We study the physics of living systems. Our research is guided by the overarching question: how do stochastic interactions of cells and molecules control the functional behaviour of multicellular systems?

Our main focus is to understand how cells collectively make decisions on their future fate. We investigate this question across physical scales from tissues to cells down to the molecular level. At the tissue scale, we develop reaction-diffusion models and information theoretic frameworks to understand collective patterning processes. At the cellular scale, we ask how cells interpret combinatorial and dynamic signals to decide their fate. At the molecular scale, we investigate how the stochastic dynamics of gene regulatory elements implements these decisions.

To tackle these problems, we use a combination of theoretical approaches firmly grounded in statistical and soft matter physics. We develop biophysical models of cell signaling and mechanics to investigate how tissues self-organize. To gain conceptual insights across systems, we analyze these models using information theory, providing a mechanism- and system-independent language to formalize biological function. Using inference and machine learning approaches, we connect our models to quantitative data sets in close collaboration with experimental labs around the world. Please refer to the research tab for more details on ongoing projects.

Starting April 2025, our group will be based at the Biozentrum of the University of Basel, Switzerland. 

We are looking for excellent people to join our group at all levels! 

News

02.07.2024  Our review on integrating the active mechanics and signaling in multicellular systems into dynamical models is now published in Cold Spring Harbour Perspectives in Biology.

19.06.2024  Our paper on cell-cell interactions in migrating trains of cells is out in Nature Physics!

This was a collaboration the experimental labs of Sylvain Gabriele (University of Mons, Belgium) and Xavier Trepat (IBEC Barcelona).

03.06.2024  Our paper on the information content of self-organized pattern formation in developmental systems is now published in PNAS.

04.04.2024  Our review on learning dynamical models of single and collective cell migration is published in Reports on Progress in Physics

We review stochastic inference, machine learning and active matter approaches to understand the dynamics of migrating cells in complex environments, and in collective systems.

16.11.2023  Our preprint on dynamic signaling and cell fate patterning in neural tube organoids is on bioRxiv

Here, we performed a data-driven inference of a minimal reaction-diffusion model that captures the spatio-temporal dynamics of stem cell patterning.

This was a collaboration the experimental lab of Anna Kicheva (IST Austria).

29.06.2023  Our paper on the stochastic dynamics of pairs of DNA loci and their transcriptional encounters was published in Science

This was a collaboration with the lab of Thomas Gregor (Princeton / Pasteur Institute), with experiments led by Hongtao Chen.

24.03.2023  Our paper on curvature-induced velocity waves in rotating cell spheroids has been published in Nature Communications

This paper was spearheaded by Tom Brandstätter, and in collaboration with Ming Guo (MIT), Ricard Alert (Dresden) and Chase Broedersz (VU Amsterdam).