Speaker
Mr
Matteo Polettini
(University of Luxembourg)
Description
Today, high-throughput technologies in genomics allow to
draft complete Chemical Networks (CN) for complex
biochemical processes such as gene regulation and cellular
metabolism, which are repositories of thousands of pathways,
metabolites, and their stoichiometry. Dwelling into these
data is an excruciating task in Systems Biology.
Because of the very small numbers of enzymes (in a cell
some can be expressed in but a few copies), these processes
are intrinsically stochastic. Moreover, closed reaction
pathways within CNs can be seen as thermodynamic cycles
processing nutrients and information into waste and
products, in a way reminiscent of thermodynamic machines.
In this talk, we argue that ideas and techniques from
Stochastic Thermodynamics can be used to decipher the
enormous complexity of (bio)Chemical Networks. In
particular, we present and address the themes of network
reconstruction of the fluxes and of free energy landscapes,
and of the effect of noise on dissipation.
