Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge

Abstract

There have been various attempts to reconstruct gene regulatory networks from microarray expression data in the past. However, owing to the limited amount of independent experimental conditions and noise inherent in the measurements, the results have been rather modest so far. For this reason it seems advisable to include biological prior knowledge, related, for instance, to transcription factor binding locations in promoter regions or partially known signalling pathways from the literature. In the present paper, we consider a Bayesian approach to systematically integrate expression data with multiple sources of prior knowledge. Each source is encoded via a separate energy function, from which a prior distribution over network structures in the form of a Gibbs distribution is constructed. The hyperparameters associated with the different sources of prior knowledge, which measure the influence of the respective prior relative to the data, are sampled from the posterior distribution with MCMC. We have evaluated the proposed scheme on the yeast cell cycle and the Raf signalling pathway. Our findings quantify to what extent the inclusion of independent prior knowledge improves the network reconstruction accuracy, and the values of the hyperparameters inferred with the proposed scheme were found to be close to optimal with respect to minimizing the reconstruction error.

Assessing the Validity Domains of Graphical Gaussian Models in Order to Infer Relationships among Components of Complex Biological Systems

“…Other methods, such as kernelbased methods (Okamoto et al, 2007;Yellaboina et al, 2007) imply a learning phase and so need a training data set. Bayesian approaches are also used to infer relations between biological entities in order to understand the regulatory mechanisms of living cells (Husmeier, 2003;Werhli & Husmeier, 2007). However these methods have to deal with the prior probability that has a non-negligible influence on the posterior probability when data are sparse and noisy.…”

Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series

“…One could also consider using prior biological information about gene interactions in the model (such as in Werhli and Husmeier (2007)), which may help identify new, previously unknown interactions.…”

Integrating biological information into the statistical analysis and design of microarray experiments

“…For example, Imoto et al (2003) and Werhli and Husmeier (2007) exploit multiple sources of prior knowledge for gene regulatory network reconstruction; and Huang and Pan (2006), Pan (2006a) and Tseng (2007 propose biologically informed gene clustering techniques. However, not much research has been published regarding the incorporation of prior knowledge into the analysis of differential expression, one exception being Pan (2006b), who uses gene functional annotation for improving detection of differentially expressed genes.…”