Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks

This paper demonstrates the use of qualitative probabilistic networks (QPNs) to aid Dynamic Bayesian Networks (DBNs) in the process of learning the structure of gene regulatory networks from microarray gene expression data. We present a study which shows that QPNs define monotonic relations that are capable of identifying regulatory interactions in a manner that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a model that maps the regulatory interactions of genetic networks to QPN constructs and show its capability in providing a set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning algorithm can use and which 1) distinguishes spurious correlations from true regulations, 2) enables the discovery of sets of coregulators of target genes, and 3) results in a more efficient construction of gene regulatory networks. The model is compared to the existing literature using the known gene regulatory interactions of Drosophila Melanogaster.