Our research aims at elucidating biophysical principles and mechanisms enabling reliable information processing in biological systems. In particular, we focus on biochemical pathways transmitting and processing information inside and between biological cells. These are based on intrinsically stochastic gene regulation processes which operate with limited numbers of molecules and thus render cellular information processing very noisy.

We employ methods from statistical and computational physics, with a particular focus on spatial-stochastic simulations that are both biophysically realistic and efficient. At the core of our approach is the development of event-driven spatial-stochastic algorithms, which attain high computational efficiency by employing probability distributions for the occurrence of relevant future events. For making accurate predictions, the mathematical derivation of these distributions has to be thoroughly based on biophysical and biochemical principles. On top, we use numerical optimization techniques to explore the rich parameter space of our models in order to find solutions that are most effective in controlling biological noise. Our approach thus tightly combines theoretical derivations and high-performance computing methods with optimization techniques. 

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