### 抄録

Mathematical modeling of large-scale metabolic networks usually requires smoothing of metabolite time-series data to account for measurement or biological errors. Accordingly, the accuracy of smoothing curves strongly affects the subsequent estimation of model parameters. Here, an efficient parametric method is proposed for smoothing metabolite time-series data, and its performance is evaluated. To simplify parameter estimation, the method uses S-system-type equations with simple power law-type efflux terms. Iterative calculation using this method was found to readily converge, because parameters are estimated stepwise. Importantly, smoothing curves are determined so that metabolite concentrations satisfy mass balances. Furthermore, the slopes of smoothing curves are useful in estimating parameters, because they are probably close to their true behaviors regardless of errors that may be present in the actual data. Finally, calculations for each differential equation were found to converge in much less than one second if initial parameters are set at appropriate (guessed) values.

元の言語 | 英語 |
---|---|

ページ（範囲） | 21-33 |

ページ数 | 13 |

ジャーナル | Mathematical Biosciences |

巻 | 282 |

DOI | |

出版物ステータス | 出版済み - 12 1 2016 |

### 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*,

*282*, 21-33. https://doi.org/10.1016/j.mbs.2016.09.011

**A new parametric method to smooth time-series data of metabolites in metabolic networks.** / Miyawaki, Atsuko; Sriyudthsak, Kansuporn; Hirai, Masami Yokota; Shiraishi, Fumihide.

研究成果: ジャーナルへの寄稿 › 記事

*Mathematical Biosciences*, 巻. 282, pp. 21-33. https://doi.org/10.1016/j.mbs.2016.09.011

}

TY - JOUR

T1 - A new parametric method to smooth time-series data of metabolites in metabolic networks

AU - Miyawaki, Atsuko

AU - Sriyudthsak, Kansuporn

AU - Hirai, Masami Yokota

AU - Shiraishi, Fumihide

PY - 2016/12/1

Y1 - 2016/12/1

N2 - Mathematical modeling of large-scale metabolic networks usually requires smoothing of metabolite time-series data to account for measurement or biological errors. Accordingly, the accuracy of smoothing curves strongly affects the subsequent estimation of model parameters. Here, an efficient parametric method is proposed for smoothing metabolite time-series data, and its performance is evaluated. To simplify parameter estimation, the method uses S-system-type equations with simple power law-type efflux terms. Iterative calculation using this method was found to readily converge, because parameters are estimated stepwise. Importantly, smoothing curves are determined so that metabolite concentrations satisfy mass balances. Furthermore, the slopes of smoothing curves are useful in estimating parameters, because they are probably close to their true behaviors regardless of errors that may be present in the actual data. Finally, calculations for each differential equation were found to converge in much less than one second if initial parameters are set at appropriate (guessed) values.

AB - Mathematical modeling of large-scale metabolic networks usually requires smoothing of metabolite time-series data to account for measurement or biological errors. Accordingly, the accuracy of smoothing curves strongly affects the subsequent estimation of model parameters. Here, an efficient parametric method is proposed for smoothing metabolite time-series data, and its performance is evaluated. To simplify parameter estimation, the method uses S-system-type equations with simple power law-type efflux terms. Iterative calculation using this method was found to readily converge, because parameters are estimated stepwise. Importantly, smoothing curves are determined so that metabolite concentrations satisfy mass balances. Furthermore, the slopes of smoothing curves are useful in estimating parameters, because they are probably close to their true behaviors regardless of errors that may be present in the actual data. Finally, calculations for each differential equation were found to converge in much less than one second if initial parameters are set at appropriate (guessed) values.

UR - http://www.scopus.com/inward/record.url?scp=84991252526&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84991252526&partnerID=8YFLogxK

U2 - 10.1016/j.mbs.2016.09.011

DO - 10.1016/j.mbs.2016.09.011

M3 - Article

C2 - 27693302

AN - SCOPUS:84991252526

VL - 282

SP - 21

EP - 33

JO - Mathematical Biosciences

JF - Mathematical Biosciences

SN - 0025-5564

ER -