![]() Considerable network software resources are available online for flux analysis. Another group of methods includes extreme pathway analysis (Papin et al.) and flux analysis (Burgard et al.) in metabolic networks. A large variety of approaches have been proposed, many of them constructing different network profiles and hierarchical trees, such as those based on node connectivity (the topological overlap method of Ravasz et al.) and node distance (the association matrix method of Rives and Galitski). Modularisation of networks is another area of intensive research, aimed at facilitating the analysis of complex networks by partitioning them into modules, presumably related to a certain biological function. Recently, another freely available software, named Kavosh (Kashani et al.), claimed slightly better performance than FANMOD and added the option of handling motifs having more than eight nodes. FANMOD is particularly user-friendly and fast software which runs under Linux, MacOS and Windows, and classifies the motifs according to their frequency of distribution, p values and z-scores, in comparison with the generated randomised networks having the same size and the same node degree distribution. Other groups followed with freely available software: MAVisto (Schreiber and Schwöbbermeyer) and FANMOD (Wernicke and Rasche). The concept of motifs was developed in the Laboratory of Uri Alon from the Weizmann Institute in Israel, where a library of identification (ID) numbers of all motifs having three to eight nodes was created, along with the downloadable MFinder software for motif identification. Viewed as the smallest building blocks of networks, motifs serve as a signature for distinguishing species, or different states of a single species, and are of interest for evolutionary and biomedical studies. One such kind of software performs a substructure search for identifying over-represented sub-graphs called motifs. Other essential types of software in this field, which solve more specific network tasks, are also listed. The detailed analysis in this review is devoted to several of the most comprehensive and multi-functional software packages for network analysis in molecular biology. ![]() Examples of such networks are protein-protein interaction networks (PPN), gene regulatory networks (GRN), and metabolic and signalling networks and pathways, as well as disease-related or cell function-related networks. This review focuses on software for the analysis of networks in living cells, the nodes in which represent genes, proteins, metabolites and other cell components. Many of those are multi-purpose programs with applications to most of the available types of complex networks: social, transportation, communication, financial, etc. ![]() The explosive development of the theory of dynamic evolutionary networks during the past decade stimulated the creation of numerous algorithms and software programs for constructing, manipulating and analysing networks. Networks are basic tools in systems biology for expressing the essence of living things as whole integrated systems. Post-genomic biology makes extensive use of network analysis at all levels of the hierarchy of life. ![]()
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