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Reverse engineering of metabolic pathways from observed data using genetic programming.

J R Koza1, W Mydlowec, G Lanza

  • 1Department of Medicine, Department of Electrical Engineering, Stanford University, Stanford, California, USA. koza@stanford.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|March 27, 2001
PubMed
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This study shows genetic programming can reverse engineer chemical reaction networks from time-domain data. It automatically creates reaction pathways and rates, matching observed product concentrations.

Area of Science:

  • Computational Biology and Systems Chemistry
  • Artificial Intelligence in Scientific Discovery

Background:

  • Complex biological and chemical systems are often modeled using non-linear continuous-time differential equations.
  • Previous work utilized genetic programming to design networks like electrical circuits and controllers.
  • The application of genetic programming to reverse engineer chemical reaction networks from data was unexplored.

Purpose of the Study:

  • To demonstrate the capability of genetic programming to automatically create (reverse engineer) chemical reaction networks.
  • To validate this approach using observed time-domain concentration data.
  • To identify specific metabolic pathways using this computational method.

Main Methods:

  • Genetic programming was employed, starting with time-domain concentration data of input substances.

Related Experiment Videos

  • The algorithm automatically evolved both the network topology (structure of reactions) and reaction rate constants.
  • The objective was to match the predicted product concentration time-domain data to the observed data.

    • Main Results:

    • Genetic programming successfully reverse engineered chemical reaction networks from time-domain data.
    • The created networks accurately reproduced the observed concentration profiles of the final product.
    • Specifically, metabolic pathways for phospholipid cycling and ketone body metabolism were automatically elucidated.

    Conclusions:

    • Genetic programming is a viable tool for reverse engineering complex chemical reaction networks.
    • This method can automatically discover biological pathways and reaction kinetics from experimental data.
    • The approach holds promise for accelerating systems biology and metabolic engineering research.