Ricerc@Sapienza

The use of experimental data from biological systems to infer the parameters of a reasonable underlying model is now a standard practice. However, due to the noise in the experimental data, for simplicity the interaction network to be inferred is often assumed to be symmetric and homogeneous, with just pairwise interactions and important differences involving peripheral nodes are just ignored.

In this project, we will apply methods and techniques commonly used in inference problems, such as Monte Carlo simulations or neural-networks to go beyond the state of the art and achieve a more detailed characterization of the interaction network in living systems to unveil new behavioral traits.
In particular we want to verify if the introduction of possible asymmetries in the iteraction networks is responsible for collective changes of state in response to noise or external reasons, like the presence of a predator. We also want to better understand the role of external individuals.