Functional Significance Checking in Noisy Gene Regulatory Networks

Finding gene regulatory pathways that explain outcomes of wet-lab experiments is one of the holy grails of systems biology. SAT-solving techniques have been used in the past to find few small explanatory pathways assuming either zero or a few known perturbations in the experimental observations. Unfortunately, these approaches do not work when (i) there is noise in the experimental data or domain knowledge, as opposed to known perturbations, and (ii) the number of possible pathways generated by repeatedly invoking a SAT-solver is too large to be analyzed by enumeration. In such settings, determining if an actor plays a functionally significant role towards explaining experimental observations is very difficult using existing SAT-based techniques.