We are an emerging research group at FIAS, the Frankfurt Institute for Advanced Studies, specialized in theoretical biophysics and high-performance computing. We are part of the recently established Center for Multiscale Modeling in Life Sciences (CMMS).

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 ask: How can cells reliably generate, sense, transmit and process biochemical signals using limited molecular resources implying a high degree of noise? How can tissues and organisms develop accurately and reproducibly based on such noisy biochemical processes? What are the biophysical mechanisms that evolved to grant cells and tissues the ability to control molecular noise for reliable information processing? How do spatial effects and processes, such as molecular diffusion and active transport on the cytoskeleton, impact on these mechanisms? And, finally, can we port our mechanistic understanding of noise control inside organisms to elucidate spatial-stochastic processes on the multi-organism / population scale?

To address these questions, 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 occurance 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 controling biological noise. Our approach thus tightly combines theoretical derivations and high-performance computing methods with optimization techniques.

Students and young researchers interested in physics-driven computational approaches for studying the robustness of biological systems are warmly welcome to contact us for potential projects or internships.

Please also check our teaching activities at Goethe University for interesting courses and seminars in biophysics, computational physics and scientific programming.