Computational Systems Biology aims at the construction and analysis of predictive mathematical models for the description of complex interactions in biological systems. These systems are often modelled with ordinary differential equations (ODEs) that describe the concentrations of substances over time. Such models are characterized by a large number of variables, parameters and constraints, which requires the application of efficient numerical algorithms and computational techniques for high-dimensional problems.

Experimental measurement data in biology are usually sparse and noisy. Therefore, we combine local optimization methods for parameter identification with new algorithmic approaches to Bayesian inverse problems. Algorithms are implemented in our software package BioPARKIN, that has especially been designed to meet the needs of modellers in systems biology.

Applications range from cellular signalling pathways to physiological processes on a whole-organism level, for example endocrinological networks, and also include classical reaction kinetics. Due to the multi-scale nature of biological systems, we increasingly consider hybrid models that combine different mathematical formalisms, e.g. ODEs for metabolic networks with discrete dynamical systems for regulatory processes, or ODEs for signaling pathways with partial differential equations describing biomechanical processes.

Our work is highly interdisciplinary. Therefore we are actively establishing academic and industrial collaborations with different partners to validate and to improve our models.