Computational Cell Biology

Research project

Cells are able to sense signals coming from the environment and to react accordingly. For example, some cells can find out where are nutrients around them, and move towards the highest concentration. Cells can also detect signals coming from within themselves: as an example, cell cycle progression is halted in presence of damaged DNA, for the time needed to fix the damage.

The ability of cells to read such signals, integrate them and react in a coherent fashion can be traced back to the dynamics of complex molecular regulatory networks localized inside cells. Different types of molecules -- mRNAs, proteins, metabolites, and so on -- interact and regulate each other's abundance and activity through different mechanisms (transcriptional control, post-translational modifications, etc.).

Understanding the dynamics of these molecular regulatory networks is far from a trivial task, because they are composed of a high number of species, interacting with each other in all sorts of feedback mechanisms. An intuitive approach is inadequate to tackle these complex systems, and in recent years a more quantitative, systems approach (i.e., Systems Biology or Computational Cell Biology) has attracted much attention. Our research belongs to this new field, and particularly we aim at studying the dynamics of molecular networks using nonlinear ordinary differential equations. We are both interested in developing new methods to simulate molecular regulatory networks and in collaborating with experimental groups here at IFOM for analyzing specific biological systems.