Received: JAccepted: Published: June 3, 2022Ĭopyright: © 2022 Sabzevari et al. University of Connecticut School of Medicine, UNITED STATES Overall, our results show that multi-agent reinforcement learning is a promising approach for guiding the strain optimization beyond mechanistic knowledge, with the goal of faster and more reliably obtaining industrially attractive production levels.Ĭitation: Sabzevari M, Szedmak S, Penttilä M, Jouhten P, Rousu J (2022) Strain design optimization using reinforcement learning. We further evaluate the proposed MARL approach in improving L-tryptophan production by yeast Saccharomyces cerevisiae, using publicly available experimental data on the performance of a combinatorial strain library.

the speed of convergence towards the optimum response, noise tolerance, and the statistical stability of the solutions found. We investigate the method’s performance relevant for practical applicability in strain engineering i.e. We demonstrate the method’s capabilities using the genome-scale kinetic model of Escherichia coli, k-ecoli457, as a surrogate for an in vivo cell behaviour in cultivation experiments. The multi-agent approach is well-suited to make use of parallel experiments such as multi-well plates commonly used for screening microbial strains. Our method is model-free and does not assume prior knowledge of the microbe’s metabolic network or its regulation. In this paper, we put forward a multi-agent reinforcement learning (MARL) approach that learns from experiments to tune the metabolic enzyme levels so that the production is improved. New techniques that can cope with the complexity and limited mechanistic knowledge of the cellular regulation are called for guiding the strain optimization. It typically relies on trial-and-error leading into high uncertainty in total duration and cost. However, the optimization of the strains required to reach industrially feasible production levels is far less efficient. Cellular synthesis routes are readily assembled and introduced into microbial strains using state-of-the-art synthetic biology tools. Engineered microbial cells present a sustainable alternative to fossil-based synthesis of chemicals and fuels.

Time bandit strain