Representing and reasoning about signal networks: an illustration using NFκB dependent signaling pathways

We propose a formal language to represent and reason about signal transduction networks. The existing approaches such as ones based on Petri nets, and π-calculus fall short in many ways and our work suggests that an artificial intelligence (AI) based approach may be well suited for many aspects. We apply a form of action language to represent and reason about NFκB dependent signaling pathways. Our language supports several essential features of reasoning with signal transduction knowledge, such as: reasoning with partial (or incomplete) knowledge, and reasoning about triggered evolutions of the world and elaboration tolerance. Because of its growing important role in cellular functions, we select NFκB dependent signaling to be our test bed. NFκB is a central mediator of the immune response, and it can regulate stress responses, as well as cell death/survival in several cell types. While many extracellular signals may lead to the activation of NFκB, few related pathways are elucidated. We study the tasks of representation of pathways, reasoning with pathways, explaining observations, and planning to alter the outcomes; and show that all of them can be well formulated in our framework. Thus our work shows that our AI based approach is a good candidate for feasible and practical representation of and reasoning about signal networks.