pathways i.e. ERK, MK2 and JNK. Coefficient of determination, ANOVA, T-value, Durban-Watson statistics were calculated for the corresponding three proteins. The model was made using linear modeling using different regression analysis techniques in which different parameters like Mean sq error, Root mean sq error, Mean abs error, Relative sq error, Root relative sq error and Relative abs error were calculated using different analysis like PLS, linear, SVM, random forest etc were calculated. In all respect results with ERK are the best. http://www.iaees.org/publications/journals/nb/articles/2016-6(2)/bit-by-bit-control-of-nonlinear-biological-networks.pdf Alessandro Ferrarini.Network Biology,2016,6(2):47-54 Evolutionary Network Control (ENC) has been first introduced in 2013 to effectively subdue network-like systems. ENC opposes the idea, very common in the scientific literature, that controllability of networks should be based on the identification of the set of driver nodes that can guide the system's dynamics, in other words on the choice of a subset of nodes that should be selected to be permanently controlled. ENC has proven to be effective in the global control (i.e. the focus is on mastery of the final state of network dynamics) of linear and nonlinear networks, and in the local (i.e. the focus is on the step-by-step ascendancy of network dynamics) control of linear networks. In this work, ENC is applied to the local control of nonlinear networks. Using the Lotka-Volterra model as a case study, I show here that ENC is capable of locally driving nonlinear networks as well, so that also intermediate steps (not only the final state) are under our strict control. ENC can be readily applied to any kind of ecological, biological, economic and network-like system. http://www.iaees.org/publications/journals/nb/articles/2016-6(3)/algorithm-for-tree-generation-and-evolution.pdf WenJun Zhang.Network Biology,2016,6(3):55-64 In present study we proposed a node-similarity based algorithm for tree generation and evolution. In this algorithm, we assume that each isolated node is a node set at the beginning, two node sets with the greatest similarity tend to connect into a new node set firstly. Repeat this procedure, until all isolated nodes are combined into a tree. Pearson correlation measure, cosine measure, and (negative) Euclidean distance measure (the three measures are for interval attributes), contingency correlation measure (for nominal attributes), or Jaccard coefficient measure (for binary attributes) were used as the between-node similarity. In this way, all connections are sequentially generated and it thus forms the evolution process of a spanning tree of maximum likelihood. The similarity value of a connection can be considered as the weight of the connection. Matlab codes of the algorithm are provided. http://www.iaees.org/publications/journals/nb/articles/2016-6(3)/decentralized-control-of-ecological-and-biological-networks.pdf Alessandro Ferrarini.Network Biology,2016,6(3):65-74 Evolutionary Network Control (ENC) has been recently introduced to allow the control of any kind of ecological and biological networks, with an arbitrary number of nodes and links, acting from inside and/or outside. To date, ENC has been applied using a centralized approach where an arbitrary number of network nodes and links could be tamed. This approach has shown to be effective in the control of ecological and biological networks. However a decentralized control, where only one node and the correspondent input/output links are controlled, could be more economic from a computational viewpoint, in particular when the network is very large (i.e. big data). In this view, ENC is upgraded here to realize the decentralized control of ecological and biological nets. http://www.iaees.org/publications/journals/nb/articles/2016-6(4)/network-robustness-implication-formulization-and-exploitation.pdf WenJun Zhang.Network Biology,2016,6(4):75-85 Robustness refers to a system's capacity for maintaining some performance when the system's internal structure is perturbed. In previous study, network robustness, i.e., network structure robustness, includes both resistance capacity (connection robustness) of network structure to perturbation and restoration capacity (restoration robustness) of network structure if it is perturbed. Besides network structure robustness, in present paper I defined two more categories of robustness, network parameter robustness, and comprehensive robustness. Network parameter robustness refers to a network's capacity, without any structural changes, for maintaining between-node flows (fluxes) / link weights if it is perturbed. Comprehensive robustness refers to the network's capacity that not only the topological structure of the network, e.g., nodes and links, are not or less changed, but also between-node flows (fluxes), link weights, nodes' state values are maintained also if the network is perturbed. Comprehensive robustness considers both structure and parameter changes of a network. Furthermore, some new indices for network parameter robustness, and comprehensive robustness were proposed. In addition to specialized indices for network robustness, the inverse of various indices of global sensitivity analysis were suggested as indices for network robustness. Differences between robustness and stability were discussed. Misuse or inaccurate use of robustness / stability in ecology was clarified. In addition, I proposed methods to facilitate network robustness. Parameters / properties of some robust bio-networks were analyzed and summarized. http://www.iaees.org/publications/journals/nb/articles/2016-6(4)/application-of-R-to-investigate-common-gene-regulatory-network.pdf Nahida Habib, Kawsar Ahmed, Iffat Jabin, Mohammad Motiur Rahman.Network Biology,2016,6(4):86-100 Depression, Major Depression or mental disorder creates severe diseases. Mental illness such as Unipolar Major Depression, Bipolar Disorder, Dysthymia, Schizophrenia, Cardiovascular Diseases (Hypertension, Coronary Heart Disease, Stroke) etc., are known as Major Depression. Several studies have revealed the possibilities about the association among Bipolar Disorder, Schizophrenia, Coronary Heart Diseases and Stroke with each other. The current study aimed to investigate the relationships between genetic variants in the above four diseases and to create a common pathway or PPI network. The associated genes of each disease are collected from different gene database with verification using R. After performing some preprocessing, mining and operations using R on collected genes, seven (7) common associated genes are discovered on selected four diseases (SZ, BD, CHD and Stroke). In each of the iteration, the numbers of collected genes are reduced up to 51%, 36%, 10%, 2% and finally less than 1% respectively. Moreover, common pathway on selected diseases has been investigated in this research. http://www.iaees.org/publications/journals/nb/articles/2016-6(4)/compedium-model-using-frequency-cumulative-distribution-function.pdf Shruti Jain.Network Biology,2016,6(4):101-110 In this paper I used the frequency and cumulative distribution functions to make a best model of the receptors of the survival proteins i.e. Epidermal Growth Factor Receptor (EGFR) and Insulin Receptor (IRS) using ten concentrations combination of TNF, EGF and Insulin. It has been revealed that survival and apoptosis signals induced by the receptors of EGF and insulin are temporarily separated and this is reflected in my model by the differences between the values of the parameters used. I conducted the analysis using KS-d, KS, AD stat, AD p-value, chi square, chi square p-value and chi square df for different distribution functions for EGFR and IRS. The frequency and cumulative distribution curves for different distribution techniques like exponential, log-normal, normal, gamma, chi-square etc are plotted using chi- square tests. http://www.iaees.org/publications/journals/nb/articles/2017-7(1)/network-criminology.pdf WenJun Zhang.Network Biology,2017,7(1):1-9 In present study, I proposed the science discipline, network criminology. Network criminology roots in criminology and network science, which focuses on network analysis of criminal networks. It uses the theory and methodology of network science to analyze, predict and control criminal patterns and behavior. The criminal network refers to a criminal group, a terrorism network, etc. Network criminology aims to understand topological structure, organization, function, identification, and control, etc., of criminal networks. Meanwhile, I defined the aims, scope, theory and methodology of network criminology. http://www.iaees.org/publications/journals/nb/articles/2017-7(1)/effects-of-a-silenced-gene-in-Boolean-network-models.pdf Emir Haliki, Nadide Kazanci.Network Biology,2017,7(1):10-20 Gene regulation and their regulatory networks are one of the most challenging research problems of computational biology and complexity sciences. Gene regulation is formed by indirect interaction between DNA segments which are protein coding genes to configure the expression level of one another. Prevention of expression of any genes in gene regulation at the levels of transcription or translation indicates the gene silencing event. The present study examined what types of results in gene silencing would bring about in the dynamics of Boolean genetic regulatory mechanisms. The analytical study was performed in gene expression variations of Boolean dynamics first, then the related numerical analysis was simulated in real networks in the literature. http://www.iaees.org/publications/journals/nb/articles/2017-7(2)/exotic-species-and-structure-of-a-plant-galling-network.pdf Walter Santos de Araujo, Julio Miguel Grandez-Rios, Leonardo Lima Bergamini, Jan Kollar.Network Biology,2017,7(2):21-32 Gall-inducing insects are highly specialized herbivores and is expected that networks composed by gall-inducing insects and their host plants are also very specialized. However, presence of exotic species might reduce the interaction number for native species, which would lead to changes in the specialization of plant-galling networks. In this study, we use network metrics to describe, for the first time, the structure of a network of gall-inducing insects associated to ornamental host plants. We found that the plant-galling network has a low-connected structure and is more modular than expected by chance. Native insect herbivores were significantly more frequent on native host plant species, while exotic herbivores occurred mostly on exotic host plant species. On the other hand, the number of interactions between insect herbivores and native or exotic plant species did not vary. Our findings show that plant-galling networks are very specialized and structured independently of exotic species presence. http://www.iaees.org/publications/journals/nb/articles/2017-7(2)/measurement-and-identification-of-positive-plant-interactions.pdf WenJun Zhang.Network Biology,2017,7(2):33-40 Positive interactions play a key role in plant communities. The present study discusses measurement/identification methods of positive plant interactions. So far, some indices, e.g., RII, RCI, RNE and lnRR, and some models, e.g., site-based neighborhood models, individual-based models, etc., are usually used to measure and identify the type and strength of positive plant interactions. Most of these methods are based on interaction data of two species only. In a multi-species community or ecosystem, which occurs mostly in the nature, the interaction between two species is influenced by other species in the environment and may change as the time. Those indices and models may not exactly represent the true situations in the nature. Therefore, I argue that the inclusion of multi-species interactions in the network and utilization of theory and methods of network analysis and network evolution should be the focus in the future. The network evolution model, and correlation- and network-based methods in relation to species interactions were introduced and discussed. Finally, I think that network thinking and selforganizology are the basis for the future research of complex and dynamic species interactions. http://www.iaees.org/publications/journals/nb/articles/2017-7(2)/drug-design-and-analysis-for-bipolar-disorder-and-associated-diseases.pdf Nahida Habib, Kawsar Ahmed, Iffat Jabin, Mohammad Motiur Rahman.Network Biology,2017,7(2):41-56 Bioinformatics deals with biological data and analyzes or processes the data using computer science techniques. With the appearance of modern bioinformatics tools, it is now possible to design a drug using these high technologies and open a new area of drug design and development. This research predicts to design a common drug for four associated mental disorders that include bipolar disorder, schizophrenia, coronary heart diseases and stroke. The key to drug design is a biomolecule or protein. To show the protein interactions and evolutions, a protein-protein interaction network is created among the common genes of the four diseases. The genes corresponding to each disease are collected from NCBI gene database. These genes are preprocessed, mined and verified to find the common genes among the diseases. After getting common genes (7 genes), PPI network is created with them. Then a common drug is designed that will work on four investigated diseases. This structure based drug design research will open a new era to discover and develop new drug compounds using different bioinformatics tools. http://www.iaees.org/publications/journals/nb/articles/2017-7(3)/reconstruction-visualization-analysis-of-human-pluripotency-network.pdf Priyanka Narad, Kailash C.Upadhyaya, Anup Som.Network Biology,2017,7(3):57-75 Identification of genes/proteins involved in pluripotency and their inter-relationships is important for understanding the induction/loss and maintenance of pluripotency. With the availability of large volume of data on interaction/regulation of pluripotency scattered across a large number of biological databases and hundreds of scientific journals, it is required a systematic integration of data which will create a complete view of pluripotency network. Describing and interpreting such a network of interaction and regulation (i.e., stimulation and inhibition) links are essential tasks of computational biology, an important first step in systems-level understanding of the underlying mechanisms of pluripotency. To address this, we have assembled a network of 166 molecular interactions, stimulations and inhibitions, based on a collection of research data from 147 publications, involving 122 human genes/proteins, all in a standard electronic format, enabling analyses by readily available software such as Cytoscape and its Apps (formerly called "Plugins"). The network includes the core circuit of OCT4 (POU5F1), SOX2 and NANOG, its periphery (such as STAT3, KLF4, UTF1, ZIC3, and c-MYC), connections to upstream signaling pathways (such as ACTIVIN, WNT, FGF, and BMP), and epigenetic regulators (such as L1TD1, LSD1 and PRC2). We describe the general properties of the network and compare it with other literature-based networks. Gene Ontology (GO) analysis is being performed to find out the over-represented GO terms in the network. We use several expression datasets to condense the network to a set of network links that identify the key players (genes/proteins) and the pathways involved in transition from one state of pluripotency to other state (i.e., native to primed state, primed to non-pluripotent state and pluripotent to non-pluripotent state). http://www.iaees.org/publications/journals/nb/articles/2017-7(3)/correlations-between-eight-types-of-malignant-neoplasms.pdf WenJun Zhang.Network Biology,2017,7(3):76-79 In present study, the data of standardised death rates of malignant neoplasms per 100000 inhabitants in 31 European countries during 1994-2013 were used to analyze linear correlations between eight types of cancers in terms of induced death rates. The results showed that most pairs of cancers closely correlate to each other. The malignant neoplasm of cervix uteri (women) and the malignant neoplasm of trachea, bronchus and lung correlate most closely (r=0.5915), followed by the malignant neoplasms (r=0.4832) of colon, rectosigmoid junction, rectum, anus and anal canal and lymphatic/haematopoietic tissue, the malignant neoplasms (r=-0.483) of stomach, and trachea, bronchus and lung, the malignant neoplasms (r=0.4605) of skin and prostate (men), the malignant neoplasms (r=0.4344) of colon, rectosigmoid junction, rectum, anus and anal canal and trachea, bronchus and lung, etc. These correlations are likely caused by common or adverse environmental, social, medical or even genetic / molecular factors. http://www.iaees.org/publications/journals/nb/articles/2017-7(4)/regression-modeling-of-different-proteins.pdf Shruti Jain.Network Biology,2017,7(4):80-93 There are different types of regression analysis. Out of which simple regression and multiple regressions was considered in this paper. For calculation purpose we have used PLS analysis which calculates squared r values. This paper considers eleven different proteins and one output. We have validated our results by calculating adjusted regression coefficient, predicted regression coefficient regression coefficient cross validation, rm^2 and F-test values. Later multiple regressions were used as we have different independent variable (proteins). For that analysis we have calculated the coefficient, standard error, standard coefficient, tolerance, t value and p value, variation explanation of predictors and estimators which gives percentage and cumulative percentage. Correlation matrixes were also shown at the end for eleven proteins and one output. http://www.iaees.org/publications/journals/nb/articles/2017-7(4)/a-stage-structured-hybrid-model.pdf Soumya Banerjee.Network Biology,2017,7(4):94-97 Stochasticity and spatial distribution of the pathogen play a critical role in determining the outcome of an infection. 1 in a million immune system cells are specific to a particular pathogen. The serendipitous encounter of such a rare immune system cell with its fated antigen can determine the mortality of the infected animal. Moreover, pathogens may remain initially localized in a small volume of tissue. Hence stochastic and spatial aspects play an important role in pathogenesis, especially early on in the infection. Current efforts at investigating the effect of stochasticity and space in modeling of host immune response and pathogens use agent based models (ABMs). However these are computationally expensive. Population level approaches like ordinary differential equations (ODEs) are computationally tractable. However they make simplifying assumptions that are unlikely to be true early on in the infection. We proposed a stage-structured hybrid model that aims to strike a balance between the detail of representation of an ABM and the computational tractability of an ODE model. It uses a spatially explicit ABM in the initial stage of infection, and a coarse-grained but computationally tractable ODE model in the latter stages of infection. Such an approach might hold promise in: 1) modeling of other emerging pathogens where the initial stochasticity of the pathogen dictates the trajectory of pathogenesis, and 2) lead to insights into immune system inspired strategies and architectures for distributed systems of computers. http://www.iaees.org/publications/journals/nb/articles/2017-7(4)/equilibrium-of-biological-and-ecological-networks.pdf Alessandro Ferrarini.Network Biology,2017,7(4):98-104 The equilibrium of biological and ecological networks is often studied using eigenvector-eigenvalue analyses in order to reckon steady/unsteady properties and trajectories. Although at equilibrium inputs equal outputs for all the system variables, network flows continue to happen. Therefore, in this study I face three underestimated topics of network equilibrium: equilibrium flows, equilibrium sensitivity and equilibrium what-if properties. Using an applicative example, I show here that these three topics add important details to the knowledge of network behaviour at equilibrium. http://www.iaees.org/publications/journals/nb/articles/2018-8(1)/improved-methods-for-analyzing-MRI-brain-images.pdf Jyotsna Dogra, Navdeep Prashar, Shruti Jain, Meenakshi Sood.Network Biology,2018,8(1):1-11 Image segmentation is a part of image processing for region or object extraction from the background area. Owing to the complex background, contrast of the infected portion, low intensity difference values, intricate inner body parts etc.; the problem of region extraction in segmentation is very challenging. Among various image segmentation techniques, thresholding is one of the simplest techniques, in which the region of interest is extracted from the background by comparing the pixel values with the threshold value. The threshold value is obtained from histogram of the image. The technique presented in the paper involves graph cut method in which the initial centroids are automatically selected by exploiting the symmetrical nature of the MRI images. The results obtained by the thresholding technique in this research work shows that any abnormality can be localized easily in horizontal divided MRI brain image rather than in vertical divided MRI image. Graph cut results show better segmentation than thresholding technique which is justified by PSNR and SSIM values. http://www.iaees.org/publications/journals/nb/articles/2018-8(1)/removal-of-electromyography-noise-from-ECG.pdf Navdeep Prashar, Jyotsna Dogra, Meenakshi Sood, Shruti Jain.Network Biology,2018,8(1):12-24 This paper presents the review of the biomedical system which consists of an energy source, signal processing, signal conditioning and signal transmission. These blocks are designed by various optimization techniques to achieve high operating speed, compressed area and minimum energy consumption. These techniques are mainly divided in to four aspects: (a) increasing the longevity of device using energy harvesting approaches; (b) reducing the delay to enhance the operating frequency; (c) reducing the data storage using data compression; (d) increasing the data rate transmission with reduced power consumption. This review paper briefly summarizes the various techniques and device performance achieved by these techniques. To attain these high performance systems input played a vital role. This paper also presents the different low pass IIR filter approximation method techniques to remove Electromyography noise from ECG input signal. For this purpose, we have taken MIT-BIH Arrhythmia database. We have calculated signal to noise ratio and power spectral density. On comparing their performance parameters of different low pass IIR filters, Elliptic filter has found best suited to remove this type of noise. http://www.iaees.org/publications/journals/nb/articles/2018-8(1)/EEG-metric-based-mental-stress-detection.pdf Gaurav, R. S. Anand, Vinod Kumar.Network Biology,2018,8(1):25-34 Mental stress level is a vital parameter affecting physical well-being, cognition, emotions, and professional efficiency. With growing adversities in modern living standards, causing abnormal mental stress, it is necessary to measure to cure it. Regular personal stress profile generated can be used as neurofeedback for the clinical as well as personal assessment. This paper describes a method to detect mental stress level based on physiological parameters. In this method, an electroencephalogram (EEG)-metric parameters based binary and ternary stress classifier is developed. This is validated through probabilistic stress profiler of differential stress inventory (a questionnaire based evaluation). Nine channel EEG is used to extract physiological signal. EEG-metric based cognitive state and workload outputs are generated for 41 healthy volunteers (37 males and 4 females, age; 24+-5 years). All subjects were guided to perform three simple tasks of closed eye, focusing vision on a red dot on center of dark screen and focusing on a white screen. Central tendencies (mean, median and mode) and standard deviation were extracted of EEG-metric (sleep onset, distraction, low engagement, high engagement and cognitive states) as features. Either of the two or three classes of stress are evaluated from probabilistic stress profiler of differential stress inventory and used as training output classes. A supervisory training of multiple layer perceptron based binary support vector machine classifier was used to detect stress class one by one. 40 subject's samples were used for training and interchanging one-by one 41th subjects stress class is determined from the designed classifier. Out of 41 subjects, stress level of 30 subjects is correctly identified by binary classifier and stress level of 26 subjects is correctly identified by ternary classifier, using multi-layer perceptron kernel based SVM. http://www.iaees.org/publications/journals/nb/articles/2018-8(1)/image-coding-algorithm-for-capsule-endoscopy.pdf Nithin Varma Malathkar, Surender Kumar Soni.Network Biology,2018,8(1):35-43 A simple and efficient image compression algorithm for the capsule endoscopy is given in this paper. The algorithm consist of simplified RGB-YUV colour transform, corner clipping, differential pulse code modulation and Golomb-Rice code. Here, different sub-sampling schemes have been tested on the chroma components. The proposed algorithm do not required any extra memory and has a low computational complexity. The proposed work supports the image sensor which sends the data in zigzag order. The proposed algorithm provides a compression ratio of 83.7% at peak signal noise ratio 45.8. The proposed algorithm provide competition to other works with respect to compression ratio and with JPEG-LS give better performance in terms of compression ratio, memory usage and computational complexity. http://www.iaees.org/publications/journals/nb/articles/2018-8(1)/network-analysis-framework-to-find-therapeutics-for-autoimmunity.pdf Soumya Banerjee.Network Biology,2018,8(1):44-54 The immune system protects a host from foreign pathogens. In rare cases, the immune system can attack the cells of the host organism causing autoimmune diseases. We outline a computational framework that combines bioinformatics and network analysis with an emerging targets platform. The computational framework presented here can be used to find drug targets for autoimmune diseases. It can also be used to find existing drugs that can be repurposed to treat autoimmune diseases based on networks of interactions or similarities between different diseases. Information on which gene regions are associated with the disease (single nucleotide polymorphisms) can be used in gene therapy when that technique becomes viable. Our analysis also revealed immune cell subtypes that are implicated in these diseases. These immune cell subtypes can be selected for immunotherapy experiments. Finally, our analysis also reveals intra-cellular and protein-protein interaction networks and pathways that can be targeted with small molecule inhibitors. The downstream off-target effects of these inhibitors can also be determined from such a network analysis. In summary, our computational framework can be used to find novel therapeutics for autoimmune diseases and potentially even other dysfunctions. http://www.iaees.org/publications/journals/nb/articles/2018-8(2)/robustness-of-plant-plant-networks.pdf Walter Santos de Araujo.Network Biology,2018,8(2):55-64 Anthropogenic modification of natural environments is the main causes of species extinction in the globe, which directly leads to loss of interspecific links and modifies the structure of ecological networks. The objective of present study is to evaluate the effect of human-induced habitat modification on the connectivity and robustness of ecological interaction networks composed by plant-epiphyte and plant-parasite interactions. In total were analyzed eight distinct binary networks of plant-plant interactions in Brazil, being three epiphyte networks and five parasite networks occurring both in conserved and anthropized habitats. The results show that the human-induced habitat modification influences the connectance of plant-plant networks, since networks of anthropized habitats had greater connectance than the networks of conserved habitats. In addition, the results showed higher values of robustness in the plant-parasite networks when compared to plant-epiphyte networks, and these differences were mainly observed in anthropized habitats. This study presented a new approach for studies of plant-plant ecological interactions, because is the first to compare the effect of human-induced habitat modification on the plant-plant network robustness. http://www.iaees.org/publications/journals/nb/articles/2018-8(2)/LDA-based-on-PPI-network.pdf Nahida Habib, Kawsar Ahmed, Md. Binyamin, M. Mesbahuddin Sarker, K. M. Akkas Ali.Network Biology,2018,8(2):65-82 Modern Bioinformatics tools have a tremendous contribution in gene analysis, Protein-Protein Interaction (PPI) Network creation and Drug design. It's been a big challenge to pick out a small subset of informative data from a large microarray dataset and reach on an accurate classification. A successful and precise classification of any disease into its subtype is necessary for successful diagnosis and treatment of the disease. The NCBI Gene Expression Omnibus (GEO) is the extensive storage containing experimental microarray data. In this research, PPI networks and a common drug is designed for the unique DENGUE samples and Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) techniques are applied for the classification of Dengue fever genes into its unique samples. Comparing to PCA, in LDA, LD1 classifies 96.2% while PC1 Classifies 46%. Using LDA, also a prediction is made to predict samples from gene variance. Moreover, LDA predicts approximately 73.21% accurate results. All of the calculation, comparison and gene analysis is performed using R tool and UniHi tool is used for the creation of PPI network and Drug design. Here, a common drug is designed which can be used for all of the sample type of the Dengue fever but in different proportion. http://www.iaees.org/publications/journals/nb/articles/2018-8(2)/delayed-control-of-ecological-and-biological-networks.pdf Alessandro Ferrarini.Network Biology,2018,8(2):83-89 Evolutionary Network Control (ENC) was introduced in 2011 to permit the control of any kind of ecological and biological networks, with an arbitrary number of nodes and links. To date, ENC has been applied with the idea to control biological and ecological networks since the beginning of their system dynamics. This approach has shown to be effective in the control of both continuous-time and discrete-time networks. However a delayed control, where network dynamics are controlled only from a certain point on, could be more economic from a computational viewpoint, and also more feasible from an applicative perspective. For this reason, ENC is further upgraded here to realize the delayed control of ecological and biological nets. http://www.iaees.org/publications/journals/nb/articles/2018-8(2)/how-lymph-node-architecture-scales-with-host-body-size.pdf S Banerjee.Network Biology,2018,8(2):90-97 The immune system can detect and respond against pathogens in time that does not vary with the size of the host animal. We suggest that this is due to the architecture of lymph nodes. Lymph nodes are anatomical structures that facilitate the otherwise serendipitous encounter of immune system cells with pathogens. We develop two complementary mathematical approaches to derive the optimal distribution of lymph nodes that enable a rapid immune response. Our work gives insights into the optimal design and architecture of the immune system and provides valuable inspiration for designing efficient computing systems. http://www.iaees.org/publications/journals/nb/articles/2018-8(3)/network-model-and-hub-proteins-for-asthma-exacerbation.pdf Tahmina Hossain, Nazmir-Nur Showva, Saleh Ahmed, Md. Mudassir Billah, Armugan Ashraf, Md. Fahmid Islam, Md. Shaifur Rahman, KM Taufiqur Rahman.Network Biology,2018,8(3):98-112 Asthma is a long-term inflammatory disease known to affect the airways in the lungs with variable and recurring symptoms. A large number of genes, transcription factors and proteins are involved in this process, which makes it polygenic. We investigated the responsible proteins for asthma by conducting in-depth analysis in the database of asthma proteins and subsequently examining their differential role in disease progression following a computational biological approach. Firstly, we constructed a protein-protein interaction network among 1152 proteins, and identified top 20 high degree nodes (known as hubs); considering threshold score of not less than 100 by using Cytoscape 3.1.0 software package. Also we identified seven asthma signal transduction pathways from KEGG database and compared them with the pathways derived from NetWalker platform to determine the constituted proteins. Secondly, we conducted MCODE (molecular complex detection) analysis that divided the network into 27 clusters having threshold score of not less than 4.0. These individual clusters of constituted proteins were compared with the hubs and the results demonstrated their functional role in asthma. We also identified the proteins involved in the regulatory, reactome and metabolic reaction interaction for asthma exacerbation, classified different lung functional roles of these proteins, and found hyper-geometric pvalue of not greater than 0.05. Thus, our in-depth analysis suggests some important consequences for interpreting the resulting data significantly and gives more insight about asthma exacerbation. http://www.iaees.org/publications/journals/nb/articles/2018-8(3)/find-common-gene-and-design-PPI-network-for-vector-borne-diseases.pdf Tanjina Akter, Lubna Yasmin Pinky, Md. Mosaddik Hasan, Farzana Akter Chowdhury, Md. Imam Hossain.Network Biology,2018,8(3):113-125 Malaria, Dengue and Chikungunya are the most common mosquito-borne viral diseases transmitted to humans by day-biting Aedes aegypti and Aedes albopictus mosquitoes. Different types of gene are responsible for these viruses. The principal study of this research is to find the relationship between genetic variant for these three diseases and to create a common pathway regulatory or Protein-Protein Interaction (PPI) network. Our investigation goes through preprocessing, filtering, sorting and gene mining on the gathered gene (Malaria, Dengue and Chikungunya) using R to find the common associated genes by the process of reduction. The investigation shows that about 60% of the collected gene from different standard gene database is responsible for animal virus attack. After preprocessing, filtering and sorting using R toolkit, the number of collected gene for three diseases(A=malaria, B=dengue and C=chikunguniya) is reduced to 35%. Gene mining is done by intersection operation on (A, B), (B, C) and (C, A) that reduces the common associated gene from 35% to 5%. Finally, the reduction is done by intersecting AB, BC and CA that reduces the common gene from 5% to less than 1%. We have discovered five (5) common associated genes for these three virus diseases. However a common pathway with the five (5) common associated genes that has been designed for selective diseases. http://www.iaees.org/publications/journals/nb/articles/2018-8(3)/word-occurrence-frequency-and-word-association.pdf YanHong Qi, GuangHua Liu, WenJun Zhang.Network Biology,2018,8(3):126-136 In present study, I presented an algorithm for analysis of word occurrence frequency and word association in English text file. Various delimiters were used for splitting words. In addition, common used grammatical words are ignored in word occurrence and association analysis. All different words were listed according to word occurrence frequency from the greater to the smaller. Word association was detected by using one-dimensional ordered cluster analysis. The words fallen in the same class may likely have strong association. Theoretically, various classes at distinct clustering hierarchical level may represent different hierarchical topics. Java software of the algorithm was provided. http://www.iaees.org/publications/journals/nb/articles/2018-8(4)/disorder-and-interactions.pdf Mouna Choura, Faical Brini.Network Biology,2018,8(4):137-143 Dehydrins (DHNs) are intrinsically disordered proteins that are expressed under conditions of water-related stress. They play a fundamental role in plant response and adaptation to abiotic stresses. The protein architecture of dehydrins can be described by the presence of three types of conserved sequence motifs that have been named the Y-, S-and K- segments. Although, dehydrins are extensively studied, their molecular interactions remain elusive. By combining network analysis with prior knowledge, we provide further insights into the role of some dehydrin disorder in cereals notably in stress tolerance. This work includes a comparative analysis with dehydrins of Arabidopsis thaliana to highlight the disorder conservation of dehydrins across evolution. http://www.iaees.org/publications/journals/nb/articles/2018-8(4)/network-matrix-based-methods-for-network-comparis.pdf WenJun Zhang.Network Biology,2018,8(4):144-152 In present article, I introduced some network matrix based methods for comparing and testing between-network difference/similarity, including the methods for interval weights based network matrix, including between-network similarity, randomization test of between-network difference, and statistic test of between-network difference, and the method for Boolean weights based network matrix. In addition, degree change index, weight change index, and eigenvector matrix change index were presented also. Matlab codes of the methods were provided. http://www.iaees.org/publications/journals/nb/articles/2019-9(1)/behavioural-networks-a-new-methodology.pdf Alessandro Ferrarini, Giuseppe Giglio, Stefania C. Pellegrino, Annagrazia Frassanito, Marco Gustin.Network Biology,2019,9(1):1-9 We introduce here a new methodology, named Behavioural Networks (BeNe), to thoroughly analyze birds' habits in space and time. Behavioural Networks are based on GIS technologies, association rules and network capabilities, all applied to GPS data. They return an information-rich and easily-interpretable synthesis of the activities taken by birds during a user-defined time interval. As a case study, we applied BeNe to the Lesser Kestrel Falco naumanni of the Santeramo in Colle colony (Apulia, Italy). Our methodology has been able to extract the main rules of the bird's behaviour during the most critical part of the chick-rearing period. BeNe can be applied to any bird species, to any time interval and to both local and migratory GPS data. http://www.iaees.org/publications/journals/nb/articles/2019-9(1)/Analytic-Hierarchy-Process-Matlab-computation-software.pdf WenJun Zhang.Network Biology,2019,9(1):10-17 How to determine relative importance (e.g., weight) of nodes or links in a network is one of the focus in network biology. Analytic Hierarchy Process (AHP) can be used to determine weights of edges (i.e., links) and vertices (i.e., nodes) of a network, which is suitable for the hierarchical structure problems hard to be quantified. In present article, the Matlab computation software of AHP was presented for practical uses. http://www.iaees.org/publications/journals/nb/articles/2019-9(2)/network-structure-of-neotropical-plant-plant-interactions.pdf Walter