Computational systems biology

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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, and others.

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.

By far the most widely accepted standard format for storing and exchanging models in the field is the Systems Biology Markup Language (SBML) [1] The SBML.org website includes a guide to many important software packages used in computational systems biology. Other markup languages with different emphases include BioPAX and CellML.

[edit] Publications

  • Albert-Laszlo Barabasi & Zoltan N. Oltvai (2004). "Network Biology: Understanding the Cell’s Functional Organization". in: Nature Reviews Genetics 5 101-115
  • Markus W. Covert, Christophe H. Schilling and Bernhard Palsson (2001). "Regulation of Gene Expression in Flux Balance Models of Metabolism". in: J. theor. Biol. 213, 73-88.
  • Markus W. Covert and Bernhard Ø. Palsson (2002). "Transcriptional Regulation in Constraints-based Metabolic Models of Escherichia coli". in: J. Biol. Chem., Vol. 277, Issue 31, 28058-28064, August 2.
  • J. S. Edwards and B. O. Palsson (2000). "The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities". in: PNAS, Vol. 97, Issue 10, 5528-5533, May 9.
  • Richard Bonneau. Learning biological networks: from modules to dynamics. Nature Chemical Biology 4, 658 - 664 (2008)
  • Jeremy S. Edwards, Rafael U. Ibarra, and Bernhard O. Palsson (2001). "In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data". In: Nature Biotechnology, February Volume 19 Number 2 pp 125 - 130.
  • D. A. Fell (1998). "Increasing the Flux in Metabolic Pathways: A Metabolic Control Analysis Perspective". in: Biotechnology and Bioengineering, vol. 58, April 20/May 5.
  • L H Hartwell, J J Hopfield, S Leibler & A W Murray (1999). "From molecular to modular cell biology", in: Nature 402, C47 - C52)
  • Trey Ideker, Timothy Galitski, Leroy Hood (2001). "A New Approach To Decoding Life: Systems Biology" in: Annual Review of Genomics and Human Genetics Sep 2001, Vol. 2: 343-372.
  • H. Kitano (2002). "Computational systems biology" in: Nature 420, 206 - 210 .
  • H. Kitano (2002). "Systems Biology: A Brief Overview", in: Science, 295, 1662-1664.
  • H. Kitano (2002). "Looking beyond the details: a rise in system-oriented approaches in geneticsand molecular biology" in: Curr Genet. 2002 Apr;41(1):1-10, PMID: 12073094
  • A.G. Gilman et al. (2002). "Overview of the Alliance for Cellular Signaling", in: Nature, 420, 703 - 706 (12 December).
  • Bernhard Ø. Palsson (2006), Systems Biology - Properties of Reconstructed Networks. . Cambridge University Press.
  • Kauffman KJ, Prakash P, and Edwards JS.(2003). "Advances in Flux Balance Analysis". in: Current Opinion in Biotechnology, 14(5): 491-496.
  • Dennis Vitkup, and George M. Church (2002), Analysis of optimality in natural and perturbed metabolic networks. PNAS, November 12, vol. 99, 15112-15117.
  • Mary C. Wildermuth (2000). "Metabolic control analysis: biological applications and insights". Genome Biology Minireview.

[edit] References

  1. ^ Klipp, Liebermeister, Helbig, Kowald and Schaber. (2007). "Systems biology standards—the community speaks" (2007), Nature Biotechnology 25(4):390–391.

[edit] External links

Computational Systems Biology Research Groups
Computational Systems Biology Software (partial list)
  • BioNetGen - software for rule-based modeling of biochemical networks
  • Cain - application for the stochastic simulation of chemical kinetics. Direct, next reaction, tau-leaping, hybrid, etc.
  • 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.
  • SBTOOLBOX2 - powerful, open, and user extensible environment, in which to build, simulate, and analyze models of biochemical networks.
  • Semantic Systems Biology
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