Bayesian integration of biological prior knowledge into the reconstruction of gene regulatory networks with Bayesian networks.

There have been various attempts to improve the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach is based on pioneering work by Imoto et al., where the prior knowledge is expressed in terms of energy functions, from which a prior distribution over network structures is obtained in the form of a Gibbs distribution. The hyperparameters of this distribution represent the weights associated with the prior knowledge relative to the data. To complement the work of Imoto et al., we have derived and tested an MCMC scheme for sampling networks and hyperparameters simultaneously from the posterior distribution. We have assessed the viability of this approach by reconstructing the RAF pathway from cytometry protein concentrations and prior knowledge from KEGG.

[1]  K. Sachs,et al.  Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.

[2]  David Heckerman,et al.  Learning Gaussian Networks , 1994, UAI.

[3]  E. Davidson,et al.  Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene. , 1998, Science.

[4]  Satoru Miyano,et al.  Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks , 2004, J. Bioinform. Comput. Biol..

[5]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[6]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[7]  Bruce E. Shapiro,et al.  An enzyme mechanism language for the mathematical modeling of metabolic pathways , 2005, Bioinform..

[8]  中尾 光輝,et al.  KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース) , 2000 .

[9]  Marco Grzegorczyk,et al.  Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks , 2006, Bioinform..

[10]  Ming Zhou,et al.  Regulation of Raf-1 by direct feedback phosphorylation. , 2005, Molecular cell.

[11]  Satoru Miyano,et al.  Error tolerant model for incorporating biological knowledge with expression data in estimating gene networks , 2006 .

[12]  Satoru Miyano,et al.  Utilizing Evolutionary Information and Gene Expression Data for Estimating Gene Networks with Bayesian Network Models , 2005, J. Bioinform. Comput. Biol..

[13]  Tommi S. Jaakkola,et al.  Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks , 2000, Pacific Symposium on Biocomputing.

[14]  Nir Friedman,et al.  Being Bayesian about Network Structure , 2000, UAI.

[15]  B. Carlin,et al.  Markov Chain Monte Carlo conver-gence diagnostics: a comparative review , 1996 .

[16]  Satoru Miyano,et al.  Using Protein-Protein Interactions for Refining Gene Networks Estimated from Microarray Data by Bayesian Networks , 2003, Pacific Symposium on Biocomputing.

[17]  E. Davidson,et al.  Cis-regulatory logic in the endo16 gene: switching from a specification to a differentiation mode of control. , 2001, Development.

[18]  Satoru Miyano,et al.  Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection , 2003, ECCB.

[19]  Dirk Husmeier,et al.  Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..

[20]  Paul J. Krause,et al.  Learning probabilistic networks , 1999, The Knowledge Engineering Review.

[21]  M. Kanehisa.  A database for post-genome analysis. , 1997, Trends in genetics : TIG.

[22]  J. York,et al.  Bayesian Graphical Models for Discrete Data , 1995 .

[23]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[24]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1998, Learning in Graphical Models.

[25]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[26]  Lorenz Wernisch,et al.  Reconstruction of gene networks using Bayesian learning and manipulation experiments , 2004, Bioinform..