Santos de Araujo.Network Biology,2019,9(2):18-27 Plant species can be used as hosts by other plant species, both in antagonistic interactions (e.g., parasitism), and in commensal interactions (e.g., epiphytism). In this study, plant-plant interaction networks were constructed using the literature available from Brazil in order to contrast networks composed by parasitic versus epiphytic plants. Eight plant-plant networks were analysed: five plant-parasite networks and three plant-epiphyte networks. The network structure was characterised using the following network metrics: network size, number of interactions, connectance, modularity and nestedness. In total, plant-plant networks comprised 110 host-plant species and 60 hosted-plant species (parasites or epiphytes) with 269 distinct interactions. Network size, number of interactions, modularity and nestedness did not differ between different types of networks. On the other hand, network connectance in plant-plant networks was significantly different between habitat types (conserved versus anthropised habitats). The present study represents a pioneer systematic investigation showing that structure of plant-plant networks is influenced by habitat conservation status, regardless of the type and the intimacy of interactions between species. http://www.iaees.org/publications/journals/nb/articles/2019-9(2)/design-of-a-common-pathway-drug-for-cardiovascular-diseases.pdf Al-Mustanjid Arif, Chandan Mandal, Md. Habibur Rahman.Network Biology,2019,9(2):28-41 More than an era among all non-communicable diseases, cardiovascular diseases have become a major concern worldwide. Cardiovascular diseases have occurred around the world because of some common risk factors. Diseases have a genetic association indirectly or directly resulting from similar risk factors. A disease is caused when a gene misses out its normal activity and affects the body negatively. Several research works have revealed the ways of how a structure-based drug from key biomolecule or protein can be designed for diseases using modern bioinformatics techniques and tools in network biology. This study evaluates protein-protein interaction network and designs a common pathway drug for all types of cardiovascular diseases. The data mining application called knowledge discovery in database (KDD) has been applied and genes are filtered, pre-processed, transformed and mined to identify common cardiovascular disease genes. Cardiovascular disease genes are collected using R from the National Center for Biotechnology Information gene database. Unihi is used as a tool for achieving the goal. http://www.iaees.org/publications/journals/nb/articles/2019-9(3)/average-reachability-A-new-metric-to-estimate-epidemic-growth.pdf Bita Shams, Mohammad Khansari.Network Biology,2019,9(3):42-57 It is a fundamental issue to find a small subset of individuals in a complex network such that their immunization (i.e. removal) minimizes epidemic growth in the network. Though some network topological metrics have been proposed to estimate the effect of individual immunization or epidemic growth of the network, none of them considered the severity of the current epidemic. This paper proposes a new metric, called average reachability (AR) to estimate epidemic growth in a network. AR incorporates infection rate of epidemics to make a trade-off between network local connectivity and global reachability. Moreover, we intend to generalize stochastic hill-climbing immunization (SHCI) algorithm to minimize network epidemic growth regarding all estimation criteria. SIR simulation on immunized networks shows that the combination of AR and SHCI results in minimal epidemic growth compared to immunization algorithms that minimize density or sum of square partitions. http://www.iaees.org/publications/journals/nb/articles/2019-9(3)/stability-bifurcation-and-chaos-control-in-a-prey-predator-model.pdf Muhammad Salman Khan, Muhammad Asif Khan, Muhammad Sajjad Shabbir, Qamar Din.Network Biology,2019,9(3):58-77 This paper deals with the qualitative study of a discrete-time prey-predator model with Holling type-II response. Particularly, we obtained a dynamically consistent prey-predator discrete-time model by applying a nonstandard difference scheme. We explore the novel explicate parametric conditions for the local stability of positive equilibrium point. Moreover, it is shown that there exists Nimark-sacker bifurcation for the unique positive steady-state of given system. In order to control the bifurcation, we introduced a new control strategy. Moreover, some interesting numerical simulations are provided in order to verify the theoretical discussion and to explore the effectiveness and feasibility of new design control strategy. http://www.iaees.org/publications/journals/nb/articles/2019-9(4)/a-new-dissimilarity-measure-to-construct-phylogenetic-network-among-DNA-sequences.pdf Rinku Mathur, Neeru Adlakha.Network Biology,2019,9(4):78-95 The growth of DNA databases used to store large number of biological sequence data, has stimulated the importance of alignment of sequences for phylogenetics. Most of the phylogenetic methods based on alignment of sequences consume long time to provide the results. In this regard, a new alignment free measure, based on frequency of occurrence of different nucleotides in sequences has been reported. The Euclidean distance metric has been used over these frequencies of nucleotides to obtain the dissimilarities among DNA sequences. These distances are then used to construct the phylogenetic tree among sequences. In addition, a fuzzy linear programming model has been developed here to construct the phylogenetic network which is considered as the generalized form of phylogenetic tree. As an application, the proposed method is applied over the data set of beta - globin gene of nine species and is validated by comparing the obtained results with the already existing method. The results obtained are more promising over the available method and can be applied over any length of input data sequences. http://www.iaees.org/publications/journals/nb/articles/2019-9(4)/class-1-to-3-integrons-in-Salmonella-and-antimicrobial-resistance-pattern.pdf Mehrnoosh Doosti Irani, Mostafa Faghani, Abbas Doosti.Network Biology,2019,9(4):96-106 High prevalent of multiple drug resistant (MDR) Salmonella is considered as a threat for human's health. Integrons are one of the most important factors that can contribute to the occurrence of MDR bacteria. The aim of this study was to determine the prevalence of class 1, 2 and 3 integrons among Salmonella strains isolated from broiler chicks. This study was performed on 100 Salmonella isolated strains, collected from male and female broiler chicks samples in southwest of Iran. The prevalence of class 1-3 integrons were verified using specific primers by multiplex PCR assay. Also antimicrobial susceptibility testing by disk diffusion method was performed for each isolate. Screening of Salmonella isolates revealed the prevalence of class 1, 2 and 3 integrons (50%), (28%) and (48%), respectively. Based on the results of this study significant correlations were between MDR and integrons, this is a serious problem in human and veterinary medicine. According to these results Ampicillin was the most resistant antibiotics against Salmonella isolated strains. The resistance to Gentamicin and Tetracycline and Chloramphenicol has increased in the presence of integrons. The presence of all three classes of integrons and its direct connection with the MDR in Salmonella is concerned. http://www.iaees.org/publications/journals/nb/articles/2020-10(1)/potential-microRNAs-mediated-from-sialic-acid-to-MMP-9-pathway.pdf Negar Noorbakhsh, Mina Zamani, Hamid Galehdari.Network Biology,2020,10(1):1-9 Sialic acids and MMPs play critical roles in inflammatory diseases. Furthermore, Interaction between Sialic acid and receptors such as siglecs leads phosphorylation of ITIM domains and promote downstream inhibitory signaling through SHP-1 phosphatases. SHP1 could positively regulate TNF-alpha and by control, the production of TNF-alpha could play a crucial role in inflammation. Besides, TNF-alpha could mediate the signaling pathway leading to MMP-9 gene expression. MMP-9 also is recognized as therapeutic targets in a variety of diseases including vascular pathologies, cancers, and auto-immuned diseases. The present in-silico study aims to identify the most potent micro-RNAs could control the signaling pathway from siglec to MMP-9. To this end, with review some articles and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis21 genes involved in this pathway have been selected. Then TARGET SCAN, DIANA-TarBase8, and miRDB database were utilized to predict the miRNAs which have the most effective to target genes. Finally, using bio-studying Software Cytoscape, three microRNA-mRNA networks were constructed for existing banks. We found shared micro-RNAs that in the three networks. Eventually, using miRTarBase database microRNAs that were linked to more genes in this path were assigned a higher privilege. The twenty-one selected micro-RNAs could be the proper options for experimental studies from sialic acid receptors (siglecs) to MMP-9. Among them miR-34a-5p could be the most interesting target. http://www.iaees.org/publications/journals/nb/articles/2020-10(1)/influence-maximization-algorithm-in-social-network.pdf Alighanbari, Esmaeil Bagheri.Network Biology,2020,10(1):10-23 The increasing use of services and different applications of social networks has led to a wide range of research and studies in the field of information technology and computer networks towards such networks. Creating a wide platform for advertising in social networks and attracting more customers in this way has created a variety of ways to maximize profits. Therefore, due to the high importance of the propagation speed and the extent of advertising, the issue of influence maximization is considered special. The influence maximization can be described as: determining a small set of nodes capable of operating large waterfalls of behavior that are spread across the network. In other words, selection of a set of K nodes from a social network is in such a way that the influence of the node in the network has maximum value. Due to the high sensitivity of the influence maximization process, in this study we try to reduce the strengths and problems of previous strategies in this field by K-shell decomposition and community detection based on SLPA algorithm. The proposed approach in this research is based on the recognition of community based on SLPA algorithm, to make a better result by flexible and optimizing the decision making in exploration and extraction of societies. In both methods, K-shell analysis and community detection are used to choose the more influential nodes, which are proportional to the graph of social networks. The proposed method is evaluated based on two fundamental criteria of execution time and number of active nodes, which have better efficiency and efficiency compared to previous methods. http://www.iaees.org/publications/journals/nb/articles/2020-10(1)/possible-synergistic-effect.pdf Reza Taherkhani, Omid Parvizi, Mehran Aboozari.Network Biology,2020,10(1):24-31 The possible synergistic effect of using a combination of formic acid (FA) and plant essential oils (EO) in broiler chick's drinking water was investigated. Performance and gut microflora were assessed from day old to 42 d of age. The experiment was carried out using a completely randomized design with factorial arrangement (2 by 3). Factors were included formic acid (0, 1000 and 2000 ppm) and EO (0 and 250 ppm) level which were administered through drinking water. Both FA and EO improved performance criteria but their combination failed to create a synergistic effects. Chicks received FA supplemented water had significantly lower numbers of C. perfringens and coliforms. Administration of EO also significantly lowered numbers of gut pathogenic bacteria (C. perfringens and coliforms) while did not affect lactobacilli population. Results obtained in our study suggest a synergistic effect of using FA and EO simultaneously only in reducing gut pathogenic bacteria. http://www.iaees.org/publications/journals/nb/articles/2020-10(2)/interactome-analysis-and-docking-site-prediction-DNA-repair.pdf Mohamed Ragab Abdel Gawwad, Mohamed Soliman Elshikh, Haris Lokvancic.Network Biology,2020,10(2):32-44 There are seven homologs in eukaryotic RAD51 gene family which are conserved among animals and plants, and those are RAD51, DMC1, RAD51c, XRCC3, RAD51b, RAD51d and XRCC2. The first four of them are important in the process of homologous recombination, but also the DNA repair mechanism, while the other three show normal meiosis. RAD51, DMC1, RAD51c and XRCC3 have lineages that are divergent from the other three paralogs, showing potential functional redundancy. The repair mechanism also includes single- or double-strand break rejoining during replication, recombination and DNA damage, which is made by the DNA ligase enzymes. There are many DNA ligase enzymes, and the sequenced genome of Arabidopsis thaliana showed a homologue XRCC4 of the human DNA ligase IV binding protein. Arabidopsis thaliana encoded also homologues for the other six vertebrate Rad51 proteins. Our Results showed the XRCC2 and XRCC3 are interacting with Rad51c. Tow complexes will be formed; BCDX2 (RAD51B-RAD51C-RAD51D-XRCC2) and CX3 (RAD51C-XRCC3). The two complexes have a function at two distinct stages of homologous recombinational DNA repair. http://www.iaees.org/publications/journals/nb/articles/2020-10(2)/chaotic-dynamics-and-control-in-discrete-time-predator-prey-system.pdf S. M. Sohel Rana.Network Biology,2020,10(2):45-61 In this paper, a discrete-time predator-prey system with a functional response of Ivlev type is examined to reveal its chaotic dynamics. We algebraically show that the system undergoes a flip bifurcation and/or Neimark-Sacker (NS) bifurcation in the interior of R2+ when one ofthe model parameter crosses its threshold value. Via application of the center manifold theorem and bifurcation theorems, we determine the existence conditions and direction of bifurcations. Numerical simulations are employed to validate analytical results which include bifurcations, phase portraits, periodic orbits, invariant closed cycle, sudden disappearance of chaotic dynamics and abrupt emergence of chaos, and attracting chaotic sets. Furthermore, maximum Lyapunov exponents and fractal dimension are computed numerically to justify the existence of chaos in the system. Finally, we apply a strategy of feedback control to control chaotic trajectories exist in the system. http://www.iaees.org/publications/journals/nb/articles/2020-10(3)/Deep-Convolutional-Neural-Network-with-PCA-and-Logistic-Regression.pdf Nahida Habib, Md. Mahmodul Hasan, Mohammad Motiur Rahman.Network Biology,2020,10(3):62-76 Consistent headway in machine learning technology is gradually substantiating its significance in many areas of medical research. Pneumonia is a disease caused due to acute respiratory infection affecting one or both lungs. Diagnosis and treatment of pneumonia at early stage can increase the survivability of suffering patients. Computer Aided Diagnosis (CAD) techniques are bridging up the gap of medical science and computer science by successfully diagnosing diseases such as tumor, cancer, pneumonia etc. This paper proposes a fusion of Deep Convolutional Neural Network Model with Principal Component Analysis (PCA) feature extraction model and Logistic Regression (LR) classifiers for the diagnosis of pneumonia from chest X-ray images. In this study, fine-tuned pre-trained CheXNet model is used as Convolutional Neural Network (CNN) model on standard pneumonia dataset collected from Guangzhou Women and Children's Medical Center, Guangzhou. The proposed model is capable of detecting pneumonia with an accuracy which outperforms the existing methods from 0.8% to 21.9% approx. Comparison with existing models and methods reveal that the proposed model delivers superior results than others according to precision, f1-score, accuracy and AUC values. This research can be a great subsidiary for radiologists or medical researchers for diagnosis of pediatric pneumonia from chest X-ray images. http://www.iaees.org/publications/journals/nb/articles/2020-10(3)/immune-parameters-and-intestinal-microbial-flora.pdf Omid Parvizi, Reza Taher Khani, Mehran Aboozari, Fardis Fathizadeh.Network Biology,2020,10(3):77-83 This study was to investigate the effect of using fenugreek essential oil on performance, some immune parameters and intestinal micro-flora on broiler chicks. A total of 240 one day Ross 308 broiler chicks were divided and assigned into 4 groups and 5 replicate of 16 birds each. Chicks were fed by basal diet as control and 3 levels (100, 200 and 250 ppm) of fenugreek essential oil respectively. During the experimental period feed intake, body weight gain and feed conversion ratio were calculated. After 30 and 38 days last, the blood samples were taken from wing vein to evaluate the sheep red blood cell (SRBC) and anti-body against Influenza and New castle disease vaccine (ND) respectively. For evaluation carcass traits 4 birds of the same weight in each group were slaughtered, separated and weighed. The obtained result showed that the highest feed intake related to the fenugreek essential oil and highest body weight was seen in the groups that the fed by fenugreek essential oil. Also there were significant differences between treatments about feed conversion ratio (p not greater than 0.05). The SRBC, ND and Influenza titer as immune system parameters was for fenugreek essential oil respectively. The carcass evaluation mentioned that the highest carcass percentage was for fenugreek oil and there were significant differences between groups about intestine and gizzard percentage. The results showed that different levels of fenugreek oil used in experimental broilers had significant effects on intestinal microbial population flora. We may conclude that fenugreek essential oil at the present levels could have better body performance, some carcass parameters, and immune system in Ross 308 broiler chicks. http://www.iaees.org/publications/journals/nb/articles/2020-10(4)/association-of-VNTR-27-bp-polymorphism-in-intron.pdf Adel Qlayel Alkhedaide, Adil Mergani, Mohamed Mohamed Soliman, Tamer Ahmed Ismail, Samir Ahmed Elshazly, Ayman Sabry.Network Biology,2020,10(4):84-91 Genetic variation and polymorphism became a hot spot for researches to study the link between societies and certain endemic diseases. This study is an attempt to examine the possible association of the incidence of ischemic heart disease with the genetic variations of 27-bp variable number of tandem repeats located in intron 4 of the eNOS3 gene among Taif population. A case-control study included 81 Ischemic Heart Disease (IHD) patients and 225 unrelated healthy participants from the population living in Taif City. Genotyping of the candidate sequence 27-bp repeat located in intron 4 VNTR of the eNOS3 gene in was conducted using the polymerase chain reaction technology. The minor allele (4a) was slightly less frequent among IHD patients and insignificantly linked to reduced relative risk for IHD. In addition, a significant difference in the distribution of both heterozygous genotype 4a4b between IHD patients and normal groups (p value = 0.012). Presented data suggest that the heterozygous genotype of eNOS3 gene intron 4a4b VNTR variation is might be associated with lowering the risk of IHD in the Taif population in the west of Saudi Arabia. While the minor allele (a) of the eNOS3 gene is insignificantly related to the predisposition of IHD. http://www.iaees.org/publications/journals/nb/articles/2020-10(4)/maximizing-influence-on-social-networks-based-on-node-membership.pdf