Control Engineering Inspired Design Tools for Synthetic Biology

The development of proof-of-concept experimental applications has revealed that, when constructed in the laboratory, most designs do not work as predicted and need to be fine-tuned on a case-by-case basis. Here we use engineering principles to establish a redesign framework to reliably generate synthetic genetic systems that behave in a predictable fashion. The framework is based on a systems-engineering design cycle. We illustrate the application of this cycle on the toggle switch and other networks, for which we investigate their quantifiably robust and predictable implementation. We first produce detailed mathematical model for the system that captures its most important biochemical properties. Combining prior knowledge reported in the literature with experimental data, we show how the constructed models and their parameters can be systematically identified. Based on a robust control analysis of the constructed models and taking into account model uncertainties, we propose modifications to biological parts that can be "easily" implemented experimentally so as to achieve an a priori specified (re-) design objective. We show how experimental data from the implemented prototype systems can be used to refine the models and improve the design through successive iterations. As an example we demonstrate a model-directed redesign of a toggle switch that meets performance specifications and predictively transits between bistable and monostable behaviours through modifications of ribosome binding site (RBS) strengths.