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. [The Cytoscape v1.1 Core runs on all major operating systems and is freely available for download from http://www.cytoscape.org/ as an open source Java application.] Such models promise to transform biological research by providing a framework to (1) systematically interrogate and experimentally verify knowledge of a pathway; (2) manage the immense complexity of hundreds or potentially thousands of cellular components and interactions; and (3) reveal emergent properties and unanticipated consequences of different pathway configurations. Typically, models are directed toward a cellular process or disease pathway of interest (Gilman and Arkin 2002) and are built by formulating existing literature as a system of differential and/or stochastic equations. However, pathway-specific models are now being supplemented with global data gathered for an entire cell or organism, by use of two complementary approaches. First, recent technological developments have made it feasible to measure pathway structure systematically, using highthroughput screens for protein-protein (Ito et al. 2001; von Mering et al. 2002 ), protein-DNA (Lee et al. 2002, and genetic interactions (Tong et al. 2001) . To complement these data, a second set of high-throughput methods are available to characterize the molecular and cellular states induced by pathway interactions under different experimental conditions. For instance, global changes in gene expression are measured with DNA microarrays (DeRisi et al. 1997) , whereas changes in protein abundance (Gygi et al. 1999) , protein phosphorylation state (Zhou et al. 2001) , and metabolite concentrations (Griffin et al. 2001) may be quantified with mass spectrometry, NMR, and other advanced techniques. High-throughput data pertaining to molecular interactions and states are well matched, in that both data types are global (providing information for all components or interactions in an organism); high-level (outlining relationships among pathway components without detailed information on reaction rates, binding constants, or diffusion coefficients); and coarse-grained (yielding qualitative data, such as the presence or absence of an interaction or the direction of an expression change, more readily than precise quantitative readouts). Motivated by the explosion in experimental technologies for characterizing molecular interactions and states, researchers have turned to a variety of software tools to process and analyze the resulting large-scale data. For molecular interactions, generalpurpose graph viewers such as Pajek (Batagelj and Mrvar 1998), Graphlet (www.infosun.fmi.uni-passau.de/Graphlet/), and daVinci (www.informatik.uni-bremen.de/daVinci/) are available to organize and display the data as a two-dimensional network; specialized tools such as Osprey (http://biodata.mshri.on.ca) and PIMrider (pim.hybrigenics.com) provide these capabilities and also link the network to molecular interaction and functional databases such as BIND ), DIP (Xenarios and Eisenberg 2001), or TRANSFAC (Wingender et al. 2001. Similarly, for gene expression profiles and other molecular states, numerous programs such as GeneCluster (Tamayo et al. 1999) , TreeView (Eisen et al. 1998), and GeneSpring (www.silicongenetics. com) are available for clustering, classification, and visualization. However, a pressing need remains for software that is able to integrate both molecular interactions and state measurements together in a common framework, and to then bridge these data with a wide assortment of model parameters and other biological attributes. Moreover, a flexible and open system will be required to facilitate general and extensible computations on the interaction network (Karp 2001) . It is through these compu-
Cystoscape REF is an open source, standalone Java application.
Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks
Genome research
Genome research
Biology Medicine