Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks

  • Daniel Marbach
  • Claudio Mattiussi
  • Dario Floreano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4447)


The effective reverse engineering of biochemical networks is one of the great challenges of systems biology. The contribution of this paper is two-fold: 1) We introduce a new method for reverse engineering genetic regulatory networks from gene expression data; 2) We demonstrate how nonlinear gene networks can be inferred from steady-state data alone. The reverse engineering method is based on an evolutionary algorithm that employs a novel representation called Analog Genetic Encoding (AGE), which is inspired from the natural encoding of genetic regulatory networks. AGE can be used with biologically plausible, nonlinear gene models where analytical approaches or local gradient based optimisation methods often fail. Recently there has been increasing interest in reverse engineering linear gene networks from steady-state data. Here we demonstrate how more accurate nonlinear dynamical models can also be inferred from steady-state data alone.


Systems Biology Gene Networks Reverse Engineering Steady-State Data Genetic Algorithm Analog Genetic Encoding (AGE). 


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Daniel Marbach
    • 1
  • Claudio Mattiussi
    • 1
  • Dario Floreano
    • 1
  1. 1.Ecole Polytechnique Fédérale de Lausanne (EPFL), Laboratory of Intelligent Systems, CH-1015 LausanneSwitzerland

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