Gene Regulatory Network Reconstruction by Bayesian Integration of Prior Knowledge and/or Different Experimental Conditions

Abstract

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. We have derived and tested a Markov chain Monte Carlo (MCMC) scheme for sampling networks and hyperparameters simultaneously from the posterior distribution, thereby automatically learning how to trade off information from the prior knowledge and the data. We have extended this approach to a Bayesian coupling scheme for learning gene regulatory networks from a combination of related data sets, which were obtained under different experimental conditions and are therefore potentially associated with different active subpathways. The proposed coupling scheme is a compromise between (1) learning networks from the different subsets separately, whereby no information between the different experiments is shared; and (2) learning networks from a monolithic fusion of the individual data sets, which does not provide any mechanism for uncovering differences between the network structures associated with the different experimental conditions. We have assessed the viability of all proposed methods on data related to the Raf signaling pathway, generated both synthetically and in cytometry experiments.

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

“…Our method could be further extended, for example by implementing an information sharing scheme based on a latent network that represents the commonalities between the inferred network, as was done in Werhli and Husmeier (2008). 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.…”

Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks

“…For that reason we have constrained the network structure to remain invariant and only allow the interaction parameters to change. As a direction for future research one might consider the implementation of a hybrid scheme with a soft rather than hard constraint on the network structures, based on the hierarchical Bayesian model proposed in [14].…”

Non-homogeneous dynamic Bayesian networks for continuous data

“…The aim is to integrate four gene expression time series, which differed with respect to the pre-experiment entrainment condition; see Table 3 for details. The ideal approach would be to use a supervised approach, as described in Werhli and Husmeier (2008), and use the knowledge we have about the experimental conditions for data segmentation. However, we elected to use these data as a test case for evaluating the efficiency of the proposed cpBGe model.…”