### Abstract

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.

Original language | English |
---|---|

Pages (from-to) | 643-650 |

Number of pages | 8 |

Journal | Bioinformatics |

Volume | 19 |

Issue number | 5 |

DOIs | |

Publication status | Published - 2003 Mar 22 |

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### ASJC Scopus subject areas

- Clinical Biochemistry
- Computer Science Applications
- Computational Theory and Mathematics

### Cite this

*Bioinformatics*,

*19*(5), 643-650. https://doi.org/10.1093/bioinformatics/btg027

**Dynamics modeling of genetic networks using genetic algorithm and S-system.** / Kikuchi, Shinichi; Tominaga, Daisuke; Arita, Masanori; Takahashi, Katsutoshi; Tomita, Masaru.

Research output: Contribution to journal › Article

*Bioinformatics*, vol. 19, no. 5, pp. 643-650. https://doi.org/10.1093/bioinformatics/btg027

}

TY - JOUR

T1 - Dynamics modeling of genetic networks using genetic algorithm and S-system

AU - Kikuchi, Shinichi

AU - Tominaga, Daisuke

AU - Arita, Masanori

AU - Takahashi, Katsutoshi

AU - Tomita, Masaru

PY - 2003/3/22

Y1 - 2003/3/22

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0037461033&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037461033&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btg027

DO - 10.1093/bioinformatics/btg027

M3 - Article

C2 - 12651723

AN - SCOPUS:0037461033

VL - 19

SP - 643

EP - 650

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 5

ER -