Computational systems biology

Computational systems biology is the algorithm and application development arm of systems biology. It is also directly associated with bioinformatics and computational biology. Computational systems biology aims to develop and use efficient algorithms, data structures and communication tools to orchestrate the integration of large quantities of biological data with the goal of modelling dynamic characteristics of a biological system. Modelled quantities may include steady-state metabolic flux or the time-dependent response of signaling networks. Algorithmic methods used include related topics such as optimization, network analysis, graph theory, linear programming, grid computing, flux balance analysis, sensitivity analysis, dynamic modeling, and others.

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It is understood that an unexpected emergent property of a complex system is a result of the interplay of the cause-and-effect among simpler, integrated parts. Biological systems manifest many important examples of emergent properties in the complex interplay of components. Traditional study of biological systems requires reductive methods in which quantities of data are gathered by category, such as concentration over time in response to a certain stimulus. Computers are critical to analysis and modeling of these data. The goal is to create accurate real-time models of a system's response to environmental and internal stimuli, such as a model of a cancer cell in order to find weaknesses in its signaling pathways, or modeling of ion channel mutations to see effects on cardiomyocytes and in turn, the function of a beating heart.

Two important markup languages for systems biology are the Systems Biology Markup Language (SBML) and CellML. Many important software projects in computational systems biology are included in that link.

Computational Systems Biology Research Groups

• Computational Systems Biology at ETH Zürich, Switzerland
• Computational Systems Biology at Max Planck Institute for Molecular Genetics, Berlin, Germany
• Computational Systems Biology at University of Washington
• Computational Systems Biology at University of Edinburgh
• Department of BioSystems at KAIST, Korea Advanced Institute of Science and Technology
• Systems Analysis, Modelling and Prediction Group at the University of Oxford. Researches techniques for inferring biochemical reaction pathways from chemical concentration data.

References

• Network Biology: Understanding the Cell’s Functional Organization. Albert-Laszlo Barabasi & Zoltan N. Oltvai. Nature Reviews Genetics 5 101-115 (2004)
• From molecular to modular cell biology, L H Hartwell, J J Hopfield, S Leibler & A W Murray, Nature 402, C47 - C52 (1999)
• A New Approach To Decoding Life: Systems Biology Trey Ideker, Timothy Galitski, Leroy Hood Annual Review of Genomics and Human Genetics Sep 2001, Vol. 2: 343-372.
• Computational systems biology, H. Kitano, Nature 420, 206 - 210 (2002).
• Systems Biology: A Brief Overview, H. Kitano, Science, 295, 1662-1664, 2002.
• Looking beyond the details: a rise in system-oriented approaches in geneticsand molecular biology, H Kitano, Curr Genet. 2002 Apr;41(1):1-10, PMID: 12073094
• Overview of the Alliance for Cellular Signaling, Nature, 420, 703 - 706 (12 December 2002).
• The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities. J. S. Edwards and B. O. Palsson. PNAS, Vol. 97, Issue 10, 5528-5533, May 9, 2000.
• In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Jeremy S. Edwards, Rafael U. Ibarra, and Bernhard O. Palsson. Nature Biotechnology, February 2001 Volume 19 Number 2 pp 125 - 130.
• Regulation of Gene Expression in Flux Balance Models of Metabolism. J. theor. Biol. (2001) 213, 73-88. Markus W. Covert, Christophe H. Schilling and Bernhard Palsson.
• Transcriptional Regulation in Constraints-based Metabolic Models of Escherichia coli. Markus W. Covert and Bernhard Ø. Palsson. J. Biol. Chem., Vol. 277, Issue 31, 28058-28064, August 2, 2002.
• Analysis of optimality in natural and perturbed metabolic networks. Daniel Segrè, Dennis Vitkup, and George M. Church. PNAS, November 12, 2002, vol. 99, 15112-15117.
• Metabolic control analysis: biological applications and insights. Mary C. Wildermuth, Genome Biology Minireview 2000.
• Increasing the Flux in Metabolic Pathways: A Metabolic Control Analysis Perspective. D. A. Fell, Biotechnology and Bioengineering, vol. 58, April 20/May 5, 1998.
• Advances in Flux Balance Analysis. Kauffman KJ, Prakash P, and Edwards JS., Current Opinion in Biotechnology, 14(5): 491--6, 2003.
• Systems Biology - Properties of Reconstructed Networks. Bernhard Ø. Palsson. Cambridge University Press. (2006)

Computational Systems Biology Software

• BioNetGen - software for rule-based modeling of biochemical networks
• COPASI - portable software for modeling, simulation and analysis of biochemical network dynamics using differential equations or Monte Carlo Markov chains.
• Cyclone - provides an open source Java API to the pathway tool BioCyc to extract Metabolic graphs.