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Research Projects

The central theme of our research is to understand how stochastic interactions of cells and molecules control the functional behaviour of multicellular systems. We specifically focus on how cells interact to shape and pattern tissues during embryonic development. To address this question, we develop theory and models based on statistical mechanics, soft matter physics and information theory. We develop these models in close collaboration with experimental labs.

 

Information processing in self-organized systems

During development, a single fertilized cell transforms into an embryo composed of complex patterned and shaped tissues. An inevitable obstacle to such coordination is intrinsic noise at the single-cell level, which constrains the amount of information accessible to cells for fate decisions. Such information was often considered to be encoded in externally provided signaling gradients, yet how information is generated, transmitted, transformed, and distributed in space and time in a self-organized process remains unclear. We combine information theory, dynamical systems theory and models of pattern formation to uncover the laws of information flows in self-organization.

Selected Publications:

Cell fate decisions and tissue patterning in development

Embryonic development relies on the emergence of different cell fates in a tissue. To determine their fate, cells integrate a number of different information sources, from dynamical and combinatorial to neighborhood and mechanical inputs. A central questions is how these mechano-chemical inputs combine to drive robust fate decisions. We approach this question in close collaboration with experimental collaborators using a combination of theoretical approaches, including dynamical systems theory, machine learning and mathematical models of pattern formation.

Selected Publications:

Active mechanics of cells and tissues

Collectively migrating cells are a prime example of active matter. A central biophysical question is how the collective behaviour of such interacting cells responds to external constraints including curvature, topology and defined geometric boundaries. We develop minimal active matter models to understand how cell interactions interplay with their environment. This will help understand how tissues generate collective flows in wound healing, cancer metastasis and embryogenesis.

Selected Publications:

Stochastic polymer dynamics of DNA loci

Chromosomes are highly organized to fit into the eukaryotic nucleus. For many functional processes, pair-wise interactions of distal chromosomal elements, such as enhancers and promoters, are essential. However, how the stochastic real-time dynamics of DNA loci determines search processes, such as mean first passage times of specific regulatory regions, remains unclear. We develop a combination of inference approaches, polymer simulations and scaling analyses to learn the physics of chromosome dynamics from experimental trajectories of pairs of DNA loci.

Selected Publications:

Inferring the stochastic dynamics of living systems

Can we learn the physics of a system just by looking at it? This fundamental problem of inference from data is particularly difficult to solve in systems that are stochastic, meaning that the dynamics exhibit fluctuations that shape the trajectories of the system. We develop methods to infer the underlying dynamics of stochastic systems directly from dynamic experimental data sets, such as movies and trajectories. These tools have applications in a broad range of stochastic systems, from biomolecules, migrating cells and animal swarms to financial markets.

Selected Publications:

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