Robust parameter identification for biological circuit calibration

The aim of this work is to compare some deterministic optimization algorithms and evolutionary algorithms on parameter estimation in a biological circuit design problem: the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We compared deterministic optimization algorithms and evolutionary algorithms in terms of robustness of the resulting parameters including all sources of uncertainty into the statistical representation of reference data and evaluating the obtained solutions in terms of confident limits. The experimental results obtained show as evolutionary algorithms are more robust with respect of deterministic optimization algorithms in particular the algorithm Differential Evolution (DE) showed the best performance over the minimization of the fitting function.