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.
Systems biology is a lively interdisciplinary research field that has received considerable attention in recent years. While traditional molecular biology studies the various components of a biological system (genes, RNAs, proteins,...) in isolation, systems biology aims to understand how these components interact in order to perform higher-level functions. Mathematical modeling then plays a major role not only in capturing and analyzing complex networks governing biological processes, but also as a way to verify hypotheses based on observational evidence and in designing efficient experiments to further the understanding of the system. The goal of this project is to pool our knowledge on discrete, deterministic and stochastic modeling of biological systems to pave the way towards efficient hybrid models (discrete/continuous, deterministic/stochastic) for elaborate biological networks.
Top-down modeling methods are based on the idea of collecting all known information on a system in a list of constraints. Rather than in one particular model this generally results in a set of models that cannot be further distinguished using the available information. Properties shared by all models in the set can be viewed as strongly supported by the invested information. Determining distinguishing characteristics for model sets can help to identify system traits that need to be clarified by further experiments. Within this project, we employ this approach in the context of logical modeling methods in systems biology.