Catalyzing Inquiry at the Interface of Computing and Biology

142 CATALYZING INQUIRY

transcription, translation, energy production, and phospholipid synthesis with only 127 genes. Most of
these genes were taken from Mycoplasma genitalium, the organism with the smallest known chromo-
some (the complete genome sequence is 580 kilobases).^48 E-CELL has also been used to construct a
computer model of the human erythrocyte,^49 to estimate a gene regulatory network and signaling

Box 5.6

Cytoscape

A variety of computer-aided models has been developed to simulate biological networks, typically focusing

on specific cellular processes or single pathways.^1 Cytoscape is a modeling environment particularly suited

to the analysis of global data on network interactions (from high-throughput screens for protein-protein, pro-

tein-DNA, and gene interactions) and on network states (including data on gene expression, protein abun-

dance, and metabolite concentrations.) The Java-based, open-source software uses plug-ins to incorporate

analyses of individual processes and pathways.^2

A model in Cytoscape is organized as a network graph, with molecular species represented as nodes and

interactions represented as edges between nodes. Nodes and edges are mapped to specific data values called

attributes that can be text strings, discrete or continuous numbers, URLs, or lists, either loaded from a data

repository or generated dynamically. Layered onto attributes are annotations, which represent a hierarchical

classification of progressively more specific descriptions (such as functions) of groups of nodes and edges. It is

possible to have many levels of annotation active simultaneously, each displayed as a different attribute of a

node or edge. To visualize the network, Cytoscape supports several layout algorithms that fix the relative

locations of specific nodes and edges in the graphical window. An attribute-to-visual mapping facility allows

attributes to determine the appearance (color, shape, size) of their associated nodes and edges. Graph selec-

tion and filtering reduces the complexity of the network by selectively displaying subsets of nodes and edges

according to a variety of criteria.

Cytoscape’s plug-in extensibility addresses the challenge of bridging high-level information (relationships among

network components) with lower-level information (reaction rates, binding constants) of specific processes. A

plug-in that organizes the network layout according to putative functional attributes of genes was used to study

energy transduction pathways in Halobacterium.^3 Another plug-in allows Cytoscape to simulate stochastic

SBML-biochemical models.^4 The authors hope a community will further develop and enhance Cytoscape.

(^1) A. Gilman and A.P. Arkin, “Genetic ‘Code’: Representations and Dynamical Models of Genetic Components and Networks,” Annual
Review of Genomics and Human Genetics 3:341-369, 2002
(^2) P. Shannon, A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, N. Amin, et al., “Integrated Models of Biomolecular Interaction
Networks,” Genome Research 13:2498-2504, 2003.
(^3) N.S. Baliga, M. Pan, Y.A. Goo, E.C. Yi, D.R. Goodlett, K. Dimitrov, P. Shannon, et al., “Coordinate Regulation of Eenergy Transduction
Modules in Halobacterium species Analyzed by a Global Systems Approach,” Proceedings of the National Academy of Sciences
99(23):14913-14918, 2002.
(^4) M. Hucka, A. Finney, H.M. Sauro, H. Bolouri, J. Doyle, and H. Kitano, “The ERATO Systems Biology Workbench: Enabling Interaction
and Exchange Between Software Tools for Computational Biology,” Pacific Symposium in Biocomputing, 450-461, 2002.
SOURCE: Adapted from P. Shannon, A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, N. Amin et al., “Cytoscape: A Software
Environment for Integrated Models of Biomolecular Interaction Networks,” Genome Research 13(11):2498-2504, 2003.
(^48) M. Tomita, K. Hashimoto, K. Takahashi, Y. Matsuzaki, R. Matsushima, K. Saito, K. Yugi, et al., “E-CELL Project Overview:
Towards Integrative Simulation of Cellular Processes,” Genome Informatics 9:242-243, 1998, available at http://giw.ims.u-
tokyo.ac.jp/giw98/cdrom/Poster-pdf/poster02.pdf.
(^49) M. Tomita et al., “In Silico Analysis of Human Erythrocyte Using E-Cell System,” poster session, The Future of Biology in the
21st Century: 2nd International Conference on Systems Biology, California Institute of Technology, Pasadena, November 4-7,
2001, available at http://www.icsb2001.org/Posters/032_kinoshita.pdf.