Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data.

Masao Nagasaki, Rui Yamaguchi, Ryo Yoshida, Seiya Imoto, Atsushi Doi, Yoshinori Tamada, Hiroshi Matsuno, Satoru Miyano, Tomoyuki Higuchi

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)46-61
Number of pages16
JournalGenome informatics. International Conference on Genome Informatics
Issue number1
Publication statusPublished - 2006 Jan 1
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)


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