Signaling networks and type-dependent interaction models

Speaker: Eduardo Jordao Neves


We will briefly describe our modeling approach for biological signaling networks in cancer research which is based on interaction particle systems with non reversible dynamics.

In their simplest setting, these models generalise the usual Stochastic Ising
Model with Glauber dynamics to allow type-dependent spin-flip rates that mimic biochemical interactions and which are no longer reversible with respect to the Gibbs measure. In the thermodynamic limit the corresponding macroscopic densities evolve according to deterministic odes that may exhibit highly non-trivial bifurcation

These "toy models" appear to be useful in understanding cellular control mechanisms.