### 抄録

Inferring regulatory networks in genetic systems and metabolic pathways is one of the most important problems in systems biology. Inferring network structure from experimentally observed time series data is an inverse problem. To deal with such problems, we have developed an efficient numerical optimization method called the hybrid method, which is a combination of real-coded genetic algorithms and the modified Powell method using the S-system representation. In general, a large regulatory network comprises numerous interactive system components and requires the optimization of a large number of parameters with non-zero interaction coefficients between them. To date, we have succeeded in optimizing 272 real-valued parameters using the hybrid method. Although compared with conventional numerical optimization methods, the hybrid method is powerful but is still insufficient for inferring large-scale networks. Here we discuss the inference of interactive large-scale regulatory networks in 'omics' studies based on our hybrid numerical optimization method.

元の言語 | 英語 |
---|---|

ページ（範囲） | 44-52 |

ページ数 | 9 |

ジャーナル | Procedia Computer Science |

巻 | 23 |

DOI | |

出版物ステータス | 出版済み - 1 1 2013 |

イベント | 4th International Conference on Computational Systems-Biology and Bioinformatics, CSBio 2013 - Seoul, 大韓民国 継続期間: 11 7 2013 → 11 9 2013 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computer Science(all)

### これを引用

*Procedia Computer Science*,

*23*, 44-52. https://doi.org/10.1016/j.procs.2013.10.007

**How to infer the interactive large scale regulatory network in 'omic' studies.** / Komori, Asako; Maki, Yukihiro; Ono, Isao; Okamoto, Masahiro.

研究成果: ジャーナルへの寄稿 › Conference article

*Procedia Computer Science*, 巻. 23, pp. 44-52. https://doi.org/10.1016/j.procs.2013.10.007

}

TY - JOUR

T1 - How to infer the interactive large scale regulatory network in 'omic' studies

AU - Komori, Asako

AU - Maki, Yukihiro

AU - Ono, Isao

AU - Okamoto, Masahiro

PY - 2013/1/1

Y1 - 2013/1/1

N2 - Inferring regulatory networks in genetic systems and metabolic pathways is one of the most important problems in systems biology. Inferring network structure from experimentally observed time series data is an inverse problem. To deal with such problems, we have developed an efficient numerical optimization method called the hybrid method, which is a combination of real-coded genetic algorithms and the modified Powell method using the S-system representation. In general, a large regulatory network comprises numerous interactive system components and requires the optimization of a large number of parameters with non-zero interaction coefficients between them. To date, we have succeeded in optimizing 272 real-valued parameters using the hybrid method. Although compared with conventional numerical optimization methods, the hybrid method is powerful but is still insufficient for inferring large-scale networks. Here we discuss the inference of interactive large-scale regulatory networks in 'omics' studies based on our hybrid numerical optimization method.

AB - Inferring regulatory networks in genetic systems and metabolic pathways is one of the most important problems in systems biology. Inferring network structure from experimentally observed time series data is an inverse problem. To deal with such problems, we have developed an efficient numerical optimization method called the hybrid method, which is a combination of real-coded genetic algorithms and the modified Powell method using the S-system representation. In general, a large regulatory network comprises numerous interactive system components and requires the optimization of a large number of parameters with non-zero interaction coefficients between them. To date, we have succeeded in optimizing 272 real-valued parameters using the hybrid method. Although compared with conventional numerical optimization methods, the hybrid method is powerful but is still insufficient for inferring large-scale networks. Here we discuss the inference of interactive large-scale regulatory networks in 'omics' studies based on our hybrid numerical optimization method.

UR - http://www.scopus.com/inward/record.url?scp=84896912171&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84896912171&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2013.10.007

DO - 10.1016/j.procs.2013.10.007

M3 - Conference article

VL - 23

SP - 44

EP - 52

JO - Procedia Computer Science

JF - Procedia Computer Science

SN - 1877-0509

ER -