Network Biology

Aims & Scope

Network biology is a science that deals with the structure, function, regulation (control), design, and application, etc., of various biological networks. It is an interdisciplinary science based on life sciences (biology, ecology, medicine, etc.), mathematics and computational science (graph theory, network science, differential equations, computation methods, programming, etc.), systems science (complexity theory, self-organizology, etc.), and statistics, etc. The NETWORK BIOLOGY (ISSN 2220-8879; CODEN NBEICS) is an open access (BOAI definition), open peer reviewed online journal (users are free to read, download, copy, distribute, print, search, or link to the full texts of the articles) that considers scientific articles in all different areas of network biology. 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: Theories, algorithms and programs of network analysis, Evolution, dynamics, optimization and control of biological networks, Network construction, link prediction, Network topology, topological analysis, relationship between topological structure and network functions, sensitivity analysis, network robustness and stability, Network flow analysis, Design and formulation of biological networks, Ecological networks, food webs and natural equilibrium, co-evolution, co-extinction, biodiversity conservation, Metabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs and modules, Physiological networks, social networks, epidemiological networks, Network regulation of metabolic processes, human diseases and ecological systems, System complexity, self-organization, emergence of biological systems, agent-based modeling, neural network modeling, and other network-based modeling, etc., Big data analytics of biological networks, meta-analysis, Mendelian randomization, casual inference, Bayesian inference, 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.

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