BINESA — A software tool for evolution modelling of biochemical networks' structure

Biochemical networks of different types such as transcription regulatory, gene regulation, metabolic, protein interaction and signal transduction networks have been extensively studied. One of research directions is related with topological properties of biochemical networks and their structure evolution. Earlier research clearly shows that biochemical networks differ considerably from random ones in their network structure. The reason could be mutation and selection based evolution combined with different importance of particular paths of the network for the survival of an organism. It would be useful to investigate and estimate the changes of network structure that are caused by different types of mutation during the evolution, such as point mutations, missense, nonsense mutations, nucleotide inversion, gene inversion, duplication, deletion, and translocation mutations. BINESA (BIochemical NEtwork Structure Analyser) is a standalone software tool developed for evolution modelling of a biochemical network structure founded on evolutionary changes of an underlying artificial genome. This software tool is written in Visual Basic programming language and developed for Windows operating system using Microsoft Access. BINESA has a database for storing of network structure and artificial genome data. This tool provides the exploration of the evolution dynamics of biochemical network structure, supports SBML and GML models import and export, allows visualization of the network structure in graph form visually marking out reactions of different importance and perform a topological analysis of the biochemical network structure.

[1]  Kwang-Hyun Cho,et al.  Analysis of feedback loops and robustness in network evolution based on Boolean models , 2007, BMC Bioinformatics.

[2]  S. Redner,et al.  Connectivity of growing random networks. , 2000, Physical review letters.

[3]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[4]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[5]  Song Li,et al.  Boolean network simulations for life scientists , 2008, Source Code for Biology and Medicine.

[6]  K. Christensen,et al.  Evolving networks through deletion and duplication , 2006, physics/0609172.

[7]  S. Kauffman,et al.  Robustness and evolvability in genetic regulatory networks. , 2007, Journal of theoretical biology.

[8]  P. Rogers,et al.  Zymomonas mobilis for fuel ethanol and higher value products. , 2007, Advances in biochemical engineering/biotechnology.

[9]  Wan Kyu Kim,et al.  Age-Dependent Evolution of the Yeast Protein Interaction Network Suggests a Limited Role of Gene Duplication and Divergence , 2008, PLoS Comput. Biol..

[10]  M. Weigt,et al.  On the properties of small-world network models , 1999, cond-mat/9903411.

[11]  M. Mednis,et al.  Comparison of genome-scale reconstructions using ModeRator , 2012, 2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI).

[12]  Maike K. Aurich,et al.  Application of string similarity ratio and edit distance in automatic metabolite reconciliation comparing reconstructions and models , 2012 .

[13]  Egils Stalidzans,et al.  Biotechnological potential of respiring Zymomonas mobilis: a stoichiometric analysis of its central metabolism. , 2013, Journal of biotechnology.

[14]  A. Wagner.  How the global structure of protein interaction networks evolves , 2002, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[15]  Monika Heiner,et al.  A structured approach for the engineering of biochemical network models, illustrated for signalling pathways , 2008, Briefings Bioinform..

[16]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[17]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[18]  Zhenjun Hu,et al.  VisANT: data-integrating visual framework for biological networks and modules , 2005, Nucleic Acids Res..

[19]  Chrystopher L. Nehaniv,et al.  The NetBuilder' project: development of a tool for constructing, simulating, evolving, and analysing complex regulatory networks , 2007, BMC Systems Biology.

[20]  Ricard V. Solé,et al.  A Model of Large-Scale proteome Evolution , 2002, Adv. Complex Syst..

[21]  Aidong Zhang.  Protein Interaction Networks: Introduction , 2009 .

[22]  Masanori Arita.  The metabolic world of Escherichia coli is not small. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Luonan Chen,et al.  Biomolecular Networks: Methods and Applications in Systems Biology , 2009 .

[24]  David Zhang,et al.  SimBoolNet—a Cytoscape plugin for dynamic simulation of signaling networks , 2009, Bioinform..

[25]  A Simulation Framework for Modeling Combinatorial Control in Transcription Regulatory Networks , 2007 .

[26]  Claudine Chaouiya,et al.  Petri net modelling of biological networks , 2007, Briefings Bioinform..

[27]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[28]  D Thieffry,et al.  GINsim: a software suite for the qualitative modelling, simulation and analysis of regulatory networks. , 2006, Bio Systems.

[29]  R. Solé,et al.  Evolving protein interaction networks through gene duplication. , 2003, Journal of theoretical biology.

[30]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[31]  Martins Mednis,et al.  Automatic comparison of metabolites names: impact of criteria thresholds , 2013 .

[32]  Andrew Wuensche,et al.  Discrete Dynamics Lab , 2009 .

[33]  Jingtai Han.  Understanding biological functions through molecular networks , 2008, Cell Research.

[34]  Aidong Zhang,et al.  Protein Interaction Networks: Computational Analysis , 2009 .

[35]  Andrew Wuensche,et al.  Discrete dynamics lab: tools for investigating cellular automata and discrete dynamical networks , 2003 .

[36]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[37]  P. Bork,et al.  Evolution of biomolecular networks — lessons from metabolic and protein interactions , 2009, Nature Reviews Molecular Cell Biology.

[38]  T. Rubina.  The procedure of evolution modelling of biochemical networks structure , 2013 .

[39]  Hans A. Kestler,et al.  BoolNet - an R package for generation, reconstruction and analysis of Boolean networks , 2010, Bioinform..

[40]  A. Vázquez.  Growing network with local rules: preferential attachment, clustering hierarchy, and degree correlations. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  Hierarchical Organization of Modularity in Metabolic Networks Supporting Online Material , 2002 .

[42]  Takeshi Hase,et al.  Protein-Protein Interaction Networks: Structures, Evolution, and Application to Drug Design , 2012 .

[43]  Egils Stalidzans,et al.  Agreement assessment of biochemical pathway models by structural analysis of their intersection , 2013, 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI).

[44]  J. Hopcroft,et al.  Are randomly grown graphs really random? , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.