Parameter identification for stochastic hybrid systems using randomized optimization: A case study on subtilin production by Bacillus subtilis

In this paper we study the parameter identification problem for a stochastic hybrid model of the production of the antibiotic subtilin by the bacterium B. subtilis. We pursue a simulation-based approach, in which the fit of candidate parameter values is evaluated by comparing simulated model trajectories with experimental data. Several score functions are considered to capture the goodness of the fit. Parameter estimation is accomplished via an evolutionary strategy that iteratively selects the best fitting parameters. Identifiability issues are discussed and are explored numerically by a Markov Chain Monte Carlo approach.