Genomic Data Assimilation for Estimating Hybrid Functional Petri Net from Time-Course Gene Expression Data

• Nagasaki Masao

  Human Genome Center, Institute of Medical Science, University of Tokyo

• Yamaguchi Rui

  Human Genome Center, Institute of Medical Science, University of Tokyo

• Yoshida Ryo

  Human Genome Center, Institute of Medical Science, University of Tokyo

• Imoto Seiya

  Human Genome Center, Institute of Medical Science, University of Tokyo

• Doi Atsushi

  Human Genome Center, Institute of Medical Science, University of Tokyo

• Tamada Yoshinori

  Institute of Statistical Mathematics Japan Science Technology Agency

• Matsuno Hiroshi

  Faculty of Science, Yamaguchi University

• Miyano Satoru

  Human Genome Center, Institute of Medical Science, University of Tokyo

• Higuchi Tomoyuki

  Institute of Statistical Mathematics Japan Science Technology Agency

Abstract

We propose an automatic construction method of the hybrid functional Petri net as a simulation model of biological pathways. The problems we consider are how we choose the values of parameters and how we set the network structure. Usually, we tune these unknown factors empirically so that the simulation results are consistent with biological knowledge. Obviously, this approach has the limitation in the size of network of interest. To extend the capability of the simulation model, we propose the use of data assimilation approach that was originally established in the field of geophysical simulation science. We provide genomic data assimilation framework that establishes a link between our simulation model and observed data like microarray gene expression data by using a nonlinear state space model. A key idea of our genomic data assimilation is that the unknown parameters in simulation model are converted as the parameter of the state space model and the estimates are obtained as the maximum a posteriori estimators. In the parameter estimation process, the simulation model is used to generate the system model in the state space model. Such a formulation enables us to handle both the model construction and the parameter tuning within a framework of the Bayesian statistical inferences. In particular, the Bayesian approach provides us a way of controlling overfitting during the parameter estimations that is essential for constructing a reliable biological pathway. We demonstrate the effectiveness of our approach using synthetic data. As a result, parameter estimation using genomic data assimilation works very well and the network structure is suitably selected.

Journal

• Genome Informatics

  Genome Informatics 17 (1), 46-61, 2006

  Japanese Society for Bioinformatics

Keywords

• data assimilation
• hybrid functional Petri net
• state space model
• particle filter
• circadian rhythm

Details

• CRID: 1390001204488379392
• NII Article ID: 130003812105
• DOI: 10.11234/gi1990.17.46
• ISSN: 2185842X
• ISSN: 09199454
• Text Lang: en
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed