Structural comparison of biological networks based on dominant vertices.

It is a current practice to organize biological data in a network structure where vertices represent biological components and arrows represent their interactions. A great diversity of graph theoretical notions, such as clustering coefficient, network motifs, centrality, degree distribution, etc., have been developed in order to characterize the structure of these networks. However, none of the existent characterizations allow us to determine global similarity among networks of different sizes. It is the aim of the present paper to introduce a mathematical tool to compare networks not only with regard to their topological structure, but also in their dynamical capabilities. For this reason we aim to propose a pseudo-distance between networks, built around the notions of determination and dominancy, concepts recently introduced in the context of regulatory dynamics on networks. We use our proposed pseudo-distance to compare networks from the following bacteria: E. coli, B. subtilis, P. aeruginosa, M. tuberculosis, S. aureus and C. glutamicum. We also use this pseudo-distance to compare these real bacterial networks with equivalent homogeneous, scale-free and geometric three dimensional random networks. We found that even when bacterial networks are characterized with different levels of detail, have different sizes and represent different aspects of the organisms, the proposed pseudo-distance captures all these characteristics, and indicates how similar they are or not from random networks.

[1]  Natasa Przulj,et al.  Biological network comparison using graphlet degree distribution , 2007, Bioinform..

[2]  Edgardo Ugalde,et al.  Dominant vertices in regulatory networks dynamics , 2007, 0708.0595.

[3]  H. Vlamakis,et al.  Generation of multiple cell types in Bacillus subtilis. , 2009, FEMS microbiology reviews.

[4]  Emery N. Brown,et al.  A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity , 2011, PLoS Comput. Biol..

[5]  Falk Schreiber,et al.  Analysis of Biological Networks , 2008 .

[6]  A. Goesmann,et al.  The complete Corynebacterium glutamicum ATCC 13032 genome sequence and its impact on the production of L-aspartate-derived amino acids and vitamins. , 2003, Journal of biotechnology.

[7]  Kenta Nakai,et al.  DBTBS: a database of Bacillus subtilis promoters and transcription factors , 2001, Nucleic Acids Res..

[8]  Ron Y. Pinter,et al.  Comparative classification of species and the study of pathway evolution based on the alignment of metabolic pathways , 2010, BMC Bioinformatics.

[9]  Tatsuya Akutsu,et al.  Comparing biological networks via graph compression , 2010, BMC Systems Biology.

[10]  Kenta Nakai,et al.  DBTBS: a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information , 2007, Nucleic Acids Res..

[11]  A. Goffeau,et al.  The complete genome sequence of the Gram-positive bacterium Bacillus subtilis , 1997, Nature.

[12]  N. W. Davis,et al.  The complete genome sequence of Escherichia coli K-12. , 1997, Science.

[13]  Julio Collado-Vides,et al.  RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units) , 2010, Nucleic Acids Res..

[14]  R. Thomas,et al.  Dynamical behaviour of biological regulatory networks--II. Immunity control in bacteriophage lambda. , 1995, Bulletin of mathematical biology.

[15]  B. Barrell,et al.  Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence , 1998, Nature.

[16]  D. Thieffry,et al.  Functional organisation of Escherichia coli transcriptional regulatory network , 2008, Journal of molecular biology.

[17]  Andreas Tauch,et al.  CoryneRegNet 6.0—Updated database content, new analysis methods and novel features focusing on community demands , 2011, Nucleic Acids Res..

[18]  J. Gallacher,et al.  Childhood Socioeconomic Position and Objectively Measured Physical Capability Levels in Adulthood: A Systematic Review and Meta-Analysis , 2011, PloS one.

[19]  J. Lyczak,et al.  Establishment of Pseudomonas aeruginosa infection: lessons from a versatile opportunist. , 2000, Microbes and infection.

[20]  S. Lory,et al.  Complete genome sequence of Pseudomonas aeruginosa PAO1, an opportunistic pathogen , 2000, Nature.

[21]  Matthew DeJongh,et al.  Inference of the Transcriptional Regulatory Network in Staphylococcus aureus by Integration of Experimental and Genomics-Based Evidence , 2011, Journal of bacteriology.

[22]  Anirban Banerjee,et al.  Structural distance and evolutionary relationship of networks , 2008, Biosyst..

[23]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[24]  Mark Gerstein,et al.  Measuring the Evolutionary Rewiring of Biological Networks , 2011, PLoS Comput. Biol..

[25]  R. Coutinho,et al.  Discrete time piecewise affine models of genetic regulatory networks , 2005, Journal of mathematical biology.

[26]  Rick Durrett,et al.  Random Graph Dynamics (Cambridge Series in Statistical and Probabilistic Mathematics) , 2006 .

[27]  M. Kanehisa,et al.  Whole genome sequencing of meticillin-resistant Staphylococcus aureus , 2001, The Lancet.

[28]  B. Kallipolitis,et al.  Searching for small σB-regulated genes in Staphylococcus aureus , 2010, Archives of Microbiology.

[29]  Natasa Przulj,et al.  Graphlet-based measures are suitable for biological network comparison , 2013, Bioinform..

[30]  U. Alon Network motifs: theory and experimental approaches , 2007, Nature Reviews Genetics.

[31]  I. Smith,et al.  Mycobacterium tuberculosis Pathogenesis and Molecular Determinants of Virulence , 2003, Clinical Microbiology Reviews.

[32]  J. Collado-Vides,et al.  Identifying global regulators in transcriptional regulatory networks in bacteria. , 2003, Current opinion in microbiology.

[33]  J. Collado-Vides,et al.  The repertoire of DNA-binding transcriptional regulators in Escherichia coli K-12. , 2000, Nucleic acids research.

[34]  Ambuj K. Singh,et al.  Deriving phylogenetic trees from the similarity analysis of metabolic pathways , 2003, ISMB.

[35]  R. Durrett Random Graph Dynamics: References , 2006 .