Formalization and automated reasoning about a complex signalling network

Tran and Baral have proposed an action language (BioSigNet-RR) that is specific for the modeling of signalling networks from Biology and for answering queries relative to the expected response to a stimulus. Translation of their action language to logic programs under Answer Set semantics yields a reasoning mechanisms that gracefully handles incomplete/partial information, updates etc. Those features are extremely important since existing regulatory networks often contain missing or suspected interaction links, or proven interactions whose outputs are uncertain. We present our application experience in developing a BioSigNet-RR formalization of the Signalling network for Arabidopsis Brassinosteroid, a complex interaction that is at the base of growth in some plant species. Such modeling exercise has involved 'filling the gaps' between the terse graphical language of signalling networks literature and the precise specification of the triggering conditions required by BioSigNet-RR. This application experience leads us to propose a new formalization style for action theories representing signaling networks that allows for the description of non-immediate effects of actions. Empirical evaluation of our declarative model has involved formulating and testing several 'what if' queries and checking the quality of the answer with domain experts