Enhancing Parameter Estimation of Biochemical Networks by Exponentially Scaled Search Steps

  • Hendrik Rohn
  • Bashar Ibrahim
  • Thorsten Lenser
  • Thomas Hinze
  • Peter Dittrich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4973)


A fundamental problem of modelling in Systems Biology is to precisely characterise quantitative parameters, which are hard to measure experimentally. For this reason, it is common practise to estimate these parameter values, using evolutionary and other techniques, by fitting the model behaviour to given data. In this contribution, we extensively investigate the influence of exponentially scaled search steps on the performance of two evolutionary and one deterministic technique; namely CMA-Evolution Strategy, Differential Evolution, and the Hooke-Jeeves algorithm, respectively. We find that in most test cases, exponential scaling of search steps significantly improves the search performance for all three methods.


Search Point Search Step Biochemical Network System Biology Markup Language Physarum Polycephalum 
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 2008

Authors and Affiliations

  • Hendrik Rohn
    • 1
  • Bashar Ibrahim
    • 1
  • Thorsten Lenser
    • 1
  • Thomas Hinze
    • 1
  • Peter Dittrich
    • 1
  1. 1.Bio Systems Analysis GroupJena Centre for Bioinformatics and Friedrich Schiller University JenaJenaGermany

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