Multi-Criteria Optimization Based Experimental Design for Parameter Estimation of a Double Feedback Gene Switching Model

Despite the rapid increase in quantity and quality of experimental data in many fields of engineering and science, quantitative measurements of many cellular components are still relatively scarce. This work deals with estimating the parameters of a double feedback gene-switching model. To achieve the goal, a model-based design of experiment (MBDOE) approach for parameter estimation is employed. To overcome the problem of convergence in parameter estimation step (due to correlation among the parameters), a non-dominated sorting genetic algorithm (NSGA-II) based, multi-objective optimization (MOO) based MBDOE has been used. The parameter estimates obtained through the MOO based DOE as well as a standard alphabetical DOE technique are then compared with the known true values from the literature to highlight the efficacy of the MOO-MBDOE technique.