Parameter estimation of in silico biological pathways with particle filtering towards a petascale computing

Kazuyuki Nakamura, Ryo Yoshida, Masao Nagasaki, Satoru Miyano, Tomoyuki Higuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)


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 108 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.

Original languageEnglish
Title of host publicationPacific Symposium on Biocomputing 2009, PSB 2009
Number of pages12
Publication statusPublished - 2009 Dec 1
Externally publishedYes
Event14th Pacific Symposium on Biocomputing, PSB 2009 - Kohala Coast, HI, United States
Duration: 2009 Jan 52009 Jan 9


Other14th Pacific Symposium on Biocomputing, PSB 2009
CountryUnited States
CityKohala Coast, HI

ASJC Scopus subject areas

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

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