Risk-averse optimization of genetic circuits under uncertainty

Engineering biological systems with specified functions requires navigating an extensive design space, which is challenging to achieve with wet-lab experiments alone. To expedite the design process, mathematical modeling is typically employed to predict circuit function in silico ahead of implementation, which, when coupled with computational optimization, can be used to automatically identify promising designs. However, circuit models are inherently inaccurate, which can result in suboptimal or non-functional in vivo performance. To mitigate this, we propose combining Bayesian inference, Thompson sampling, and risk management to find optimal circuit designs. Our approach employs data from non-functional designs to estimate the distribution of model parameters and then employs risk-averse optimization to select design parameters that are expected to perform well, given parameter uncertainty and biomolecular noise. We illustrate the approach by designing adaptation circuits and genetic oscillators using real and simulated data, with models of varied complexity. A record of this paper's transparent peer review process is included in the supplemental information.