We study problems at the intersection of statistical physics - such as active matter or non-equilibrium physics - and living systems.
- We study how information is transmitted within organisms via chemical signalling and mechanical forces, as well as how these contribute to the development and specialization of the organism to enable its healthy function. For instance, we have quantified the information transfer for stochastic particles in complex fluid flows (such as those observed in brain ventricles or other organs). Our theoretical framework allows the investigation of genetic signalling in embryos, in addition to identifying the consequences of vorticity (or chirality) in out of equilibrium processes.
- Besides being determined by one's genetic code, another component of healthy function occurs via learning from external stimuli and input. We analyze how particles or organisms acquire information from their surroundings, through building physically-based models of learning in order to yield insight and intuition in today's data- and computation-driven world.
As theoretical physicists, we draw on statistical mechanics and other quantitative tools (such as information theory, network theory and dynamical systems). Our goal is to formulate new conceptual approaches to existing problems and to develop rigorous analytical models suited to these strongly interacting systems.