Dynamic modeling of genetic networks using genetic algorithm and S-system
Dynamic modeling of genetic networks using genetic algorithm and S-system
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DOI：
10.1093/bioinformatics/btg027
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发表时间：
2003-03-22
期刊：
影响因子：
5.8
通讯作者：
Tomita, M
中科院分区：
文献类型：
--
作者：
Kikuchi, S;Tominaga, D;Tomita, M
动机：从时间过程数据对遗传网络、代谢网络或信号转导级联的系统动力学建模是一个反向问题。以往的研究主要集中在对网络结构的估计上，对于具有反馈回路的网络结构的推断是无效的。我们之前提出了一种使用遗传算法（GA）和s系统形式化来预测网络结构和动态的方法。然而，它只能预测少量的参数，很少能得到必要的结构。在这项工作中，我们提出了一种基本方法的统一推广。值得注意的改进如下：(1)在其评价函数中增加了一项，旨在消除无用参数；(2)采用单纯形交叉法（Simplex crossover， SPX）提高交叉算法的优化能力；(3)逐步优化策略，增加可预测参数的数量。结果：所提出的方法被实现为一个名为PEACE1 （Predictor by Evolutionary Algorithms and Canonical Equations 1）的C程序。并与基本方法进行了性能比较。对比表明：(1)收敛速度提高了约5倍；(2)优化速度提高约1.5倍；(3)可预测参数的数量增加了约5倍。此外，我们仅利用基因表达的时间过程数据，成功地推断了一个由5个网络变量和反馈回路的60个参数组成的小型遗传网络的动态。
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.