Esmaeil Bagheri.Network Biology,2020,10(4):92-107 Influence maximization is one of the fundamental issues in social networks context. In viral marketing which is one of applications of this category, a small group of users are selected to accept a product and influence of these users on other people might result in massive acceptance of this product in social network. The influence maximization problem is choosing a set of k nodes from a social network that maximizes the influence in the network. Various studies have been conducted to find more effective k nodes for influence propagation on social networks. But the main challenges of these studies are the lack of scalability and low speed. Influential nodes must also have local influence and global influence throughout the network so that they can affect the entire network at an acceptable time. Considering the important role of influential nodes in each community for influence propagation in that community and, consequently propagating the influence throughout social network, in this paper, an algorithm is presented that maximizes the influence throughout social network through finding the nodes that have more membership strength to their community. The proposed algorithm is tested on several real and synthetic social networks. Experimental results show that the proposed method can effectively find appropriate seed nodes for influence maximization. http://www.iaees.org/publications/journals/nb/articles/2020-10(4)/effects-of-savory-aqueous-extract-on-immune-parameters-of-broiler-chickens.pdf Mohammad Yadegari, Hasan Ghahri, Mohsen Daneshyar.Network Biology,2020,10(4):108-118 An experiment was done to study effects of savory aqueous extract on performance, carcass traits, some blood biochemical and immune parameters of broiler chickens under heat stress condition. To do this 320 day-old Ross chickens were assigned to four distinct treatments in a complete randomized design. Each treatment was given to four replicates of twenty birds. Variables were heat stress (34 +- 2 oC for 8 hours) and savory extract (0.4 ml/L) in drinking water. At different weeks of trial, feed intake (FI), weight gain (WG) and feed conversion ratio (FCR) was measured. Some relative weight of different organs (dressing, breast, thigh, liver, heart, spleen and bursa of Fabricus) determined at d 42 of age. The blood serum glucose and plasma content of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP) were measured after blood sampling at d 42 of age. Plasma IgG were quantified on days 21, 28, 35 and 42. The savory extract did not affect FCR, or the relative weights of different organs (p greater than 0.05). BW and FI increased with savory oil inclusion (p less than 0.05). Besides, the savory extract decreased the plasma glucose, AST and ALT of heat stressed broilers significantly (p less than 0.05). Also the ALP content decreased but not significantly (p greater than 0.05). Totally, blood IgG of heat stressed broilers, increased with savory extract treating (p less than 0.05). In conclusion, under heat stress situations, 0.4 ml/L of savory extract improves economic proficiency in broiler flocks due to accumulation of minute advantages in greater WG, FI, and improved IG and lowered hepatic enzymes. http://www.iaees.org/publications/journals/nb/articles/2021-11(1)/reliability-analysis-of-flow-networks.pdf Ali Muhammad Ali Rushdi, Omar Mutab Alsalami.Network Biology,2021,11(1):1-28 This paper attempts to set the stage for a prospective interplay between ecology and reliability theory concerning the common issue of the concept of a capacitated or flow network. The paper treats the problem of species survivability, which pertains to the ability of a specific species to avoid local extinction by migrating from a critical habitat patch to more suitable destination habitat patches via perfect stepping stones and heterogeneous imperfect corridors. The paper proposes various types of techniques for analyzing a capacitated ecological network for the process of migration in a metapopulation landscape network that arises when paths to destination habitat patches share common corridors. These techniques include (a) Karnaugh maps, which are crucial in providing not only the visual insight necessary to write better future software but also constitute an adequate means of verifying such software and, (b) a generalization of the max-flow min-cut theorem that is applicable through the identification of minimal cut-sets and minimal paths in the ecological flow network. Care is taken to ensure that the reliability expressions obtained are as compact as possible and to check them for correctness. The ecological network capacity is a random pseudo-Boolean (-switching) function of the corridor successes; and hence, its expected value is easily obtainable from its sum-of-products formula. This network capacity has obvious benefits in the representation of nonbinary discrete random functions, which commonly arise during the analysis of flow networks. A tutorial example demonstrates these methods and illustrates their computational merits with ample details. http://www.iaees.org/publications/journals/nb/articles/2021-11(1)/validation-of-forward-in-time-method-using-artificial-neural-network.pdf Abdurahim Kalajdzic, Samim Konjicija, Belma Kalamujic Stroil, Naris Pojskic.Network Biology,2021,11(1):29-43 Simulation studies in population genetics play a crucial role in better understanding of different evolution scenarios and effects of different genetic models on genetic diversity. Forward-in-time method starts with an initial population and follows the entire evolution under various genetic models within multiple generations. Artificial neural networks represent a formidable method for genetic simulation and prediction. In this study, we wanted to compare and corroborate results obtained with forward-in-time simulation with results attained from a specially designed strategy based on artificial neural networking. As input data, alleles of 13 microsatellite loci from 187 specimens representing autochthonous Adriatic haplotype of Salmo trutta L. from the Neretva River were used. The main goal of this study was to compare precision and reliability of these two methods. Our results are in concordance with other reports from literature which indicate that both of these approaches can be used as a reliable simulation tools. However, it is believed that artificial neural networks can represent more powerful simulation tools. http://www.iaees.org/publications/journals/nb/articles/2021-11(1)/correlational-analysis-between-feed-amino-acid-profile.pdf Familusi Oluwatosin Adekunle, I. A. Adebayo, Obaisi Alaanuloluwa.Network Biology,2021,11(1):44-53 Sustainable use of natural resource such as fishmeal in aquaculture, can be achieved by substituting with more abundant biomass. This goal relies on the use of available research methods to understand how cultured species respond to each substitute and factors responsible for such responses to know how they can be modified for better response. The quantity and quality of research needed for understanding these relationships can be optimized by employing identified statistical methods. Correlation coefficient is a statistical method to quantify degree of corresponding changes between 2 features, while LASSOCV gives a defined list of independent features, relevant to the target feature being analyzed based on the ordinary least squares. Most relevant features to RBC are phenylanaline and valine, other less relevant features are histidine and threonine, all other essential amino acids are identified as irrelevant to RBC with a certainty score of 50% (R square). RBC and HB showed strong relationship with a coefficient of 0.8, but differ in relevant amino acid components (isoleucine, histidine and threonine). The analysis suggests the relevant amino acids for each clinical parameters, which can be confirmed with further experiments. http://www.iaees.org/publications/journals/nb/articles/2021-11(2)/network-pharmacology-and-component-analysis-of-four-herbs.pdf Fui Fui Lem, Fernandes Opook, Fahcina P. Lawson, Wilson Thau Lym Yong, Fong Tyng Chee.Network Biology,2021,11(2):54-67 Traditional Chinese Medicines (TCM) are known for their curative effects on hypertension through a holistic approach. The molecular mechanisms of the formulation comprising Polygonum multiflorum, Rehmannia glutinosa, Senna obtusifolia and Crataegus, used by Chinese practitioners in ameliorating hypertension, however remain a mystery. This initial study is thus aimed at unveiling the molecular mechanisms of this TCM formulation in treating hypertension. The methanolic extract compounds of the decoction were identified through Liquid chromatography mass spectrometry-mass spectrometry (LC-MS/MS). Oral bioavailability and drug likeness were then measured to filter out identified compounds. Several databases, such as the SwissTargetPrediction, STRING, OMIM and KEGG, were used to retrieve information on the predicted targets for the purpose of developing a network using Cytoscape Version 3.8. Enrichment analysis was then performed to elucidate the mechanisms of the decoction in hypertension mitigation. A total of 11 compounds identified were revealed to possess bioavailable and drug like characteristics, based on the Veber and Quantitative Estimation of Drug-likeness (QED) parameters. Pathway analysis showed enrichment of pathways such as cardiac muscle contraction, fluid shear stress and atherosclerosis, dilated cardiomyopathy, renin-angiotensin system and hypertrophic cardiomyopathy (HCM), which are all strongly associated with hypertension. The network pharmacology analysis clearly shows that this TCM decoction ameliorates hypertension through several indirect pathways where most of the targets are involved in HCM, which is caused by hypertension. http://www.iaees.org/publications/journals/nb/articles/2021-11(2)/predicting-lung-cancer-survivability.pdf Iffat Jabin, Mohammad Motiur Rahman.Network Biology,2021,11(2):68-81 Lung cancer is one of the main leading causes of cancer death in all over the world. Accurate prediction of lung cancer survivability can enable physicians to make more reliable decisions about a patient's treatment. The objective of this research is to design robust machine learning model with supervised regression model to predict survivability of the lung cancer patients. This work includes Multiple Linear Regression, Support Vector Regression with Radial Function, Random Forest, Extreme Gradient Boosting Tree regression algorithms to build an ensemble model using stacking technology with meta-learner Gradient Boosting Machine. This experiment is performed on large SEER 2011-2017 dataset. The novel model achieved a high root mean squared error (RMSE) value of 8.58459 on the test dataset which outperforms the base models. The experimentation results show that the proposed system attains better result compared to the existing models. http://www.iaees.org/publications/journals/nb/articles/2021-11(2)/constrained-minimal-cost-network-flow-problem.pdf Muhammad Tlas.Network Biology,2021,11(2):82-96 A polynomial time algorithm for solving the minimum-cost network flow problem has been proposed in this paper. This algorithm is mainly based on the binary representation of capacities; it solves the minimum-cost flow problem in directed graph of n nodes and m directed arcs as a sequence of O(n2) shortest path problems on residual networks. The algorithm runs in O(n2mr) time, where r is the smallest integer greater than or equal to Log2B, and B is the largest arc capacity of the network. A generalization of this proposed algorithm has been also performed in order to solve the minimum-cost flow problem in a directed network subject to non-negative lower bound on the flow vector. A formulation of both the transportation and the assignment problems, as a minimal cost network flow problem has been also performed. A numerical example has been inserted to illustrate the use of the proposed method. http://www.iaees.org/publications/journals/nb/articles/2021-11(2)/capacitated-networks.pdf Ali Muhammad Ali Rushdi, Omar Mutab Alsalami.Network Biology,2021,11(2):97-124 This paper introduces a novel method for analyzing capacitated networks through the utilization of the concept of a "probability-ready expression" for a Boolean-based coherent pseudo-Boolean function. Our main concern is to assess the performance indexes of biology and ecology networks having fixed channel capacities. The technique introduced is based on constructing an exhaustive description (specifically, a value-entered Karnaugh map) for the pseudo-Boolean capacity function of the network via a generalization of the max-flow min-cut theorem. Then the function is expressed in a disjunctive-normal form (DNF) by obtaining the socalled 'contributions' of each entered value via standard Karnaugh maps. The technique heavily relies on the fact that the pertinent function is a coherent one, and it is self-checking since it must produce a DNF of solely uncomplemented Boolean literals. The notorious Inclusion-Exclusion (IE) Principle is ruled out as a practical means for converting the DNF of the capacity function into its probabilistic expectation (its expected value). Instead, a method is proposed for converting the DNF of the capacity function to a 'probability-ready expression' (PRE), which can be easily transformed, on a one-to-one basis into a probability function. Two tutorial examples demonstrate the afore-mentioned method and illustrate its computational advantages over the exhaustive state enumeration method and the IE method. http://www.iaees.org/publications/journals/nb/articles/2021-11(3)/amino-acids-network-based-on-transition-of-codons.pdf Tazid Ali, Chandra Borah.Network Biology,2021,11(3):125-136 In this paper, we have developed a network of 20 amino acids based on a distance matrix of amino acids. This distance matrix is obtained by considering the transition and transversion mutation of codons. We have proposed that the evolutionary pattern of amino acids is reflected throughout this network. We have discussed different measures of centrality: degree centrality, closeness centrality, betweenness centrality and eigenvector centrality, concerning this network and investigated the comparative impact of the amino acids. We have also explored the correlation coefficients between the different centrality measures checking the assortativity of the network. Further, we have explored three network parameters: namely clustering coefficient, degree of distribution and skewness. http://www.iaees.org/publications/journals/nb/articles/2021-11(3)/genes-associated-with-lung-and-liver-disease-in-Cystic-Fibrosis.pdf Rafat Ali, Safia Tazyeen, Mohd Murshad Ahmed, Aftab Alam, Nikhat Imam, Shahnawaz Ali, Md Zubbair Malik, Romana Ishrat.Network Biology,2021,11(3):137-153 The pathogenesis of Cystic Fibrosis (CF) airway disease is not well understood. CF is an autosomal recessive monogenic genetic disease. It affects the exocrine glands, which normally produce thin secretions such as mucus, sweat and tears. In CF, the mucus is thick and sticky which interferes with certain normal organs. A broad knowledge of the genes which are involved in the regulation or co-regulation of affected organs in the CF is required to get a better understanding of its pathophysiological mechanisms. DNA microarray approaches have made it possible to get an insight on gene expression across the genome. In the current study, microarray data related to CF and CF-associated affected organs were retrieved from the NCBS Gene Expression Omnibus database and were subjected to gene regulatory network analysis. We constructed two separated networks of up and down regulated genes from six microarray datasets. The power-law obeying topological properties showed scale-free hierarchical nature of the both networks. Density and compactness of both networks at each level was calculated by modularity and Hamiltonian energy. From all the leading hubs we found four key genes namely GSTT1, ANKRD7, PBX1, and TGFB2 deeply rooted in up and down regulated networks respectively. Conclusively these genes may have prognostic significance. http://www.iaees.org/publications/journals/nb/articles/2021-11(3)/construction-and-analysis-of-word-network-from-Random-Reading-Frame.pdf WenJun Zhang.Network Biology,2021,11(3):154-193 In present study, a method was developed to construct and analyze the word network. The core of the method is Random Reading Frame (RRF) method. First, download or collect word files (in various formats, e.g., pdf, txt, doc, docx, rtf, html, etc.) from internet or local machine in terms of the concerned topics. All files were then combined in a final text file. Excepting for splitting words and stop words, all words were arranged in a word vector following their orders in the combined text file. In the RRF method, for a given pair of unique words (x, y), x, y belong to {u1,u2,...,um}, a reading frame with randomly changeable width is randomly placed on the vector to count the respective number of the two words in the frame. Randomly repeating the procedure p times, the paired numbers are thus achieved: (x1, y1), (x2,y2), ..., (xp, yp). In such a way, the paired numbers for all pairs of unique words are achieved. Thereafter, for a given pair of unique words (x, y), Pearson correlation and Pearson partial correlation, Spearman rank correlation, or point correlation is used to calculate their correlation value according to their paired numbers (x1, y1), (x2,y2), ..., (xp, yp), and the statistically significance can be determined by t-test (Pearson correlation, Pearson partial correlation, Spearman rank correlation) or chi2-test (point correlation). In such a way, all statistically significant word pairs are achieved in terms of the correlation measure chosen by user. Finally, the word network, in terms of the correlation measure chosen, can be constructed based on these word pairs, and no links between statistically insignificant word pairs. Network analysis is conducted for the word network constructed from significant between-word positive correlations among all unique words. Word centrality measures, word tree, word chains, word modules, etc., can be calculated in the method. The Matlab software, wordNetwork for the method was given also. http://www.iaees.org/publications/journals/nb/articles/2021-11(3)/driver-genes-in-renal-stress-condition-using-network-clustering.pdf Mohd Murshad Ahmed, Safia Tazyeen, Rafat Ali, Aftab Alam, Md Zubbair Malik, Romana Ishrat.Network Biology,2021,11(3):194-206 Chronic kidney disease (CKD) is a non-contagious, ageing-related and covert disease. It is a (chronic) disease of the kidneys leading to renal failure, huge public health problem worldwide and common comorbidity with type 2 diabetes mellitus (T2DM). Its presence and severity influence disease prognosis significantly. Identification of driver genes which regulate the CKD network is one of the main challenges in understanding its biological significance. We have analyzed microarray dataset and compare the gene expression profile of the patient with healthy control. Besides, we studied the gene regulatory networks that may help to understand the molecular mechanism in CKD. Further, after a comparative analysis of CKD and DKD. We proposed five driver genes, namely ALB, WT1, IL7R, PTPRC and DOCK2, that play an essential role in the pathogenesis of CKD and could serve as biomarkers. In the present study, we have mapped and analyzed the interactions of these five genes in the form of network and in addition to this we also tracked down the other essential, i.e. driver genes responsible for the modular nature of the network. The proposed study is based on network analysis approaches to predict some unknown CKD-associated genes, which can be