LACL, bâtiment P2 niveau 1, salle 131 (P2-131)comment y aller
Abstract: Biochemical networks are usually modeled by ordinary differential equations that describe the time evolution of the concentrations of the interacting (biochemical) species for specific initial concentrations and certain values of the interaction rates. They consist, in general, of many variables and parameters. Parameter estimation of such systems using standard optimization algorithms is computationally expensive since a large number of numerical simulations must be performed for numerous values of initial conditions and parameters, making these approaches either inefficient or inaccurate. In this talk, we explain how biochemical networks can be approximated by dynamic bayesian networks, a class of discrete probabilistic models. This type of approximation enables the use of Bayesian inference for performing tasks such as parameter estimation. We explain how to make this type of approximations as accurate and computationnaly efficient as possible. Our approach is applied on concrete examples such as the EGF-NGF cellular signaling pathway.
Abstract: Timed automata are a common way of modelling real-time systems so that errors can be checked for or avoided. In particular, we may want to constrain the behaviour of the system so that it validates a safety property. We then seek to synthesise a controller that acts on the possible actions of the system at each moment of execution. On the other hand, we can represent a secret that we don't want to reveal by a private state in the timed automaton. The system is then opaque if, for every execution that passes through the private state, there is another execution with the same execution time that does not pass through the private time, and vice versa. In this talk, we will look at controller synthesis for the opacity problem.