title: Bayesian learning of biological pathways on genomic data assimilation.
abstract: BACKGROUND
  Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential first step to find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision.
  
  
  RESULTS
  The aim of our research is to present a new statistical technology towards data-driven construction of in silico biological pathways. The method starts with a knowledge-based modeling with hybrid functional Petri net. It then proceeds to the Bayesian learning of model parameters for which experimental data are available. This process exploits quantitative measurements of evolving biochemical reactions, e.g. gene expression data. Another important issue that we consider is statistical evaluation and comparison of the constructed hypothetical pathways. For this purpose, we have developed a new Bayesian information-theoretic measure that assesses the predictability and the biological robustness of in silico pathways.
  
  
  BACKGROUND
  The FORTRAN source codes are available at the URL http://daweb.ism.ac.jpyoshidar/GDA/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Statistical Mathematics, Research Organization of Information and Systems, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. yoshidar@ism.ac.jp
authors.forename: Ryo
authors.initials: R
authors.lastname: Yoshida
authors.forename: Masao
authors.initials: M
authors.lastname: Nagasaki
authors.forename: Rui
authors.initials: R
authors.lastname: Yamaguchi
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
authors.forename: Tomoyuki
authors.initials: T
authors.lastname: Higuchi
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002940:Circadian Rhythm / Q000235:genetics; D019295:Computational Biology; D016678:Genome / Q000235:genetics*; D008957:Models, Genetic; D012680:Sensitivity and Specificity; D014158:Transcription, Genetic / Q000235:genetics