Our goal is to gain understanding of biological systems using concepts and tools developed in theoretical (mostly statistical) physics. Of course, collaborations with experimentalists, when possible, enrich our research.
Projects can develop along several lines:
1) How do biological systems function by consuming energy?
A plethora of biological processes are fueled by energy an energy source, such as ATP or GTP hydrolysis, phosphorylation, methylation and many others. What are the particular physical features of these processes that are due to energy consumption? How can we understand them using the tools of statistical physics?
– protein homeostasis, namely the processes that protect the cell against protein aggregation
– chromatin organization, namely the processes that maintain DNA in a conformation that is useful for correct gene expression
– cellular clocks, namely biochemical processes that give rise to robust oscillatory behavior
and many others.
2) How could the prebiotic environment provide the conditions for life itself to emerge?
This “origins of life” project takes on from the recognition that non-equilibrium conditions, such as the presence of sustained gradients of temperature, pH and other environmental characteristics can modulate the velocity of chemical reactions in space. Coupled to transport, this allows chemical networks to explore a much richer space than what would be possible at thermodynamic equilibrium.
Our goal here is to understand how much “chemical space” can be explored thanks to non-equilibrium, with the ultimate goal, far in the future, of understanding darwinian evolution itself as a non-equilibrium phenomenon, driven by external energy sources, and to quantify its energetic cost.
3) Co-evolutionary prediction of protein structures, protein-protein interactions and beyond.
The three-dimensional shape of proteins and of their complexes is crucial to understand how they perform their functions, and to devise strategies to interfere with them, in search for therapies for diseases. Unfortunately, the experimental determination of the structure of proteins is not easy, and possibly even much more difficult for protein complexes.
In this project we rely on co-evolutionary information to provide additional, complementary information in this direction. In a nutshell, the sequences of the same protein from different organisms are different because of mutations. Nonetheless, mutations are not completely at random. Indeed, some pairs of amino-acids must change in a coordinated manner if they must mutually adapt their physical-chemical properties because they are close to each other in the three dimensional shape of the protein or of the protein complex.
Novel approaches, inspired by the physics of magnetic systems, by machine learning and by deep neural networks, are emerging that are going to facilitate the task. Coupled to the exponentially growing number of sequences from thousands of different organisms, that offers an incredible amount of information, these co-evolutionary techniques are revolutionising the field of structural biology and will likely extend beyond it.
We work both on the development of the methods and on a number of systems that are close to our interests.
PREREQUISITES (not mandatory): in general, in our work we are going to rely on the tools of statistical physics, complex network theory, bioinformatics, and coding in various, different languages.