Synthesis of Boolean Networks with Weak and Strong Regulators

Noy Biton, Sharon Shoob, Ani Amar, Hillel Kugler

Abstract: Computational modeling of gene regulation processes is a gainful approach in combination with experimental research to elucidate mechanistic behavior and understand the dynamic nature of cell signaling events. Previously, automatic synthesis methods have been developed that consider a large set of Boolean networks and identify solutions that are consistent with all experimental data. A method and tool termed the Reasoning Engine (RE:IN) allows synthesis from Abstract Boolean Networks (ABN) that can represent unknowns in the network topologies and the logical regulation conditions and synthesize Boolean networks that are consistent with experimental measurements. However, the regulation conditions considered in the Reasoning Engine synthesis methods were restricted and potentially limited in their ability to address various types of regulation, giving equal weight to each of the regulators, thus not being able to distinguish between a strong and a weak regulator. We develop and present an extension of the formal reasoning framework by distinguishing between strong and weak regulators for a more realistic representation of gene regulation processes. By capturing the relative strength of activators and repressors, we show how the synthesized Boolean networks can be extended to include additional qualitative information on relationships between regulators. We apply our analysis to a model for control of the mammalian cell cycle, thus demonstrating the feasibility and utility of the method.