Bridging the gap between model-based design and reliable implementation of feedback-based biocircuits: A systems inverse problem approach

Our concern is the tuning of mathematical models describing rationally designed genetic biocircuits. Based on a deterministic lumped continuous-time approach, we propose a tuning methodology combining both exact algebraic parameter reconstruction and nonlinear parameter estimation of a given model supporting the design of a specific genetic biocircuit, i.e., we bridge the gap between model-based design and implementation as the solution of a systems inverse problem. As a proof of concept, our proposal is constrained to cyclic feedback systems characterizing synthesized transcriptional networks conditioned to display sustained oscillatory behavior. Our proposed methodology is illustrated via computer-based simulations involving the tuning of a state-based model describing a well-know cyclic feedback biocircuit: the celebrated repressilator. Tuning in our case is conceived as a procedure to adjust the parameter values of the mathematical model taking into account for this the actual behavior observed from the corresponding synthesized biocircuit.