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In this paper, two important computational approaches for modeling gene regulatory networks, PBN and DBN, are compared using a biological time-series dataset from the Drosophila Interaction Database [26] to construct a Drosophila gene network. We present the PBN and DBN approaches and GRN construction methods used and discuss the performance of the two approaches in constructing GRNs.

Results

Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks

Motivation: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes.

Results: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks—a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.

We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty.

We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases.

Then, we discuss the relationship between PBNs and Bayesian networks—a family of graphical models that explicitly represent probabilistic relationships between variables.

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