Research Interests

I am broadly interested in the quantitative theoretical understanding of biological systems, including developmental systems, cell migration, and chromosome organization. To approach these complex systems, I use a mix of data-driven inference methods, mechanistic biophysical models, and normative approaches based on information theory. My current projects focus mainly on fate decision making of stem cells in development.

Collective cell migration in curved 3D systems

Multicellular organization in diverse systems, including embryos, intestines and tumors, relies on coordinated cell migration in curved environments. While collective migration is increasingly understood in flat systems, the consequences of geometrical and topological constraints on collective migration in curved systems are largely unknown. Using a combination of statistical analysis and active matter modelling, we seek to identify the principles of collective migration in curved systems.

Dynamics of cell-cell interactions in confined cell migration

When cells migrate collectively, such as to heal wounds or invade tissue, they coordinate through cell-cell interactions. While much is known about the complex molecular basis of these interactions, the system-level dynamics of interacting cell behaviour remains poorly understood. We develop tools to infer cell-cell interactions directly from experimental trajectory data. We are using this approach to quantitatively study how molecular perturbations control cell-cell interactions, and aim to generalize this as a conceptual basis to treat the stochastic dynamics of larger cell collectives.

Inference from stochastic trajectories

Stochastic inference can extract the underlying dynamical laws that give rise to the complex, stochastic behaviour of complex systems. Experimental observations of such systems frequently provide limited data, recorded at a finite sampling rate and are subject to measurement noise, introducing significant errors in the inferred terms. We are developing methods that eliminate these errors, thus enabling the inference of simple laws from limited experimental data.

Stochastic nonlinear dynamics of confined cell migration

In many physiological processes, migrating cells navigate structured and confining environments. Yet, the emergent dynamics of such confined active motion remains poorly understood. We develop data-driven methods to infer the complex stochastic dynamics governing cell motion, protrusion formation, and shape dynamics from experimental data.

'Smart vesicles': from swarm robotics to synthetic biology

How can collections of active entities, ranging from small bots to actin filaments self-organize to perform functional tasks? By studying such systems in simplified, well-controlled environments, we aim to elucidate this question and construct synthetic biology and robotic systems.

Cell migration on elastic substrates

Cells constantly probe and sense the mechanical properties of their micro-environment, and their migratory behaviour is intimately controlled by the mechanics of their substrate. However, it is unclear which aspects of this response are due to biochemical signalling and which might be of purely physical origin. Here, we investigated how substrate stiffness and viscosity as well as externally applied strain can direct cell migration through purely physical mechanisms.