Cost-Based Abduction (CBA) is an AI model for reasoning under uncertainty. In CBA, evidence to be explained is treated as a goal which is true and must be proven. Each proof of the goal is viewed as a feasible explanation and has a cost equal to the sum of the costs of all hypotheses that are assumed to complete the proof. The aim is to find the Least Cost Proof. This paper uses CBA to develop a novel method for modeling Genetic Regulatory Networks (GRN) and explaining genetic knock-out effects. Constructing GRN using multiple data sources is a fundamental problem in computational biology. We show that CBA is a powerful formalism for modeling GRN that can easily and effectively integrate multiple sources of biological data. In this paper, we use three different biological data sources: Protein-DNA, Protein–Protein and gene knock-out data. Using this data, we first create an un-annotated graph; CBA then annotates the graph by assigning a sign and a direction to each edge. Our biological results are promising; however, this manuscript focuses on the mathematical modeling of the application. The advantages of CBA and its relation to Bayesian inference are also presented.