### 抄録

In a mathematical model, estimation of parameters from time-series data of metabolic concentrations in cells is a challenging task. However, it seems that a promising approach for such estimation has not yet been established. Biochemical Systems Theory (BST) is a powerful methodology to construct a power-law type model for a given metabolic reaction system and to then characterize it efficiently. In this paper, we discuss the use of an S-system root-finding method (S-system method) to estimate parameters from time-series data of metabolite concentrations. We demonstrate that the S-system method is superior to the Newton–Raphson method in terms of the convergence region and iteration number. We also investigate the usefulness of a translocation technique and a complex-step differentiation method toward the practical application of the S-system method. The results indicate that the S-system method is useful to construct mathematical models for a variety of metabolic reaction networks.

元の言語 | 英語 |
---|---|

ページ（範囲） | 21-31 |

ページ数 | 11 |

ジャーナル | Mathematical Biosciences |

巻 | 301 |

DOI | |

出版物ステータス | 出版済み - 7 1 2018 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Statistics and Probability
- Modelling and Simulation
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Agricultural and Biological Sciences(all)
- Applied Mathematics

### これを引用

*Mathematical Biosciences*,

*301*, 21-31. https://doi.org/10.1016/j.mbs.2018.01.010

**Evaluation of an S-system root-finding method for estimating parameters in a metabolic reaction model.** / Iwata, Michio; Miyawaki-Kuwakado, Atsuko; Yoshida, Erika; Komori, Soichiro; Shiraishi, Fumihide.

研究成果: ジャーナルへの寄稿 › 記事

*Mathematical Biosciences*, 巻. 301, pp. 21-31. https://doi.org/10.1016/j.mbs.2018.01.010

}

TY - JOUR

T1 - Evaluation of an S-system root-finding method for estimating parameters in a metabolic reaction model

AU - Iwata, Michio

AU - Miyawaki-Kuwakado, Atsuko

AU - Yoshida, Erika

AU - Komori, Soichiro

AU - Shiraishi, Fumihide

PY - 2018/7/1

Y1 - 2018/7/1

N2 - In a mathematical model, estimation of parameters from time-series data of metabolic concentrations in cells is a challenging task. However, it seems that a promising approach for such estimation has not yet been established. Biochemical Systems Theory (BST) is a powerful methodology to construct a power-law type model for a given metabolic reaction system and to then characterize it efficiently. In this paper, we discuss the use of an S-system root-finding method (S-system method) to estimate parameters from time-series data of metabolite concentrations. We demonstrate that the S-system method is superior to the Newton–Raphson method in terms of the convergence region and iteration number. We also investigate the usefulness of a translocation technique and a complex-step differentiation method toward the practical application of the S-system method. The results indicate that the S-system method is useful to construct mathematical models for a variety of metabolic reaction networks.

AB - In a mathematical model, estimation of parameters from time-series data of metabolic concentrations in cells is a challenging task. However, it seems that a promising approach for such estimation has not yet been established. Biochemical Systems Theory (BST) is a powerful methodology to construct a power-law type model for a given metabolic reaction system and to then characterize it efficiently. In this paper, we discuss the use of an S-system root-finding method (S-system method) to estimate parameters from time-series data of metabolite concentrations. We demonstrate that the S-system method is superior to the Newton–Raphson method in terms of the convergence region and iteration number. We also investigate the usefulness of a translocation technique and a complex-step differentiation method toward the practical application of the S-system method. The results indicate that the S-system method is useful to construct mathematical models for a variety of metabolic reaction networks.

UR - http://www.scopus.com/inward/record.url?scp=85044366891&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044366891&partnerID=8YFLogxK

U2 - 10.1016/j.mbs.2018.01.010

DO - 10.1016/j.mbs.2018.01.010

M3 - Article

C2 - 29410225

AN - SCOPUS:85044366891

VL - 301

SP - 21

EP - 31

JO - Mathematical Biosciences

JF - Mathematical Biosciences

SN - 0025-5564

ER -