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.



Apr 2022

Michael Ramírez Sierra is invited to act as a junior guest lecturer at the 2022 UQ-Bio Summer School in Fort Collins, Colorado. He will also present our work on early mouse embryo development at the Q-Bio Conference.


Feb 2022

Mirjam Schulz (MSc in Biophysics, Goethe University Frankfurt) joins the group for several months for a scientific project. She will work on elucidating how smart spacing of transcription factor binding sites can optimize the association rate of transcription factors.


Oct / Nov 2021

Thomas Sokolowski teaches the course "Introduction to Data Analysis and Simulation with MATLAB" at Goethe-University. Enlistment is possible until beginning of November. Please check the "Teaching" page for details.


Sep 29 - Oct 01, 2021

Michael Ramírez Sierra und Thomas Sokolowski give talks at the "Cell Physics 2021" conference at Saarland University in Saarbrücken.


Jul 09 - 11, 2021

We participate in a scientific mini-symposium and all-day hike in the Taunus within an event linking five emerging research groups in the Rhein-Main area.


May 2021

Michael Ramírez Sierra is awarded one of the rare yearly participation slots at this year's (virtual) q-bio summer school. Congratulations Michael!


Mar 02, 2021

A short movie describing the research of our and three more newly established research groups at FIAS is now available on the FIAS YouTube Channel.


Feb 15, 2021

Together with collaborators from IST Austria we published a paper in Neuron, one of the most influential neuroscience journals. In this work we develop a generic Bayesian framework that allows the usage of "optimization priors" to aid statistical inference, and to quantitatively interpolate between a data-rich inference regime and a data-limited prediction regime.


Sep 01, 2020

Michael Alexander Ramírez Sierra joins the group as its first PhD student. He will work on elucidating mechanisms of robust tissue development, using event-driven spatial-stochastic simulations.


Apr 01, 2020

Thomas Sokolowski joins FIAS as a fellow and new independent group leader as part of the newly established Center for Multiscale Modeling in Life Sciences (CMMS).

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