Signal transduction networks play a major role in ensuring normal function of living cells, and constitute a strong research focus in fields ranging from biotechnology to medicine. In this project, a novel modeling method rooting in logic-based approaches and specifically tailored to signaling networks will be developed in close cooperation with life scientists from plant sciences and medical research. Building upon ideas from network modularization, it will be highly suited to discovering and analyzing subnetworks crucial for regulatory effects, stability and perturbation processing. The project will advance our grasp of the signaling mechanisms of interest to our cooperation partners, namely cytokinin signaling in plants as well as oncogenic RAS/RAF signaling in humans, and supply modeling tools capable of supporting signal transduction research in all areas of systems biology.
Mathematical modelling in biological and medical applications is almost always faced with the problem of incomplete and noisy data. Rather than adding unsupported assumptions to obtain a unique model, a different approach generates a pool of models in agreement with all available observations. Analysis and classification of such models allow linking the constraints imposed by the data to essential model characteristics and showcase different implementations of key mechanisms. Within the project, we aim at combining the advantages of logical and continuous modeling to arrive at a comprehensive system analysis under data uncertainty. Model classification will integrate qualitative aspects such as characteristics of the network topology with more quantitative information extracted from clustering of joint parameter distributions derived from Bayesian approaches. The theory development is accompanied by and tested in application to oncogenic signaling networks.