validated as reliable candidates for further in vitro/in vivo experiment. http://www.iaees.org/publications/journals/nb/articles/2021-11(3)/potential-role-of-non-coding-RNAs.pdf Mahya Payazdan, Mohammad Shafiei, Seyedeh Sahar Mortazavi Farsani.Network Biology,2021,11(3):207-221 Long non-coding RNAs (lncRNAs) have attracted lots of attention worldwide. With the rapid advances in bioinformatics, several lncRNAs have been identified in the last decade. Ample evidence has shown that lncRNAs are involved in different mechanisms and play chief roles in many biological processes. Therefore, dysregulations of lncRNAs are associated with human complex diseases including glioblastoma (GBM). In this study, we have used lncRNA high throughput data analysis and some databases about their expression level, function, etc. Currently, a limited number of GBM-related lncRNAs have been reported experimentally. Therefore, analyzing lncRNA-GBM associations and predicting potential lncRNAs would benefit mechanism understanding, diagnosis, treatment, and prevention of this tumor. Therefore, we applied in silico analysis to find GBM-related lncRNAs and select the most promising lncRNAs for experimental validation. According to the results RP11-573G6.6 and HCG11, lncRNAs play critical role in GBM pathogenesis and could be promising targets in novel therapeutic approaches. http://www.iaees.org/publications/journals/nb/articles/2021-11(3)/diagnosis-of-diabetes-machine-learning-paradigm.pdf Rafid Mostafiz, Khandaker Mohammad Mohi Uddin, Mohammad Shorif Uddin, Farhana Binte Hasan, Mohammad Motiur Rahman.Network Biology,2021,11(3):222-240 Diabetes is considered one of the incurable diseases at present which is caused by hyperglycemia. Modern healthcare finds some attributes such as uncontrolled lifestyle, lack of balanced diets, genetic complexities, excess mental fatigue, obesities, and so on, which are responsible to precipitate the rapid mobility of diabetes diseases. This is not only a single disease but it also damages the nervous systems, heart, kidney, liver, eyes, and various organic metabolisms. Currently, the clinical industries have a huge amount of data for the diagnosis of diabetic patients. Machine learning algorithms can work appropriately to mitigate this tedious task in finding hidden patterns, discovering knowledge from the database, and predict outcomes. This research has proposed an efficient machine learning-based diagnosis methodology that outperforms the existing similar methodologies. The experiment selects the minimum Redundancy Maximum Relevance (mRMR) features from the working dataset and then recursive feature elimination (RFE) technique for optimization. The irregularity problem in the dataset is addressed by the synthetic minority oversampling technique (SMOTE). Machine learning classification is performed on the selected optimized features through Decision Tree (C4.5 DT), K-Nearest Neighbors (KNN), Naive Bayes (NBs), Support Vector Machine (SVM), Logistic Regression (LGR), and Random Forest (RF), where RF classifier produces best-suited results with minimum false detection rate. This experiment has used a 5-fold cross-validation approach to justify the reliability of the proposed model and finally obtain an accuracy of 98.10%. http://www.iaees.org/publications/journals/nb/articles/2021-11(3)/interactome-and-docking-site-of-DNA-repair-proteins.pdf Esma Kurtanovic, Mohamed Ragab Abdel Gawwad.Network Biology,2021,11(3):241-246 Flowering time is a life history trait of adaptiveness. Over many generations, phenotypes happen to emerge as mutations or spontaneous damage accumulates in the plastome. Thus, it is of great importance to investigate DNA repair mechanism roles of some proteins. Specifically, this study aims to determine potential targets that are part of base excision repair mechanism in Arabidopsis thaliana. To do so, bioinformatic methods are implemented in order to shed light on the functioning of our protein homologs. Their structural and functional similarities are confirmed by multiple sequence alignment, 3D structure prediction, phylogenetic tree construct and interactome analysis. The results indicate that interaction between two proteins is strong evidence that the proteins are involved in the same biological process. This study can be seen as a valuable data resource of predicted cellular functions of proteins and the evolutionary conservation of AP endonuclease families, which again, portrays the divergence of activities and biological contributions. http://www.iaees.org/publications/journals/nb/articles/2021-11(4)/web-tool-for-generating-user-interface-interactive-networks .pdf WenJun Zhang.Network Biology,2021,11(4):247-262 In the present study, an online web tool (http://www.iaees.org/publications/software/netJa/netGen.htm) for generating user-interface interactive networks was presented. In the network, the user can mouse-press any of the nodes to drag the network, examine network topology, evaluate node centrality, etc. It can be freely used and run on popular web browsers as Chrome, etc. http://www.iaees.org/publications/journals/nb/articles/2021-11(4)/a-method-for-causality-inference-of-Boolean-variables .pdf WenJun Zhang.Network Biology,2021,11(4):263-273 In present study, a statistical simulation method for causality inference of Boolean variables was proposed. First, I used statistical simulation to generate artificial data of two Boolean variables with known independent and dependent variables. A law was drawn from the simulation analysis of the artificial data. For a set of data of two Boolean variables, a randomization method was proposed and used to test the statistical significance of the Boolean correlation measure (point correlation, quartile correlation, or Jaccard correlation, etc.). The causality inference was then conducted to observed data based on the law. Finally, the statistical simulation was used to determine the statistic significance of the causality. Full Matlab codes were presented also. http://www.iaees.org/publications/journals/nb/articles/2021-11(4)/reflections-on-intrinsically-disordered-proteins.pdf Mouna Choura, Ahmed Rebai.Network Biology,2021,11(4):274-276 Intrinsically Disordered Proteins or Regions (IDPs) are proteins that lack a predetermined 3D structure playing key cellular functions including regulation, signaling, and protein-protein/DNA interaction. IDPs are often involved in diseases such as cancer, cardiovascular and neurodegenerative diseases, and diabetes. IDPs have been shown to be attractive therapeutic targets and drug development. Intrigued by these controversial observations, some questions are raised: how IDPs are so common even under the scenario that they are unstable and linked to misfolding and diseases? Does the cellular regulation depend on disorder? http://www.iaees.org/publications/journals/nb/articles/2021-11(4)/structural-and-functional-properties-of-ORF4-Hepatitis-virus.pdf Zoya Shafat, Ayesha Tazeen, Murshad Ahmed, Mohammad K. Parvez, Shama Parveen.Network Biology,2021,11(4):277-294 Hepatitis E virus (HEV) belongs to the family Hepeviridae and is the major cause of hepatitis E infections across the globe. Recently, a novel viral protein of HEV, named as open reading frame (ORF4), has been associated with its replication in genotype 1 isolates. However, much information regarding ORF4 has not been explored. Thus, the study was conceptualized to explore the structural and functional features of HEV ORF4 protein to better understand the possible molecular mechanisms. The detailed investigation of the ORF4 was carried out in terms of its physicochemical properties, secondary and tertiary structure predictions and functional analysis using different bioinformatics tools. The in silico analyses revealed that ORF4 sequences were enriched in Serine, Proline and Glycine amino acid residues suggesting the prevalence of disordered residues. The protein was found to be thermostable, unstable and highly hydrophobic. The structural analysis showed the presence of cleft, tunnel and pore suggesting their participation in interaction with other molecules. Moreover, identification of several modified sites in ORF4 sequences such as glycosylation, phosphorylation and myristoylation sequences suggest their involvement in cellular signaling pathways and biological processes. Thus, taken together, it can be interpreted that HEV ORF4 possesses significant enormous flexibility due to the presence of Serine, Glycine and Proline amino acids, which suggest its involvement in protein-protein interaction. Furthermore, the presence of motifs, clefts and tunnels also strengthens our analysis, suggesting the commitment of ORF4 towards interaction with other target molecules. Thus, it could be potent drug-targets. http://www.iaees.org/publications/journals/nb/articles/2021-11(4)/dark-proteome-of-Hepatitis-E-Virus.pdf Zoya Shafat, Anwar Ahmed, Mohammad K. Parvez, Shama Parveen.Network Biology,2021,11(4):295-314 Hepatitis E virus (HEV) is a quasi-enveloped RNA virus of the family Hepeviridae. HEV is the chief cause of acute hepatitis worldwide, causing approximately 20 million infections annually, which results in 60,000 deaths. Due to insufficiency in appropriate HEV in vitro cell culture systems, our knowledge of its pathogenesis is inadequately understood. HEV encodes three open reading frames (ORFs): ORF1 (replicative machinery), ORF2 (viral capsid) essential for (infectious particles formation) and ORF3 (viral release). The presence of known and unknown coding and non-coding regions of HEV ORFs are still debated. Viral proteins entail disordered regions which are linked with the infectivity and pathogenicity of virus. Thus, we examined the dark proteome of HEV through analyzing intrinsically disordered protein regions (IDPRs) present in the ORFs by exploiting computational methodologies. Our findings suggested that ORF3 had the highest prevalence of disordered regions. The ORF3 region was followed by ORF2, which had comparatively lesser fraction of intrinsic disorder. The ORF1 had the least number of disordered residues in the HEV proteome. Our intrinsic disorder analysis results revealed that ORF1 polyprotein consists of mostly ordered domains, i.e., proteins having significant level of well-defined structures, with the exclusion of Pro and PCP domains. The analysis reveals Pro domain as a highly disordered protein while PCP domain as an intrinsic disordered protein. MoRF analysis revealed that HEV proteome contains multiple MoRFs across all ORFs. IDPRs are characterized by remarkable conformational flexibility and structural plasticity resulting in their engagement in several biological processes. Due to possession of MoRF in the HEV proteins, these regions can be used for protein-protein interactions due to the structural flexibility. IDPRs are characterized by remarkable conformational flexibility and structural plasticity resulting in their engagement in several biological processes. Due to possession of MoRF in the HEV proteins, these regions can be used for protein-protein interactions due to the structural flexibility. These extensive findings on the HEV proteome will have significant implications in understanding the deeper functioning of structural as well as non-structural biology of HEV proteins. http://www.iaees.org/publications/journals/nb/articles/2021-11(4)/ultrasonic-in-food-microbiology.pdf Kartikey Chaturvedi, Smriti Chaturvedi, Siddhartha Singha, Kalyan Das.Network Biology,2021,11(4):315-333 Ultrasonic is one of the promising technological innovation to modify structure, inactivate enzymes and/or neutralize microorganisms in food products for enhancement of their quality and safety. Apart from pasteurization, sanitation, disinfection and cleaning procedures, in the area of food microbiology, ultrasound can facilitate recovery of microorganisms and their identification through cell lysis or detachment of microbe from food surface. Hence, study of effect of sound waves on microbial cells in suspended condition and food matrices has opened a new horizon of its application in the area of food microbiology. Use of ultrasound in microbial analysis is already in practice and expanding, but the physics of interaction of acoustic waves with microbial cells in presence of actual food matrices need further attention. Ultrasonic reactor (UR) design needs interdisciplinary approach to further exploit the promise of ultra sound in food microbiology. Substantial demand in recent years for bench-top ultrasound reactors for cleaning of food contact surfaces in common households has increased. This review deals with the state of art of the ultrasound technology, process development, and further scope of the technology specific for its applications in cleaning and sanitization, microbial inactivation and in microbiological analysis in food processing industries. http://www.iaees.org/publications/journals/nb/articles/2022-12(1)/total-graph-in-genetic-code-algebra.pdf Birinchi Kumar Boruah, Tazid Ali.Network Biology,2022,12(1):1-10 Suppose R be a commutative ring and Z(R) its set of zero-divisors. Total graph is the (undirected) graph where set of all elements of R is taken as the vertex set and two vertices say x and y (x not equals to y) in R are adjacent if and only if their sum is zero-divisor. Genetic code is the blueprint for protein synthesis. In this paper we discuss total graph in the genetic code algebra. http://www.iaees.org/publications/journals/nb/articles/2022-12(1)/characteristics-of-ORF6-encoded-protein.pdf Zoya Shafat, Anwar Ahmed, Mohammad K. Parvez, Shama Parveen.Network Biology,2022,12(1):11-25 Hepatitis E virus (HEV) of the family Hepeviridae, is a major causative agent of acute hepatitis in developing countries. The Norway rat HEV genome is organized into six open reading frames (ORFs), i.e., ORF1, ORF2, ORF3, ORF4, ORF5 and ORF6. The additional reading frame encoded protein ORF6 is attributed to life cycle of rat HEV. As ORF6 protein's remains to be explored in terms of its structural and functional implications, the following study was conceptualized to explore the prospective role of this additional genomic component of rat HEV. The detailed computational investigation was carried out for the ORF6 protein to elucidate its physiochemical properties, primary structure, secondary structure, tertiary structure and post-translational modifications, motif prediction and other functional characteristics. The in silico analysis revealed ORF6 protein as unstable, highly thermostable, hydrophobic and basic in nature. The amino acid compositional analysis revealed higher abundance of Leu, Arg, Ile and Pro amino acids in the polypeptide chain of ORF6 protein. The secondary structural analysis revealed all the three major elements, i.e., alpha-helices, beta-strands and coils. The generated 3D structural model of the ORF6 protein through homology modeling algorithm revealed mixed alpha/beta structural fold of the ORF6 protein with abundance of coils. Additionally, the structural models revealed the presence of clefts and a tunnel. The identified binding functions and the presence of several clefts suggested the commitment of ORF6 protein towards interaction with other ligand molecules. This theoretical study will facilitate towards deciphering the role of unexplored ORF6 encoded protein, thereby providing better understanding towards the pathogenesis of Norway rat HEVs. http://www.iaees.org/publications/journals/nb/articles/2022-12(1)/Weibull-model-altered-neuroendocrine-control.pdf S. Vijaya, A. Anandan.Network Biology,2022,12(1):26-31 An extreme value distribution, the Weibull distribution is frequently used to model reliability, wind speed, survival, and other data. One of the main reasons for this is its flexibility. Weibull distribution can mimic various distributions like the normal or exponential. The two-parameter Weibull has a shape (alpha) and scale (beta) parameter. Hence, in the present study, we investigated the effect of sumatriptan on serum GH levels of 8 younger and 8 older normally cycling women using two parameter Weibull distribution. Here, we have employed the two parameter Weibull distribution to analyse the life time data and to interpret the plot. The result clearly indicates that decreases the sumatriptan on serum GH levels of 8 younger and 8 older normally cycling women. Further, it should be noted that the Effect of sumatriptan on serum GH levels of 8 younger and 8 older normally cycling women showed the decreased levels of probability density functions and the hazard and survival function has zero, suggesting that the regular exercise welfares the life span. http://www.iaees.org/publications/journals/nb/articles/2022-12(2)/ORF5-protein-signifies-its-importance.pdf Zoya Shafat, Anwar Ahmed, Mohammad K. Parvez, Asimul Islam, Shama Parveen.Network Biology,2022,12(2):32-44 Hepatitis E virus (HEV) is the chief cause of hepatitis E (inflammation of liver) across the globe. The Norway rat HEV genome consists of six open reading frames (ORFs), i.e., ORF1, ORF2, ORF3, ORF4, ORF5 and ORF5. The additional reading frame encoded protein ORF5 protein's structure and function remain to be explored. Therefore, the presented study was conceptualized to analyze the ORF5 protein for its physiochemical properties, primary structure, secondary structure, tertiary structure and functional characteristics using bioinformatics tools. The initial analysis revealed ORF5 protein as unstable, thermostable, hydrophilic and highly basic in nature. The primary structural analysis revealed higher percentages of amino acids Arg, Leu, Pro, Ser and Gly, which suggested that the ORF5 protein is richly endowed with some regulatory amino acids (Leu, Pro and Gly). The secondary structure of ORF5 protein showed all three major components (alpha-helix, beta-strand and random coil). The tertiary structure generated through homology modelling revealed mixed alpha/beta structural fold with subsequently higher percentage of strands and abundance of coils. Moreover, the surface analysis revealed the several clefts and tunnels along with few pores, clearly suggested the ability of ORF5 protein towards interaction with other molecules. The ORF5 protein was also identified with several post-translationally modified sites including glycosylation, phosphorylation and myriystoylation. The presence of these modified sites indicated the role of ORF5 protein in regulation. Thus, our analyses taken together interpret the ORF5 protein's essentiality in HEV. This data will help in exploring the prospective role of this additional genomic component of rat HEV through the sequence, structure and functional annotation of ORF5 protein. http://www.iaees.org/publications/journals/nb/articles/2022-12(2)/special-identity-subgraph-in-genetic-code.pdf Birinchi Kumar Boruah, Tazid Ali.Network Biology,2022,12(2):45-63 The genetic code is a series of codons that stores genetic information about protein molecule formation. The identity graph of a group G is a graph in which the vertex set is the set of all elements of the group and two vertices in G are adjacent if a.b = e, where e is the group's identity element. Let H be a subgroup of G then the identity graph drawn for the subgroup H is known as the identity special subgraph of G (special identity subgraph of G). In this study, we looked at the special identity graph in the genetic code algebra. Different measures of centrality have been thoroughly discussed in our current study. Aside from this investigation, research is being conducted on the correlation coefficients between different measures of centrality, as well as the clustering coefficient, degree of distribution, and skewness. http://www.iaees.org/publications/journals/nb/articles/2022-12(2)/web-based-heart-disease-prediction-system.pdf Md. Mahbubur Rahman, Morshedur Rahman Rana, Md. Nur-A-Alam, Md. Saikat Islam Khan, Khandaker