## 抄録

The aim of this paper is to demonstrate the potential power of large-scale particle filtering for the parameter estimations of in silico biological pathways where time course measurements of biochemical reactions are observable. The method of particle filtering has been a popular technique in the field of statistical science, which approximates posterior distributions of model parameters of dynamic system by using sequentially-generated Monte Carlo samples. In order to apply the particle filtering to system identifications of biological pathways, it is often needed to explore the posterior distributions which are defined over an exceedingly high-dimensional parameter space. It is then essential to use a fairly large amount of Monte Carlo samples to obtain an approximation with a high-degree of accuracy. In this paper, we address some implementation issues on large-scale particle filtering, and then, indicate the importance of large-scale computing for parameter learning of in silico biological pathways. We have tested the ability of the particle filtering with 10^{8} Monte Carlo samples on the transcription circuit of circadian clock that contains 45 unknown kinetic parameters. The proposed approach could reveal clearly the shape of the posterior distributions over the 45 dimensional parameter space.

本文言語 | English |
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ホスト出版物のタイトル | Pacific Symposium on Biocomputing 2009, PSB 2009 |

ページ | 227-238 |

ページ数 | 12 |

出版ステータス | Published - 2009 12 1 |

外部発表 | はい |

イベント | 14th Pacific Symposium on Biocomputing, PSB 2009 - Kohala Coast, HI, United States 継続期間: 2009 1 5 → 2009 1 9 |

### Other

Other | 14th Pacific Symposium on Biocomputing, PSB 2009 |
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Country | United States |

City | Kohala Coast, HI |

Period | 09/1/5 → 09/1/9 |

## ASJC Scopus subject areas

- Computational Theory and Mathematics
- Biomedical Engineering
- Medicine(all)