Research Interests

The central theme of my research is to understand how stochastic interactions of cells and molecules control functional behaviours in multicellular systems. 

How do mechanical and biochemical interactions between cells facilitate self-organization of tissue patterns and shapes? How do cells interpret these mechanical and biochemical signals to make cell fate decisions? How are these fate decisions implemented by the stochastic dynamics of the chromosome?

To address these questions, I use approaches from statistical mechanics and soft condensed matter physics. My research combines three complementary theoretical approaches:  minimal biophysical models, information theory, and stochastic inference.

Cell fate decisions and tissue patterning in development

Many developmental systems have the ability to generate patterns of cell fates in a self-organized manner. I investigate the principles of cell-cell communication that give rise to such self-organization, either through mechanical or chemical signaling. A key question is how these communication networks are organized for self-organization to occur reproducibly. I am develop minimal models of such signaling processes, and analyze these models using information-theory. I am applying these ideas to a variety of experimental systems including neural tube and intestinal organoids.

Chromosome dynamics and its role in transcription

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 chromosome organization and real-time dynamics of DNA loci interplay remains unclear. I develop a combination of inference approaches, polymer theory and scaling analyses to learn the physics of chromosome dynamics from experimental trajectories of pairs of DNA loci.

Collective cell migration and cell-cell interactions

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. I develop minimal active matter models of interacting active particles to understand how cell interactions interplay with their environment. This will help understand how cellular tissues generate collective flows in wound healing, cancer metastasis and embryogenesis.

Stochastic dynamics of confined migrating cells

Migrating cells are a beautiful example of a complex molecular machinery that gives rise to stereotypical emergent behaviours. A key challenge is to identify the dynamical laws that describe these emergent behaviours at the scale of the whole cell, and how these behaviours are determined by the molecular mechanisms at the subcellular scale. I work closely with experimental collaborators to infer these dynamics directly from experimental data of confined cell migration, cell shape dynamics, and interacting cells.

Inference from stochastic trajectories

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 experimental trajectories. The key challenges that we are addressing are to achieve reliable inference in the face of finite data, discrete observations, measurement errors and high signal dimensionality. These tools may have applications in a broad range of stochastic systems, from biomolecules, migrating cells and animal swarms to financial markets.