Mohammad Mohi Uddin.Network Biology,2022,12(2):64-80 Disease diagnosis is the most critical task in the medical diagnosis system. At present, the biggest challenge is to predict heart disease very quickly; for that limitation, the number of dying people is increasing day by day. If a heart disease is diagnosed quickly, we can reduce the death rate indisputably. Thus, this research produces a manual and web-based automatic prediction system that can confer a conceptual report of clear warning of patient's heart condition. The proposed prediction system predicts heart disease using some health parameters. The system uses thirteen health parameters like age, sex, chest pain type, blood pressure, ECG, etc. Eight algorithms are used separately to diagnose heart disease accurately, namely KNN, XgBoost, Logistic Regression (LR), Support Vector Machine (SVM), Ada Boost, Decision tree (DT), Naive Bayes, and Random Forest (RF). Decision Tree and Random Forest provide better performance than others among all methods. This research also established a website to easily check their heart condition from home instantly. The system has used 1026 individual patients' data for training and testing. It achieves higher accuracy in the different algorithms such as DT (99%), RF (99%), XgBoost (95%), KNN (89%), SVM (85%), LR (85%), Ada Boost (83%) and Naive Bayes (82%). The experiment result provides a target value of 0 or 1 that refers to the patient's presence or absence of heart disease. http://www.iaees.org/publications/journals/nb/articles/2022-12(3)/constrained-maximum-flow-problem-in-directed-networks.pdf Muhammad Tlas.Network Biology,2022,12(3):81-96 In this paper, the binary representation of arc capacity has been used in developing an efficient polynomial time algorithm for the constrained maximum flow problem in directed networks. The algorithm is basically based on solving the maximum flow problem as a sequence of O(n2) shortest path problems on residual directed networks with n nodes generated during iterations. The complexity of the algorithm is estimated to be no more than O(n2mr) arithmetic operations, where m denotes the number of arcs in the network, and r is the smallest integer greater than or equal to log B (B denotes the largest arc capacity in the directed network). Generalization of the algorithm has been also performed in order to solve the maximum flow problem in a directed network subject to non-negative lower bound on the flow vector. A formulation of the simple transportation problem, as a maximal network flow problem has been also performed. Numerical example has been inserted to illustrate the use of the proposed algorithm. http://www.iaees.org/publications/journals/nb/articles/2022-12(3)/confidence-intervals-fallacies-criticisms-solutions.pdf WenJun Zhang.Network Biology,2022,12(3):97-115 For a long time, confidence interval theory is the basis of statistics, and confidence interval has been regarded as an important content of statistical analysis. Almost all statistical textbooks and statistical analysis software contain the contents of confidence intervals, which are used to estimate statistical parameters or parameters of mathematical models, and are an important part of many methods such as interval estimation, analysis of variance, and regression analysis, etc. They are recommended or required by the method guidelines of many reputable journals. So far, confidence interval theory and methods have been widely used in various scientific or engineering fields including life sciences, medicine, environmental science, chemistry, physics, and psychology. However, due to the fallacies or deficiencies of the confidence interval theory and methodology, it has caused a wide range of misuses, and has been criticized more and more in recent years. Some statisticians even suggest abandoning the confidence interval theory. To avoid the problems of classical confidence interval theory, one can use Bayesian credible intervals, use uncertainty methods, calculate confidence intervals by avoiding statistic significance tests, or use the Bootstrap credible interval method proposed by me, etc. In practice, for controlled experiments, multiple replicates or treatments should be designed; for observational experiments, multiple representative samples should be drawn, and even a single sample can be used if sufficient sample size is ensured. It is necessary to implement the whole process control for every procedures from sampling to statistical analysis. Cross-comparison and validation of confidence interval analysis results with other multi-source results should be conducted to obtain the most reliable conclusions. Finally, in addition to writing, publishing and adopting new statistical works and teaching materials as soon as possible, it is imperative to revise and distribute various statistical software in new editions based on new statistics for use. http://www.iaees.org/publications/journals/nb/articles/2022-12(3)/WRKYs-intrinsic-disorder-and-interactions.pdf Mouna Choura, Faical Brini.Network Biology,2022,12(3):116-119 The WRKY transcription factors are involved in a range of biological processes in plants, including the response to biotic and abiotic stresses and plant immunity. There is also evidence that intrinsic disorder proteins (IDPs) are involved in key cellular functions such as regulating biological processes. Here, the intrinsic disorder distribution of WRKYs within T. turgidium and its protein-protein interactions are investigated. The analysis showed that the hub proteins have higher level of disorder content in T. turgidium. In comparison to other cereals, it is shown the intrinsic features in T. turgidium are evolutionary conserved, which may explain the multifunctionality of WRKYs in plants. http://www.iaees.org/publications/journals/nb/articles/2022-12(3)/periodicity-and-stochastic-on-COVID-19-pandemic.pdf Kalyan Das, Ranjith Kumar, Prasenjit Das.Network Biology,2022,12(3):120-132 We analyzed the features of the COVID-19 outbreak with temporal delay and stochastic influence using the SIRS epidemic model in this study. We investigate the local stability of each equilibrium point in terms of basic reproduction numbers. Hopf bifurcation is detected in the system, and a time delay is inserted in the transmission terms to represent the virus's incubation period. The spread of the novel COVID-19 strain to humans is influenced by environmental conditions such as mugginess, precipitation, and temperature. To explore the impact of environmental oscillations on the coronavirus, we employ white noise perturbations in the system. Finally, we examine the mathematical reenactments using MATLAB. http://www.iaees.org/publications/journals/nb/articles/2022-12(4)/protein-sequence-network-based-on-2D-representation.pdf Sanjay Sharma, Birinchi Kumar Boruah, Tazid Ali.Network Biology,2022,12(4):133-141 For the comparative analysis of proteins, their proper clustering, and evolutionary relationships require analysis of their sequences. We used a mathematical parameter termed a similar factor to create a similar degree matrix of ND6 protein sequences taken from eight different species in this paper. We built a network out of the matrix to analyze their evolutionary and similarity trends with each other. By observing the various centrality measures, the correlation between multiple centrality measures and different network parameter shows that our network is consistent with the known evolution fact of ND6 protein sequences. http://www.iaees.org/publications/journals/nb/articles/2022-12(4)/chaotic-dynamics-of-a-fractional-order-glycolysis-model.pdf Md. Jasim Uddin, S. M. Sohel Rana.Network Biology,2022,12(4):142-159 Glycolysis model has been considered by Caputo fractional derivative. We give the topological classifications of fixed points of this model. Then, we show analytically that under certain parametric conditions fractional order glycolysis model underlies a Neimark-Sacker (NS) bifurcation and Flip bifurctaion. By using central manifold and bifurcation theory, we confirm the existence and direction of both NS and Flip bifurcations. To reinforce our analytical findings, we perform numerical simulations that include bifurcations, phase portraits, periodic orbits, invariant closed cycles, abrupt emergence of chaos and abrupt elimination of chaos. At the end, OGY method is applied to eliminate chaotic trajectories of the system. http://www.iaees.org/publications/journals/nb/articles/2022-12(4)/influence-maximization-in-social-networks.pdf Mansoureh Abolghasemi, Esmaeil Bagheri.Network Biology,2022,12(4):160-175 With the emergence of social networks, human relationships on the internet have become a new form. Social networks are not only a communication tool for users, but can also be a basis for marketing and advertising products of different companies. Studying the impact of maximum penetration has attracted many researchers in recent years due to the benefits of viral marketing. Given a social network, the goal is to find a subset of K individuals as influential nodes that can generate maximum cascading influence through the network under a predefined diffusion model. The first research in this field did not work for large networks. After this effort, different methods were presented to maximize influence, among them, methods based on communities were proposed. Algorithms for maximizing community influence often use the influence of a node in its own community to approximate its influence in the entire network, so they can perform better. One of these community-based algorithms is the COFIM algorithm. In this paper, the efficiency of the COFIM algorithm, which is a community-based influence maximization method, is improved by distributing seed nodes through the community structure. The results of the proposed algorithm have been tested on six different data sets and then compared with the basic methods. The test results show the efficiency of the proposed method. http://www.iaees.org/publications/journals/nb/articles/2023-13(1)/predict-heart-disease-using-machine-learning-classifiers.pdf Aparna Chaurasia,Vikas Chaurasia.Network Biology,2023,13(1):1-16 The exact forecast of heart disease is necessary to proficiently treat cardiovascular patients before a heart failure happens. Assuming we talk about AI techniques can be accomplished utilizing an ideal AI model with rich medical services information on heart diseases. To begin with, the feature extraction technique, gradient boosting-based sequential feature selection (GBSFS) is applied to separate the significant number of features (5, 7, 9, and 11) from coronary illness dataset to create important medical services information. The stacking model is prepared for coronary illness forecast. A comparison model is made between datasets with prominent features (5, 7, 9, and 11) as well as all features. The proposed framework is assessed with coronary illness information and contrasted and customary classifiers in view of feature elimination include determination strategies. The proposed framework acquires test accuracy of 98.78%, which is most noteworthy in marking model with 11-featuers and higher than existing frameworks. This outcome shows that our framework is more powerful for the expectation of coronary illness, in contrast with other cutting edge strategies. http://www.iaees.org/publications/journals/nb/articles/2023-13(1)/algebraic-structures-and-distance-analysis-of-genetic-code.pdf Chandra Borah, Tazid Ali.Network Biology,2023,13(1):27-36 This paper explores the genetic code's algebraic structures associated with the four mRNA (or DNA) bases A, G, C, and U. We have obtained quotient group structures of codons by considering the transition and substitution mutation. In these quotient group structures, cosets (codon members) explain intriguing interactions between the algebraic properties of codons and the physico-chemical properties (polarity, hydrophilicity, and hydrophobicity) of amino acids. Considering the evolutionary impacts of base locations in a codon, the base's hydrogen bond number, and the base's chemical form distinctions, we have generated a distance-based amino acids matrix. This matrix exhibits a fascinating association between distance measurements and amino acids' physico-chemical aspects. Also, we have obtained multiple amino acid graphs relating to this distance-giving matrix, which explores the evolutionary organization of amino acids. http://www.iaees.org/publications/journals/nb/articles/2023-13(2)/machine-learning-approach-to-predict-autism-spectrum-disorder.pdf Khandaker Mohammad Mohi Uddin, Hasibur Rahman, Mahadi Hasan, Fatema Akter, Suman Chandra Das.Network Biology,2023,13(2):37-52 A central nervous system known as an Autism Spectrum Disorder (ASD) has long-term effects on a person's capacity for engagement and interaction with others. Since its symptoms often manifest in the first two years of life, autism is considered to be a behavioral condition that can be identified at any point in a person's life. This study investigated the potentiality of machine learning techniques such as Logistic Regression, Random Forest, Multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB) to predict ASD using some health parameters. There are 292 instances and 21 attributes in the first dataset linked to the screening for ASD in children. The adult individuals in the second dataset had a total of 704 occurrences and 21 characteristics related to ASD detection. In order to achieve the highest accuracy possible from the machine learning models, feature optimization is used in this study along with other preprocessing approaches. The findings overwhelmingly support the notion that Random Forest performs better on all of these datasets, with the greatest accuracy (100%) for data on Autistic Spectrum Disorder (ASD) in children and adults, respectively. http://www.iaees.org/publications/journals/nb/articles/2023-13(2)/information-system-of-acupoint-diagnosis-and-treatment.pdf WenJun Zhang, YanHong Qi.Network Biology,2023,13(2):53-73 In this study we developed an information system of acupoint diagnosis and treatment for Traditional Chinese Medicine (ISADTTHM). In this system, there are 311 acupoints, about 600 acupoints for treating or preventing diseases, 12 meridians, 8 extra meridians, and 5 elements of the Five Elements. In the ISADTTHM, users can choose to list the complete collection of ISADTTHM, or can choose to list concerned information using searching keywords such as acupoint indications and clinical applications, acupoint names, meridians, extra meridians, and Five Elements (a total of more than 900 keywords) to query about the information of acupoint diagnosis and treatment. Each item information of acupoint diagnosis and treatment includes the following contents: acupoint name, Pinyin of acupoint name, international code of acupoint, alias of acupoint, acupoint definition, acupoint positioning method, acupoint anatomical position and structure, Five Elements affiliation of acupoint, meridian affiliation of acupoint, English name of meridian affiliation of acupoint, Five Elements affiliation of meridian affiliation of acupoint, acupoint treatment methods - acupuncture, acupoint treatment methods - moxibustion, acupoint treatment methods - massage and others, acupoint functions, acupoint indications and clinical applications, main compatible acupoints of acupoint, and acupoint pictures. Different from information systems based on static webpages, ISADTTHM is based on web database, with only about five files, small storage space, easy to upgrade, maintain, and update, and low error rate, which is conducive to information mining and analysis, and user inquiries to the information of interest. At the bottom left of each page of ISADTTHM, there is Google Translate, which can translate the Chinese of the current page into the selected language. http://www.iaees.org/publications/journals/nb/articles/2023-13(3)/acupoint-network-with-functional-similarity-in-TCM.pdf WenJun Zhang, YanHong Qi.Network Biology,2023,13(3):74-83 In this study, we constructed the data table of acupoint functions (i.e., acupoint indications and clinical applications) based on the previously developed information system of acupoint diagnosis and treatment in Traditional Chinese Medicine. Based on the data table of acupoint functions, between-acupoint point correlations were calculated. Acupoints with statistically significant point correlations were linked to construct the acupoint network with functional similarity. The degree centrality values of acupoints (totally 311 acupoints) showed that the acupoints Zhengying (GB17), Qinglengyuan (SJ11), Jiaji (EX-B2), etc. have the highest degree centrality values. Most of these acupoints are located on the head and neck, and their functions are universal. The acupoints Yinbai (SP1) and Zhongkui (EX-UE4) have strong specific functions and weak substitutability. The results of frequency distribution, skewness, aggregation index, coefficient of variation, entropy, and probability distributions showed that the obtained acupoint network is a scale-free complex network. Among them, the probability density function of the power law distribution is p(x)=x^(-6.1818), x not less than 36. http://www.iaees.org/publications/journals/nb/articles/2023-13(3)/graphical-representation-of-genetic-code-algebra.pdf Birinchi Kumar Boruah, Tazid Ali.Network Biology,2023,13(3):84-97 During the translation process, the genetic code is the nucleotide sequence that determines the amino acid sequence of protein molecules. A codon is a universal triplet of nucleotides that codes for an amino acid. A group G's identity graph is a graph in which the vertex set is the set of all group elements and two vertices in G are adjacent if a.b = e, where e is the group's identity element. In this study, we looked at the identity graph in the genetic code algebra. In our current study, we have thoroughly discussed various measures of centrality. In addition to this analysis, the correlation coefficients between various centrality measures, clustering coefficient, degree of distribution, and skewness are all examined. http://www.iaees.org/publications/journals/nb/articles/2023-13(4)/pattern-classification-of-acupoints.pdf WenJun Zhang, GuangHua Liu.Network Biology,2023,13(4):98-121 Based on previously constructed data tables of acupoint functions and meridians, we performed a pattern classification of acupoints based on functional similarity in this study. Unsupervised pattern classification methods include k-means clustering, hierarchical clustering, BP neural network, self-organizing feature map network; supervised pattern classification methods include BP neural network, and LVQ network. Supervised pattern classification helps to determine the correspondence between acupoint functions and meridians, and to further explore the meridian properties of acupoints. Among them, hierarchical clustering gives two categories of pattern classifications, that is, fixed number classes of pattern classification and cluster tree, and other methods give pattern classification of fixed number classes. The cluster tree of acupoints reflects hierarchical relationship of functional similarity between acupoints, which can determine the functional relationship between acupoints at different levels. In unsupervised pattern classification, k-means clustering gave the most reasonable pattern classification, followed by BP neural network, and the results of hierarchical clustering and self-organizing feature map network were poor. In supervised pattern classification, LVQ network is superior to BP neural network, and self-organizing feature map network is poor. Compared with 15 categories of meridians, the LVQ network divided 13 classes of acupoints, the BP neural network divided 6 classes of acupoints, and the self-organizing feature