title: Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data.
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.
authors.affiliation: Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. masao@ims.u-tokyo.ac.jp
authors.forename: Masao
authors.initials: M
authors.lastname: Nagasaki
authors.forename: Rui
authors.initials: R
authors.lastname: Yamaguchi
authors.forename: Ryo
authors.initials: R
authors.lastname: Yoshida
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.forename: Atsushi
authors.initials: A
authors.lastname: Doi
authors.forename: Yoshinori
authors.initials: Y
authors.lastname: Tamada
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Matsuno
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
authors.forename: Tomoyuki
authors.initials: T
authors.lastname: Higuchi
mesh_terms: D001499:Bayes Theorem; D002940:Circadian Rhythm / Q000235:genetics; D019295:Computational Biology; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D023281:Genomics; D008956:Models, Chemical; D008957:Models, Genetic; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D015398:Signal Transduction / Q000235:genetics