Statistical Physics & Computational Biology

I developed an interest in quantitate biology from a background in condensed matter physics. My goal is to identify the protein sequence features responsible for distinct biological function in different protein families. Unveiling these sequence-function relationships is, in my view, one of the major milestones to advance the field of computational biology. The copious amount of information about sequences, structures, and atomistic molecular dynamics trajectories that became available in the last few years holds promise to render this Holy Grail a reachable scientific goal.

However, while Big Data brings us into a new and exhilarating era of the life sciences, there is a need to organize the newly acquired knowledge into a solid and coherent framework. As in many other fields of investigations concerned with complexity, the rules governing the interactions among the constituents of biological systems are becoming accessible to empirical analysis thanks to approaches based on logical induction and statistical inference. Due to the high-dimensional nature of these problems, statistical physics appears as a natural methodological choice thanks to its ability to relate microscopic interactions to the emergent, collective behavior of many-body systems.

My research is especially focused on the physico-chemical underpinnings of ion transport across lipid membranes. This molecular process is one of the cornerstones of cell physiology: the distinctive feature of living matter is the ability to maintain different chemical conditions in different compartments and to change these conditions in a highly controlled fashion. Accordingly, ion channels and transporters are ubiquitously present in all organisms from the three domains of life and show recognizable sequence conservation over periods of time of the order of billions of years. Highlighting evolutionary conserved features in light of an atomic-level description of their molecular mechanism is my major strategy to formulate hypothesis and build predictive models to investigate the function of these proteins.