mapping network divided 83 classes of acupoints. The pattern classification results obtained have guiding significance for selection, collocation, diagnosis and treatment of acupoints, and can be used for further in-depth mining and analysis. http://www.iaees.org/publications/journals/nb/articles/2024-14(1)/executable-Java-software-for-visualizing-networks.pdf WenJun Zhang.Network Biology,2024,14(1):1-11 An executable Java software, netGen, for visualizing networks was developed in present study. An interactive network can be visualized from the specified text or csv data file by using netGen. netGen is useful to visualize both undirected and directed networks. Both netGen and demonstration data files are given. http://www.iaees.org/publications/journals/nb/articles/2024-14(1)/Matlab-software-for-visualizing-interactive-networks.pdf WenJun Zhang.Network Biology,2024,14(1):13-19 Based on the previous web tool, I developed a Matlab software, netVisual, for generating a HTML file from which the user-interface interactive network can be visualized in the web browsers. In the network, the user can mouse-press any node to drag the network, to examine network topology, and to evaluate node centrality, etc. It can be freely used and run on popular web browsers as Chrome, etc. Both netVisual and demonstration data files are given. http://www.iaees.org/publications/journals/nb/articles/2024-14(1)/human-disordered-charged-biased-proteins.pdf Mouna Choura.Network Biology,2024,14(1):20-24 The human disordered charged biased proteins (HDCBPs) are involved in complex diseases. The HDCBP-disease network constructed in our earlier showed that HDCBPs share many diseases. Therefore, they are attractive therapeutic targets for drug discovery. In this study, we explore the associations between (HDCBPs), the related diseases, and the drugs. The results show that 20% and 14% of HDCBPs are listed in DRUGBANK and ChemDB respectively. The elaborated HDCBP-Drug-Disease network revealed that most of the therapeutic area indications included cancer, neoplasm, lymphoma, cardiovascular, respiratory and skin diseases. The constructed HDCBP-Drug-Disease network may improve our understanding of complex diseases and related drugs. Moreover, such a network could suggest opportunities of drug repurposing for which efficacy should be investigated in functional validation studies. http://www.iaees.org/publications/journals/nb/articles/2024-14(1)/Open-Reading-Frame-4-protein-as-potential-drug-target.pdf Zoya Shafat, Shama Parveen.Network Biology,2024,14(1):25-37 Hepatitis E virus (HEV) is the main cause of acute hepatitis worldwide. The viral infection caused by G1 HEV in pregnant women has become a major health concern in the past few years. The mechanism underlying the pathogenesis of viral infection in HEV G1 isolates is attributed to four different open-reading frames (ORFs) i.e., ORF1, ORF2, ORF3 and ORF4. The present analysis has considered ORF4 protein as the molecular target due to its intrinsic disorder propensity. Intrinsically disordered regions (IDRs) are regions in proteins that do not possess stable secondary and tertiary structure and are prevalent in eukaryotes. IDRs are found to be closely associated with numerous human diseases, for instance, Parkinson and Alzheimer disease. The extreme flexibility and random coiled conformations of IDR allow it to undergo protein-protein interaction (PPI). The 3-dimensional (3D) structures of the target protein were designed using homology modelling algorithms. The generated models were assessed through structure verification tool PROCHECK. In this paper, we provide an overview of ORF4 protein structure-function relationship and its involvement in several biological processes through PPIs. Our results suggest that ORF4 protein has the potential to act as drug molecule, thus can accelerate the process of drug designing strategies against HEV. http://www.iaees.org/publications/journals/nb/articles/2024-14(2)/molVisual3D-software-for-3D-visualization-of-molecules.pdf WenJun Zhang.Network Biology,2024,14(2):38-68 In this article, a standalone executable software molVisual3D, for 3D visualization of molecules was developed. It uses the XYZ file of a molecule to generate its 3D graphics. Some parameters can be specified by the user. In the generated 3D graphics window, the user can right- or left-click mouse to zoom in or zoom out the 3D graphics, or mouse-drag the graphics to rotate the 3D graphics, or slide scrollbar to vertically or horizontally translate the 3D graphics. The 3D graphics can be saved into an image file (in BMP format). Both molVisual3D and demonstration data files were given. http://www.iaees.org/publications/journals/nb/articles/2024-14(2)/trends-of-WRKYs-transcription-factors.pdf Mouna Choura.Network Biology,2024,14(2):69-76 WRKY transcription factors play important roles in plant growth, development, and stress responses. WRKYs have been intensively studied in plants providing valuable information. However, limited data were for the evaluation of the trend of WRKYs researches. This study aims to investigate the trend of WRKY researches from 2010 to 2021 including the latest findings, major contributors, institutions, and journals. Here, 2302 publications were retrieved for analysis. They have been published in 361 journals. Over 1789 organisations have contributed to WRKY publications, with China and United States of America in the first place. The trend of publications is increasing year by year. Plant Sciences journals ranked top for publishing papers. The Chinese authors and institutions were the most productive in the WRKY research. The WRKYs are most studied in different plants such as Arabidopsis in the context of biotic and abiotic stresses. This study provides a comprehensive overview and valuable references allowing researchers to identify cooperation, find research hotspots, and provide intuitive profile for the contribution in this field. http://www.iaees.org/publications/journals/nb/articles/2024-14(2)/literature-mining-of-angiotensin-converting-enzyme-2.pdf Neelam Krishna, Shivani Tyagi, Pramod Katara.Network Biology,2024,14(2):77-88 COVID-19, caused by zoonotic coronavirus SARS-CoV-2, is not a first coronavirus infection, prior to this, two severe coronavirus infections were already faced by the humans at different parts of the world. COVID-19 is found to be more severe than its previous counterparts and cause respiratory syndrome along with some other pathophysiology effects. The main human protein which used by SARS causing coronavirus (SARS-CoV and SARS-CoV-2) is angiotensin-converting enzyme 2 (ACE2), a key member and regulator of RAS. Coronavirus shows a significant affinity with the ACE2, spike protein of the virus participate in this crucial interaction and initiate the infection cycle of the SARS. This ACE2 plays a very significant role in RAS, which directly affect the pathophysiology of humans, mainly of respiratory and cardiovascular diseases. Blockage or down-regulation of ACE2 can easily block the virus entry in the cells, but due to the other important role of the ACE2, the human system cannot afford its suppression or blockage. Due to its importance, it is required to understand the physiology and pathophysiological role of the ACE2, which can help to develop therapy against the SARS. This report provides a detailed account of ACE2, and help to understand about it, which will help to plan a possible way to fight against SARS-CoV-2 and other coronaviruses. http://www.iaees.org/publications/journals/nb/articles/2024-14(2)/Ayurvedic-medicines.pdf Suvarna Ingale, Tejal Bele, Pramod Ingale.Network Biology,2023,14(2):89-99 Ayurveda, an ancient holistic healing system originating from India emphasizes balance and harmony between body, mind, and spirit. Ayurvedic medicines offer diverse therapeutic claims, spanning preventive care, disease treatment, and overall well-being. This traditional system significantly influences global healthcare and complements modern medicine. However, scientific validation of Ayurvedic claims remains a challenge. Network pharmacology, an interdisciplinary approach, has emerged as a promising tool for this purpose. Network pharmacology explores complex interactions in biological systems. It aligns well with Ayurveda's holistic philosophy and offers potential in validating Ayurvedic medicines. This review covers successful case studies of justification of Ayurvedic claims using network pharmacology approach. We further outlined key steps for such validation, including literature review, compound identification, data collection, network construction, and enrichment analysis. The approach aligns traditional knowledge with modern scientific findings, enhancing the credibility of both. The integration of Ayurvedic principles with network pharmacology can shed light on the mechanisms of action underlying Ayurvedic medicines. In conclusion, network pharmacology holds immense potential in validating the traditional claims of Ayurvedic medicines. It provides a framework for understanding complex interactions, confirming traditional knowledge, and potentially unlocking novel therapeutic mechanisms. Collaboration between traditional practitioners and scientists, along with advancements in data integration and analysis, can drive this integration, enriching global healthcare with Ayurvedic insights. http://www.iaees.org/publications/journals/nb/articles/2024-14(2)/SampSizeCal-computational-tool-for-sample-sizes.pdf WenJun Zhang.Network Biology,2024,14(2):100-155 Dependent upon the statistical significance p-value and statistical power, the sample size estimation is widely used in various experimental sciences. Nevertheless, the p-value based paradigm, which has resulted in numerous fake conclusions that originate partly from insufficient sample sizes, has been widely criticized in recent years for serious problems. Therefore, I developed a platform-independent computational tool, SampSizeCal, for sample sizes in the paradigm of new statistics. In this tool, both default p-values and the maximum p-values were greatly enhanced, which will lead to the reasonable increase of sample sizes. The computational tool harbors more than 120 sample size methods for experimental designs. SampSizeCal includes both online and offline versions, and can be used for various computing devices (PCs, iPads, smartphones, etc.), operating systems (Windows, Mac, Android, Harmony, etc.) and web browsers (Chrome, Firefox, Sougo, 360, etc). It is currently the most comprehensive platform-independent computational tool for sample sizes, and can be used in experimental sciences such as medicine (clinical medicine, experimental zoology, public health, pharmacy, etc.), biology, ecology, agronomy, psychology and engineering technology. http://www.iaees.org/publications/journals/nb/articles/2024-14(2)/centrality-based-analysis-of-amino-acids-network.pdf Chandra Borah, Tazid Ali.Network Biology,2024,14(2):156-173 A network is a crucial asset in biology for capturing and exploring interaction data in biological systems of many types, such as protein-protein communications, amino acid associations, gene regulation, and cellular metabolism. In this article, we constructed an amino acid distance matrix by considering each base's positional relevance in a codon, chemical types: Purine and Pyrimidine, and H-bonding count. Based on the amino acid distance matrix, we eventually generated a twenty amino acid network having evolutionary significance. We reviewed multiple centrality metrics to assess the relative importance of amino acids in the proposed network: Degree Centrality, Closeness Centrality, Betweenness Centrality, Eigenvector Centrality, Eccentricity Centrality, and Radiality Centrality. We also looked at the correlation coefficients between the different centrality measures to figure out whether the network is assortative or disassortative. Furthermore, we examined the Clustering Coefficient and Degree Distribution as two effective network measures, and the results seem noteworthy. http://www.iaees.org/publications/journals/nb/articles/2024-14(2)/dynamics-of-a-prey-predator-system.pdf K. Venkataiah, K. Ramesh.Network Biology,2024,14(2):174-186 Capturing complicated dynamics and understanding the underlying controlling ecological processes is one of the major ecological issues. The Allee effect is an essential component in ecology, and considering it can have a substantial impact on system dynamics. In the present investigation, we analysed a prey-predator scenario in which the predator is a generalist since it feeds on prey populations and the Allee phenomenon impacts the prey population's growth. The influence of the Allee effect on the changing nature of the system is investigated. The stability and boundedness of the model's equilibria are extensively investigated. We found that including the Allee effect enhances the system's local and global behaviours through a detailed bifurcation analysis. The chaotic nature of the system is strongly impacted by the Allee effect, particularly once a specific threshold value is reached. In the study of bifurcation analysis, we looked into bifurcations such the presence of transcritical bifurcation and Hopf-bifurcation to chaos. We added stochastic perturbation to this problem by including random fluctuations in the sensitive parameters. Finally, we analysed the system's mean-square stochastic stability towards the internal equilibrium. As a result, it is discovered that the Allee effect and stochastic perturbation considerably influence the behaviour of the prey-predator system. http://www.iaees.org/publications/journals/nb/articles/2024-14(2)/computational-tool-for-meta-analysis.pdf WenJun Zhang.Network Biology,2024,14(2):187-214 Meta-analysis is a statistical method used in systematic review to quantitatively integrate the results of multiple related studies to obtain a pooled result that can represent these studies. Meta-analysis overcomes the limitations of traditional reviews that only conduct qualitative research. In present study, I developed a platform-independent computational tool for meta-analysis. It is a comprehensive tool consisting of a full set of meta-analysis methodology, including the methods for fixed-effects model and random-effects model and the methods for heterogenetity testing in which the effect size tests based heterogeneity testing were proposed. Effect size tests of difference significance for post meta-analysis were also presented. The computational tool is a web browser based meta-analyzer that includes both online and offline versions and can be used on various computing devices (PCs, iPads, smartphones, etc.), operating systems (Windows, Mac, Android, Harmony, etc.) and web browsers (Chrome, Firefox, etc). It can be used in various sciences as medicine, biology, ecology, psychology, sociology, economy, physics and chemistry etc. http://www.iaees.org/publications/journals/nb/articles/2024-14(3)/stability-analysis-of-predator-prey-model.pdf Kaladhar Kolla, Khushbu Singh.Network Biology,2024,14(3):215-227 The current study is based on a predator-prey model with infection that affects only predator species. Predators are divided into two categories such as the susceptible predator and the infected predator, which are feeding on prey species. The Takagi-Sugeno (T-S) based fuzzy impulsive control model was used to explore the stability of the Lotka-Volterra predator-prey system. Numerical simulation provides global stability and the fuzzy solution. http://www.iaees.org/publications/journals/nb/articles/2024-14( P. Sireeshadevi, G. Ranjith Kumar.Network Biology,2024,14(3):228-241 In the present investigation, we examine the consequences of a predator-prey model that includes a constant harvesting technique in a population of susceptible prey. A particular kind of flipping functional approach is present in our proposed prey-predator system; in this response, the predator consumes on susceptible and sick prey, however it shifts its attention to a new sort of prey when its supply of that kind of prey decreases. By employing boundedness, positivity, equilibrium analysis and stability analysis, the essential mathematical characteristics of the model are explored. Attention is directed on the prey refuge in further explorations of the Hopf bifurcation close to the coexistence equilibrium point. This paper's unique contribution is that it examines the dynamics of predator-prey systems from an eco-epidemiological perspective while simultaneously considering the impacts of prey refuge and constant-rate harvesting. To ascertain the critical values of the bifurcation parameters, if present, and to validate the primary findings, numerical simulations are executed. Our numerical simulations reveal that the presence of prey refuge increases the severity of sickness, which is how the three-species eco-epidemiological system creates chaos. However, we find that both the prey refuge and harvesting can manage the resulting chaotic dynamics. http://www.iaees.org/publications/journals/nb/articles/2024-14(3)/amino-acids-network-based-on-graph-mining.pdf Nasrin Irshad Hussain, Kuntala Boruah.Network Biology,2024,14(3):242-253 Applications of graph mining have proliferated across the research spectrum in recent years. Mining data to retrieve information is a big deal as data are unstructured and huge in size, volume and in different data-types as internet is available to everyone and anywhere. Therefore data is so rapidly increased and for that point this mining concept came. In graph mining, analysis of graph base data is considered. In different research fields use graph base mining as it give quick and efficient result of large datasets. Here we consider biological data which are very complex to describe and analyse to extract useful information, so now researchers use computational tools to mine the large datasets, graphs are the most efficiently used. We consider amino acid network to do graph mining and extract some useful patterns from the network. Amino Acid Networks (AANs) are undirected graphs where amino acids are act like vertices and their relationships connect two vertices in protein structures. Every amino acid exhibits different physico-chemical properties. The shift in R groups affects various characteristics of the amino acids. The shift in R groups affects the various characteristics of the amino acids. In this paper we have construct a graph of amino acids based on property similarity and discussed different measures of centrality. We have also investigated the correlation coefficients between different measures of centrality. http://www.iaees.org/publications/journals/nb/articles/2024-14(4)/network-based-investigation.pdf Tejal Bele, Suvarna Ingale.Network Biology,2024,14(4):254-265 Neurological illnesses such as Alzheimer's disease (AD), Parkinson's disease (PD), and epilepsy (EP) have a significant impact on worldwide health. This study uses network pharmacology and genomic analysis to find shared genes and pathways linked to various illnesses.The STRING database was used to identify shared genes between AD, PD, and EP. Associated proteins of common genes were obtained and imported into Cytoscape to design and analyze networks. Gene enrichment analysis was performed using ShinyGO V0.77. AD, PD, and EP share three genes: KIF5A, NDUFB9, and MT-ND1. Network analysis showed relationships between these genes and their associated proteins. Pathway enrichment study revealed major pathways, including Alzheimer's, Parkinson's disease, Neurodegeneration, and oxidative phosphorylation pathways. The current study revealed genetic interconnectivity of AD, PD, and EP, underlining the role of mitochondrial failure, oxidative stress, and synaptic dysfunction in their development. KIF5A, NDUFB9, and MT-ND1 play critical roles in these pathways, making them attractive