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A Hybrid Approach to Piecewise Modelling of Biochemical Systems

  • Zujian Wu
  • Shengxiang Yang
  • David Gilbert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7491)

Abstract

Modelling biochemical systems has received considerable attention over the last decade from scientists and engineers across a number of fields, including biochemistry, computer science, and mathematics. Due to the complexity of biochemical systems, it is natural to construct models of the biochemical systems incrementally in a piecewise manner. This paper proposes a hybrid approach which applies an evolutionary algorithm to select and compose pre-defined building blocks from a library of atomic models, mutating their products, thus generating complex systems in terms of topology, and employs a global optimization algorithm to fit the kinetic rates. Experiments using two signalling pathways show that given target behaviours it is feasible to explore the model space by this hybrid approach, generating a set of synthetic models with alternative structures and similar behaviours to the desired ones.

Keywords

Hybrid Approach Extracellular Signal Regulate Kinase Kinetic Rate MAPK Cascade Raf1 Kinase Inhibitor Protein 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zujian Wu
    • 1
  • Shengxiang Yang
    • 1
  • David Gilbert
    • 1
  1. 1.School of Information Systems, Computing and MathematicsBrunel UniversityUxbridgeUK

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