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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Robust simplifications of multiscale biochemical networks.

Ovidiu Radulescu1, Alexander N Gorban, Andrei Zinovyev

  • 1IRMAR (CNRS UMR 6025), Université de Rennes 1, Rennes, France. ovidiu.radulescu@univ-rennes1.fr

BMC Systems Biology
|October 16, 2008
PubMed
Summary

We developed new model reduction techniques for complex biological networks. These methods simplify models while identifying critical parameters and creating hierarchies for better understanding of cellular processes.

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Area of Science:

  • Systems Biology
  • Biochemical Networks
  • Computational Biology

Background:

  • Cellular processes involve complex biochemical reaction networks.
  • Understanding these systems requires model reduction techniques.
  • Comparing and coupling models necessitates common complexity levels.

Purpose of the Study:

  • To systematically treat model reduction for multiscale biochemical networks.
  • To develop algorithms for simplifying linear and nonlinear systems.
  • To identify critical parameters and create model hierarchies.

Main Methods:

  • Reduction algorithm for linear kinetic models based on limiting step theory.
  • Algorithm for nonlinear systems using dominant solutions of quasi-stationarity equations.
  • Combining quasi-stationarity and averaging for oscillating systems.

Main Results:

  • Robust simplifications of multiscale biochemical networks.
  • Identification of critical model parameters.
  • Demonstration on simple examples and the NF-kappaB pathway model.

Conclusions:

  • The approach yields critical parameter identification and model hierarchies.
  • Hierarchical modeling supports "middle-out" approaches in systems biology.
  • Methods naturally handle multiple time scales common in biological models.