Bioinformatics Vol. 19 no. 5 2003
Pages 643-650
© 2003 Oxford University Press
Dynamic modeling of genetic networks using genetic algorithm and S-system
1 Computational Biology Research Center
(CBRC), National Institute of Advanced Industrial Science and
Technology (AIST), 2-41-6 Aomi, Koto-ku, Tokyo, 135-0064, Japan
2 Institute for Advanced Biosciences, Keio
University, 14-1, Baba, Tsuruoka, Yamagata, 997-0035, Japan
Received on June 5, 2002
; revised on September 20, 2002
; accepted on September 28, 2002
Motivation: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters.
Results: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.
Contact: kikuchi{at}sfc.keio.ac.jp
* To whom correspondence should be addressed.
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