Biomolecular network querying: a promising approach in systems biology

The rapid accumulation of various network-related data from multiple species and conditions (e.g. disease versus normal) provides unprecedented opportunities to study the function and evolution of biological systems. Comparison of biomolecular networks between species or conditions is a promising approach to understanding the essential mechanisms used by living organisms. Computationally, the basic goal of this network comparison or 'querying' is to uncover identical or similar subnetworks by mapping the queried network (e.g. a pathway or functional module) to another network or network database. Such comparative analysis may reveal biologically or clinically important pathways or regulatory networks. In particular, we argue that user-friendly tools for network querying will greatly enhance our ability to study the fundamental properties of biomolecular networks at a system-wide level.

[1]  S. Horvath,et al.  A General Framework for Weighted Gene Co-Expression Network Analysis , 2005, Statistical applications in genetics and molecular biology.

[2]  Johannes Berg,et al.  Cross-species analysis of biological networks by Bayesian alignment. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[3]  A. Butte,et al.  Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Roded Sharan,et al.  Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data , 2004, J. Comput. Biol..

[5]  Adam A. Margolin,et al.  Reverse engineering of regulatory networks in human B cells , 2005, Nature Genetics.

[6]  Shi-Hua Zhang,et al.  Alignment of molecular networks by integer quadratic programming , 2007, Bioinform..

[7]  Ambuj K. Singh,et al.  Closure-Tree: An Index Structure for Graph Queries , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[8]  R. Karp,et al.  Conserved pathways within bacteria and yeast as revealed by global protein network alignment , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[10]  Michael Griffin,et al.  Gene co-expression network topology provides a framework for molecular characterization of cellular state , 2004, Bioinform..

[11]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[12]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[13]  T. Ideker,et al.  Modeling cellular machinery through biological network comparison , 2006, Nature Biotechnology.

[14]  Kim Sneppen,et al.  Functional Alignment of Regulatory Networks: A Study of Temperate Phages , 2005, PLoS Comput. Biol..

[15]  M. Kanehisa,et al.  A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. , 2000, Nucleic acids research.

[16]  G. Gibson,et al.  Cross-species comparison of genome-wide expression patterns , 2004, Genome Biology.

[17]  Ron Y. Pinter,et al.  Alignment of metabolic pathways , 2005, Bioinform..

[18]  S. Horvath,et al.  Conservation and evolution of gene coexpression networks in human and chimpanzee brains , 2006, Proceedings of the National Academy of Sciences.

[19]  H. Lehrach,et al.  A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome , 2005, Cell.

[20]  Trupti Joshi,et al.  Inferring gene regulatory networks from multiple microarray datasets , 2006, Bioinform..

[21]  Luonan Chen,et al.  Inferring transcriptional regulatory networks from high-throughput data , 2007, Bioinform..

[22]  Roded Sharan,et al.  PathBLAST: a tool for alignment of protein interaction networks , 2004, Nucleic Acids Res..

[23]  R. Karp,et al.  From the Cover : Conserved patterns of protein interaction in multiple species , 2005 .

[24]  M. Gerstein,et al.  Interrelating different types of genomic data, from proteome to secretome: 'oming in on function. , 2001, Genome research.

[25]  M. Medina.  Genomes, phylogeny, and evolutionary systems biology , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[26]  T. Sittler,et al.  The Plasmodium protein network diverges from those of other eukaryotes , 2005, Nature.

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

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

[29]  S. Horvath,et al.  Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target , 2006, Proceedings of the National Academy of Sciences.

[30]  Antal F. Novak,et al.  networks Græmlin : General and robust alignment of multiple large interaction data , 2006 .

[31]  R. Albert.  Scale-free networks in cell biology , 2005, Journal of Cell Science.

[32]  Michael Lässig,et al.  Local graph alignment and motif search in biological networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Philip S. Yu,et al.  A graph-based approach to systematically reconstruct human transcriptional regulatory modules , 2007, ISMB/ECCB.

[34]  Dennis Shasha,et al.  NetMatch : a Cytoscape plugin for searching biological networks , 2006 .

[35]  Roded Sharan,et al.  QPath: a method for querying pathways in a protein-protein interaction network , 2006, BMC Bioinformatics.

[36]  Luonan Chen,et al.  Analysis on multi-domain cooperation for predicting protein-protein interactions , 2007, BMC Bioinformatics.

[37]  Luonan Chen,et al.  Discovering functions and revealing mechanisms at molecular level from biological networks , 2007, Proteomics.

[38]  Wojciech Szpankowski,et al.  Pairwise Local Alignment of Protein Interaction Networks Guided by Models of Evolution , 2005, RECOMB.