Inference of regulatory networks with MCMC sampler guided by mutual information

Computationally efficient and exact inference of regulatory network topology is an open problem in System Biology. In this work we investigate the use of prior information about the network topology as a guide to a Markov Chain Monte Carlo sampler of network structures. The prior information is obtained from a coarser and faster network inference method, the Relevance Networks with Mutual Information scores. Moreover, the regulatory networks are represented by the Bayesian Networks model. The results show that the use of prior information drastically improves the convergence of the MCMC sampler. Therefore, the use of a more refined method is justified as it is likely to lead to more reliable results with less MCMC iterations.

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

[2]  Adriano Velasque Werhli,et al.  Inference of regulatory networks with a convergence improved MCMC sampler , 2015, BMC Bioinformatics.

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

[4]  Adriano Velasque Werhli,et al.  Reconstruction of gene regulatory networks from postgenomic data , 2007 .

[5]  Marco Grzegorczyk,et al.  Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move , 2008, Machine Learning.

[6]  Alexander J. Hartemink,et al.  Principled computational methods for the validation discovery of genetic regulatory networks , 2001 .

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

[8]  Mark E. Borsuk,et al.  On Monte Carlo methods for Bayesian inference , 2003 .

[9]  D. Husmeier,et al.  Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge , 2007, Statistical applications in genetics and molecular biology.

[10]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[11]  Nir Friedman,et al.  Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks , 2004, Machine Learning.

[12]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[13]  Marco Grzegorczyk,et al.  Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes , 2011, Bioinform..

[14]  Adrian E. Raftery,et al.  Fast Bayesian inference for gene regulatory networks using ScanBMA , 2014, BMC Systems Biology.

[15]  Dario Floreano,et al.  GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods , 2011, Bioinform..

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

[17]  Xiaobo Guo,et al.  Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation , 2014, PloS one.

[18]  Brian Godsey,et al.  Improved Inference of Gene Regulatory Networks through Integrated Bayesian Clustering and Dynamic Modeling of Time-Course Expression Data , 2013, PloS one.

[19]  Rainer Spang,et al.  Inferring cellular networks – a review , 2007, BMC Bioinformatics.

[20]  Isaac S. Kohane,et al.  Relevance Networks: A First Step Toward Finding Genetic Regulatory Networks Within Microarray Data , 2003 .

[21]  Patrik D'haeseleer,et al.  Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..

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

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

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

[25]  Stephen J. Roberts,et al.  Probabilistic Modeling in Bioinformatics and Medical Informatics , 2010 .

[26]  Bart Deplancke,et al.  Gene Regulatory Networks , 2012, Methods in Molecular Biology.

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

[28]  David Maxwell Chickering,et al.  Large-Sample Learning of Bayesian Networks is NP-Hard , 2002, J. Mach. Learn. Res..