Learning logic models of signalling networks
Signalling networks regulate how a biological system as a whole responds to external and internal signals. The underlying dynamics of these complex networks is often unknown. Currently, we are developing methods to infer Boolean network models. By optimally explaining experimental data given as stimuli and response sets we uncover the logical functions behind each interaction. The next step will be to suggest those experiments that provide the best information to distinguish between different alternative models.
We build our inference algorithms based on different techniques from mathematical optimization and satisfiability solving. This way, we hope to get more insight into the strengths and weaknesses of each method.