Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

[1]  Trey Ideker,et al.  Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. , 2002, Molecular cancer research : MCR.

[2]  Min Pan,et al.  Coordinate regulation of energy transduction modules in Halobacterium sp. analyzed by a global systems approach , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Nicola J. Rinaldi,et al.  Transcriptional Regulatory Networks in Saccharomyces cerevisiae , 2002, Science.

[4]  Benno Schwikowski,et al.  Discovering regulatory and signalling circuits in molecular interaction networks , 2002, ISMB.

[5]  B. Snel,et al.  Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.

[6]  Gary D Bader,et al.  Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants , 2001, Science.

[7]  J. Griffin,et al.  Choline containing metabolites during cell transfection: an insight into magnetic resonance spectroscopy detectable changes , 2001, FEBS letters.

[8]  Hiroaki Kitano,et al.  The ERATO Systems Biology Workbench: Enabling Interaction and Exchange Between Software Tools for Computational Biology , 2001, Pacific Symposium on Biocomputing.

[9]  T. Chiba,et al.  Exploring the protein interactome using comprehensive two-hybrid projects. , 2001, Trends in biotechnology.

[10]  L. Loew,et al.  The Virtual Cell: a software environment for computational cell biology. , 2001, Trends in biotechnology.

[11]  P D Karp,et al.  Pathway Databases: A Case Study in Computational Symbolic Theories , 2001, Science.

[12]  D. Eisenberg,et al.  Protein interaction databases. , 2001, Current opinion in biotechnology.

[13]  R. Aebersold,et al.  A systematic approach to the analysis of protein phosphorylation , 2001, Nature Biotechnology.

[14]  R. Brent,et al.  Modelling cellular behaviour , 2001, Nature.

[15]  Ian M. Donaldson,et al.  BIND: the Biomolecular Interaction Network Database , 2001, Nucleic Acids Res..

[16]  Anton J. Enright,et al.  Protein interaction maps for complete genomes based on gene fusion events , 1999, Nature.

[17]  S. Gygi,et al.  Quantitative analysis of complex protein mixtures using isotope-coded affinity tags , 1999, Nature Biotechnology.

[18]  D. Eisenberg,et al.  Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[19]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[20]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[21]  P. Brown,et al.  Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.

[22]  P Mendes,et al.  Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3. , 1997, Trends in biochemical sciences.

[23]  A. Ruepp,et al.  Fermentative arginine degradation in Halobacterium salinarium (formerly Halobacterium halobium): genes, gene products, and transcripts of the arcRACB gene cluster , 1996, Journal of bacteriology.

[24]  D. Oesterhelt,et al.  Functions of a new photoreceptor membrane. , 1973, Proceedings of the National Academy of Sciences of the United States of America.

[25]  A. Arkin,et al.  Genetic "code": representations and dynamical models of genetic components and networks. , 2002, Annual review of genomics and human genetics.

[26]  Susumu Goto,et al.  The KEGG databases at GenomeNet , 2002, Nucleic Acids Res..

[27]  Xin Chen,et al.  The TRANSFAC system on gene expression regulation , 2001, Nucleic Acids Res..

[28]  Michael Y. Galperin,et al.  The COG database: new developments in phylogenetic classification of proteins from complete genomes , 2001, Nucleic Acids Res..

[29]  Gary D Bader,et al.  BIND--The Biomolecular Interaction Network Database. , 2001, Nucleic acids research.

[30]  J. Blake,et al.  Creating the gene ontology resource: design and implementation. , 2001, Genome research.

[31]  Vladimir Batagelj,et al.  Pajek - Program for Large Network Analysis , 1999 .

[32]  Masaru Tomita,et al.  E-CELL: software environment for whole-cell simulation , 1999, Bioinform..

[33]  Jehoshua Bruck,et al.  A probabilistic model of a prokaryotic gene and its regulation , 1999 .

[34]  Peter Eades,et al.  A Heuristic for Graph Drawing , 1984 .