Multi-Objective Robust Optimization for In Vitro RNA Synthesis

Satoru Akama, Masayuki Yamamura, and Takanori Kigawa


Genetic algorithm, multi-objective optimization, enzymatic reaction, model-based design


Optimization of reaction conditions for biocatalytic synthesis has been extensively studied as single-objective optimization (SOO) or multi-objective optimization (MOO). Most of the studies have focused only on maximization of single objectives such as the synthesis rate. In biocatalytic synthesis, however, it is highly probable that component concentrations selected as design variables contain large errors. For example, there can be a gap between the true and the assumed concentrations of enzymes because of their easy inactivation, leading to unexpected decrease in yield. Therefore, it is important to focus on the robustness of the synthesis as well as the enhancement of other objectives. In this paper, we apply an MOO method considering robustness for finding optimal reaction conditions of an in vitro RNA synthesis. This multi-objective robust optimization was performed by considering both the mean value and standard deviation of the yield as objective functions in the MOO problem and solving the problem with NSGA-II, a genetic algorithm. We first verified the effectiveness of our method and then conducted yield and cost optimization considering robustness. Thus, optimal reaction conditions with increased robustness could be successfully obtained.

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