therapeutic targets. Indirect interactions between these genes via common proteins such as SNCA and MAPT indicate complicated regulatory networks. Identifying common genes and pathways sheds light on shared mechanisms underlying AD, PD, and EP. Drug repurposing opportunities targeting key proteins like SNCA and MAPT may offer novel therapeutic avenues. http://www.iaees.org/publications/journals/nb/articles/2024-14(4)/most-potent-bioactive-natural-compound.pdf Sana Begum, Vishal K. Singh, Priyanka Kumari, Anup Som.Network Biology,2024,14(4):266-292 Emergence of COVID-19 and thereafter intensive research on bioactive natural compounds against SARS-CoV-2, identified a large number of phytochemicals (i.e., plants-derived) and mycochemicals (i.e., fungi-derived) as potential inhibitors with proven antiviral properties against SARS-CoV-2, but there are no comparative study on the reported compounds. A comparative study among the previously identified/reported main protease (Mpro) inhibitors of SARS-CoV-2 can lead to the most potent compound that eventually helps to make an effective drug lead against SARS-CoV-2. Through manual literature curation, we selected 57 potential bioactive compounds and screened them against Mpro protein of SARS-CoV-2. A series of in silico screening such as binding affinity, drug-like properties, pharmacokinetic, physicochemical, and ADMET studies identified top ten compounds as potential Mpro inhibitors. Further, docking studies prioritized the top two compounds namely Norquinadoline A and Quinadoline B, based on their predicted affinity for the target protein. Binding free energy calculations further emphasized them as top candidates for effective Mpro inhibitors that hold promise for drug development against COVID-19. In-depth molecular dynamics studies and MM/PBSA analysis culminated in the recognition of Norquinadoline A as the most potent Mpro inhibitor of SARS-CoV-2. Thus, Norquinadoline A can be used as lead compound in further drug discovery process after in vitro and in vivo experimental studies. http://www.iaees.org/publications/journals/nb/articles/2024-14(4)/interaction-profiling-of-cow-milk-metabolites.pdf Neelam Krishna, Shraddha Vishwakarma, Pramod Katara.Network Biology,2024,14(4):293-304 To maintain healthy human physiology and promote growth and development, it is imperative to consume milk, which provides essential nutrients like vitamins and minerals. However, cow milk compounds contain different types of molecules, which may elicit varied responses within individuals. Milk metabolites are studied to impact several human biological processes that result in altered physiology. The Renin-Angiotensin System (RAS) is responsible for regulating blood pressure and maintaining a proper balance of fluids and electrolytes. However, impaired regulation of RAS may cause medical conditions such as heart failure, kidney disease, or hypertension. RAS is one of the studied systems, whose proteins reportedly interacted with and were affected by milk metabolites. The study attempts to find milk metabolites with high affinity towards RAS-proteins, and results from circumstances of interaction between them. Molecular docking between milk metabolites and RAS-proteins' and an interaction network was utilized to achieve the objective. In total 206 milk metabolites and 13 Ras proteins are considered for the study. Network analysis depends on the docking score, which helps us understand the interaction between milk molecules. Based on free energy analysis study indicates that out of 206, 35 milk metabolites showed free energy less than -8 Kcal/mol, which indicates high binding affinity between these metabolites with 12 RAS-proteins. Four RAS proteins, i.e., ANPEP, CTSA, MRGPRD, and ACE, were found to have significantly interacted with more than 15 milk metabolites. Based on binding affinity, we can predict whether the specific metabolites with effective binding scores modulate the function of specific RAS proteins. http://www.iaees.org/publications/journals/nb/articles/2025-15(1)/amino-acid-network-based-on-bond-energy.pdf Nasrin Irshad Hussain, Kuntala Boruah, Adil Akhtar.Network Biology,2025,15(1):1-14 Amino acids are building blocks of proteins which are essential for all biological functions. Each amino acid has different physico-chemical characteristics. The interconnection between one amino acid with other amino acids in surrounding environment creates a network which is very complex in nature. Graph mining is most efficient and effective methods for analyzing various complex datasets. However, the knowledge discovery from those heterogeneous datasets is a nontrivial task. The computational method automate the prediction of patterns and less expensive than experimental methods in terms of resources and time. This paper presents amino acid network based on bond energy where different graph mining techniques are used to extract information from the network. Further, different centrality measures are calculated to analyze and determine the amino acids relative importance in this network. Also we have examined the clustering coefficient, correlation coefficient among different centrality measures, eccentricity and center of the network. Our results reveal significant insights into the evolutionary roles of amino acids, highlighting the centrality of hydrophilic amino acids and the unique positions of specific residues like Tryptophan and Glycine. These findings provide a deeper understanding of amino acid interactions and their evolutionary implications, demonstrating the effectiveness of computational methods in biological research. http://www.iaees.org/publications/journals/nb/articles/2025-15(1)/signed-graph-in-amino-acid-network.pdf Adil Akhtar, Nasrin Irshad Hussain.Network Biology,2025,15(1):15-23 This article is aimed to construct a model related to protein structure and amino acids viz., Brandstein and Tooze's condition. To study Brandstein and Tooze's condition we have considered signed graph in amino acids. For this we have constructed a network structure, where the link is based on the one point mutation of codons of corresponding 20 different essential amino acids. To study this we have considered signed graph in amino acids and consider two cases. In the first case positive sign is assigned to an edge if both the amino acids are either hydrophobic or hydrophilic, otherwise negative sign is assigned. And in the second case positive sign is assigned to an edge if one amino acid is hydrophobic and the other is hydrophilic, otherwise negative sign is assigned. http://www.iaees.org/publications/journals/nb/articles/2025-15(2)/Mendelian-randomization-Principles-and-methods.pdf WenJun Zhang.Network Biology,2025,15(2):24-47 Mendelian randomization (MR) is a methodology for evaluating causality in observational studies. MR tries to find the fact that genotypes are not susceptible to reverse causation and confounding based on Mendel's law of inheritance. MR may provide information on causality in situations where randomized controlled trials are impossible. In present article, the principles and methods of MR were fully discussed. http://www.iaees.org/publications/journals/nb/articles/2025-15(2)/a-bit-arc-capacity-scaling-algorithm.pdf Muhammad Tlas.Network Biology,2025,15(2):48-66 A bit-arc capacity scaling algorithm to solve the maximal flow problem subjected to box constraints on the flow vector in directed network has been presented. The algorithm is mainly based on successive divisions of capacities by multiples of two. It solves the maximal flow problem as a sequence of O(n2) Dijkstra's shortest path between two nodes in the defined residual network with n nodes and m arcs. It is proven that, the algorithm's complexity was estimated to be no more than O(n2mr) arithmetic operations in the worst case to reach the maximum vector flow through the directed network. Where r denotes to the smallest integer greater than or equal to log B, and B denotes to the largest arc capacity of the network. A numerical example has been illustrated using the proposed algorithm. http://www.iaees.org/publications/journals/nb/articles/2025-15(3)/analysis-of-Z64-genetic-code-network.pdf Adil Akhtar, Nasrin Irshad Hussain.Network Biology,2025,15(3):67-74 The genetic code is the set of rules defining how the sequence of nucleotides in DNA or RNA determines the specific amino acid sequence in the synthesis of protein. Proteins are the basic functional elements of living organisms and amino acids are the building blocks of proteins. Each protein is formed by a linear chain of amino acids. The DNA consists of two complementary long chains of nucleotides, viz. Adenine (A), Cytosine (C), Guanine (G) or Thymine (T) (Uracil (U) in case of RNA). Three consecutive DNA nucleotides form a codon. Each codon specifies a particular amino acid. The set of 64 codons can be equipped with a ring structure isomorphic to the ring of integers modulo 64 (Z64). Different graph structures can be generated from a ring. In this paper we have discussed total graph in this ring. Total graph of a ring R is an undirected graph where vertex set is the set of all elements of the ring and for distinct x, y belong to R, the vertices x and y are adjacent if x+y belongs to Z(R), where Z(R) be the set of zero divisor of R. In this manuscript using signed graph we have try to explain that the graph of the genetic code is an unbalanced graph. http://www.iaees.org/publications/journals/nb/articles/2025-15(3)/artificial-intelligence-in-acoustic-ecology.pdf Bruno Daleffi da Silva, Linilson Rodrigues Padovese.Network Biology,2025,15(3):75-89 This article explores the application of machine learning techniques in acoustic ecology to classify the formations of the Brazilian Cerrado (Forest, Savanna, and Grassland) based on their soundscapes. Considering the importance of the Cerrado in biodiversity and hydrology, along with the challenges faced by the biome due to agricultural expansion, the study seeks more efficient and cost-effective methods for identifying its phytophysiognomies. Five statistical models were developed and evaluated, utilizing both traditional Machine Learning and Deep Learning, with Mel Frequency Cepstral Coefficients (MFCCs) and spectrogram images as input variables. The performance comparison of these models revealed the superiority of the Convolutional Neural Network (CNN), which, although requiring higher computational costs and training time, provided high accuracy in classifications and valuable insights through the application of the LIME explainability technique. Additionally, the study proposes a multiple classification methodology by majority voting for frequently observed events, enabling reliable classifications through models with moderate performance. The conclusion is that it is possible to classify different Cerrado formations through their acoustic landscape, and the choice of the optimal model for classification should consider a balance between accuracy, operational complexity, and efficiency. The findings of this study offer relevant guidance for future research and the application of monitoring technologies in conservation and biome recovery efforts. http://www.iaees.org/publications/journals/nb/articles/2025-15(3)/biosynthesis-mathematical-modeling-of-lactic-acid-bacteria.pdf Ankit Barot, Kalyan Das, Yogesh Patel, Divya Kundu.Network Biology,2025,15(3):90-122 There is a dearth of literature on mathematical modeling for producing exopolysaccharides (EPS) synthesized by lactic acid bacteria (LAB). Mathematical models can enhance EPS yield by considering the distribution of control over metabolic flows and physicochemical restrictions. These models improve the economic viability of LAB-EPS production by optimizing output and reducing substrate use. White-box models, utilize comprehensive system knowledge, forecast performance, and optimize processes such as EPS biosynthesis while employing algorithms like fuzzy patterns, rule-based systems, and decision trees to increase productivity and identify bottlenecks. Stoichiometric models quantify the relationship between substrate consumption and EPS production, optimizing carbon fluxes. Metabolic and pathway-based models provide data regarding intracellular networks governing EPS biosynthesis and the effects of nutrient availability. Global models, such as flux balance analysis (FBA), integrate genome-scale networks to predict cellular behavior and optimize production conditions. This review highlights the utility of mathematical modeling as a technique for augmenting LAB-EPS production, discussing various modeling approaches that offer insights into modifying EPS's physicochemical characteristics, structure, and functions. These models can aid in upgrading manufacturing processes, enhancing scalability, and improving the efficiency of LAB-EPS research. The review emphasizes the role of mathematical modeling in optimizing EPS production for applications in the cosmetics, food, dairy, and pharmaceutical industries and strategies to improve industrial-scale processes. http://www.iaees.org/publications/journals/nb/articles/2025-15(4)/state-space-analysis-of-diphtheria-pathogenesis.pdf Ugbene Ifeanyichukwu Jeff, Ighovotueko Sophia Ajuremisan.Network Biology,2025,15(4):123-149 In this study, we analyze the state space of a Boolean network modeling diphtheria pathogenesis, focusing on key genes such as Tox, Rep, INF1/INF2, TLR, AP1, IL6, and TNF. We introduce targeted perturbations to reveal how the network responds and converges to its attractors. Our approach utilizes semi-tensor product techniques and permutation methods to recast the Boolean dynamics into a linear algebraic scheme, enabling efficient identification of transient states, stable attractors, and Garden-of-Eden states. This work fills an important gap by clarifying how specific gene interactions drive the network toward non-pathogenic states. Our results show that altering regulatory relationships, particularly those between Rep, Tox, and interferon signals, significantly influences basin sizes and attractor stability, thereby enhancing our understanding of the network's resilience and informing potential therapeutic strategies. http://www.iaees.org/publications/journals/nb/articles/2025-15(4)/Nilpotent-graph.pdf Birinchi Kumar Boruah.Network Biology,2025,15(4):150-162 The genetic code is a set of codons that contains genetic information regarding the creation of protein molecules. We studied nilpotent graphs in genetic code algebra in this work. The vertex set is the set of all non-nilpotent elements of a ring, and two vertices are neighbouring if and only if their sum is nilpotent. Different measurements of centrality have been thoroughly examined in our current paper. We also investigated three network parameters: clustering coefficient, degree of dispersion, and skewness. http://www.iaees.org/publications/journals/nb/articles/2026-16(1)/.pdf Pandya Trupti, Hajoori Murtaza.Network Biology,2026,16(1):1-14 Polyhydroxyalkanoates (PHAs) are storage materials, accumulated by various bacteria as energy and carbon reserve materials. They are biodegradable and biocompatible; hence, they can be used in packaging and carrier molecules in the agricultural field. In the present study, we aim to produce PHA by isolating and screening PHA-accumulating bacteria by performing primary and secondary screening with Sudan Black B dye and Nile Blue A, respectively. Out of 110 isolates, 19 isolates showed bluish black colouration. The quantification of PHA was carried out by cell dry weight, and biofilm was obtained by the sodium hypochlorite-chloroform method. The isolate K2(2) showed maximum PHA production and was optimised for PHA production using the software RSM. The isolate was further classified up to the genus level by studying its morphological and biochemical characteristics, as well as 16s rRNA sequencing, and it was found to be Bacillus subtilis. The extracted PHA polymer was characterised by Fourier Transform Infrared (FTIR) spectroscopy. The isolate was tested on various parameters to check its effect on PHA production. The isolates were good candidates for the industrial production of PHA. http://www.iaees.org/publications/journals/nb/articles/2026-16(2)/uncovering-multi-target-natural-inhibitors.pdf Neha Singh, Abhay Raj Kori, Pramod Katara.Network Biology,2026,15(2):31-48 Hypertension is a major global health challenge and a key risk factor for cardiovascular, renal, and cerebrovascular disorders. Despite the availability of several synthetic antihypertensive drugs, their prolonged use often leads to adverse side effects, underscoring the need for safer alternatives. Natural compounds represent a promising source of bioactive molecules with potential therapeutic efficacy. Given the multifactorial nature of hypertension, multi-target therapeutic strategies may offer improved disease management. This study employed an integrative computational approach combining network pharmacology and structure-based analyses to identify potential protein targets and natural compounds relevant to hypertension. A total of 22 protein targets associated with hypertension-related pathways were identified. Virtual screening and pharmacokinetic (ADME) evaluations revealed 16 phytochemicals with strong binding affinities, among which 10 exhibited favorable drug-likeness and multi-target interaction profiles. Overall, the findings highlight several natural compounds as promising antihypertensive candidates with polypharmacological potential and a lower likelihood of adverse effects compared to conventional drugs. Experimental validation of the identified targets and lead compounds is warranted to confirm their therapeutic efficacy. http://www.iaees.org/publications/journals/nb/articles/2026-16(2)/TraitGenePathAna.pdf WenJun Zhang.Network Biology,2026,16(2):49-81 The AI-Powered biological trait analysis platform, TraitGenePathAna, is a single-page web application that helps a user explore the biology behind a trait (e.g., longevity, disease resistance) for a chosen species (e.g., Homo sapiens, Drosophila melanogaster). It does this by sending a structured prompt to an LLM provider (DeepSeek or Google Gemini, etc.) and then presenting the model's response in a multi-tab results UI: (1) Overview: summary, significance, broad context; (2) Genetics: key genes, loci, heritability and gene-level discussion; (3) Pathways: molecular mechanisms, signaling cascades, network view; (4) Interventions: potential strategies and caveats (research, ethics, feasibility). http://www.iaees.org/publications/journals/nb/articles/2026-16(2)/convergent-transcriptomic-signatures.pdf Chaitanya Kumar, Ravi Verma, Ashok Sharma, Vishnupriya Veeraraghavan.Network Biology,2026,16(2):82-102 Triple-negative breast cancer (TNBC) remains one of the most aggressive and therapeutically challenging breast cancer subtypes due to the absence of hormone receptors and HER2 expression. To uncover reproducible molecular signatures and potential therapeutic targets, we performed an integrative transcriptomic analysis using three independent GEO datasets (GSE38959, GSE65216, and GSE65194). Differentially expressed genes were examined through protein-protein interaction network construction, enrichment analysis, and survival validation. A highly connected gene cluster comprising CHEK1, PLK1, AURKA, CCNA2, CCNB1, RAD51, TOP2A, KIF11, and KIF23 was consistently upregulated across datasets. These genes were predominantly involved in cell-cycle regulation and DNA-repair pathways and showed significant association with poor overall survival in TNBC cohorts. The findings suggest that dysregulation of the mitotic checkpoint and homologous recombination processes defines a conserved oncogenic program in TNBC. This study highlights druggable molecular targets that could guide the development of pathway-directed therapies in TNBC.