Jensen Kristian, Broeken Valentijn, Hansen Anne Sofie Lærke, Sonnenschein Nikolaus, Herrgård Markus J
The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark.
Metab Eng Commun. 2019 Mar 16;8:e00087. doi: 10.1016/j.mec.2019.e00087. eCollection 2019 Jun.
Biological production of chemicals is an attractive alternative to petrochemical-based production, due to advantages in environmental impact and the spectrum of feasible targets. However, engineering microbial strains to overproduce a compound of interest can be a long, costly and painstaking process. If production can be coupled to cell growth it is possible to use adaptive laboratory evolution to increase the production rate. Strategies for coupling production to growth, however, are often not trivial to find. Here we present OptCouple, a constraint-based modeling algorithm to simultaneously identify combinations of gene knockouts, insertions and medium supplements that lead to growth-coupled production of a target compound. We validated the algorithm by showing that it can find novel strategies that are growth-coupled in silico for a compound that has not been coupled to growth previously, as well as reproduce known growth-coupled strain designs for two different target compounds. Furthermore, we used OptCouple to construct an alternative design with potential for higher production. We provide an efficient and easy-to-use implementation of the OptCouple algorithm in the cameo Python package for computational strain design.
由于在环境影响和可行目标范围方面具有优势，化学品的生物生产是基于石化产品生产的一种有吸引力的替代方案。然而，对工程微生物菌株进行改造以过量生产目标化合物可能是一个漫长、昂贵且艰苦的过程。如果生产能够与细胞生长相耦合，那么就有可能利用适应性实验室进化来提高生产率。然而，找到将生产与生长相耦合的策略通常并非易事。在此，我们展示了OptCouple，这是一种基于约束的建模算法，可同时识别基因敲除、插入和培养基补充物的组合，这些组合可导致目标化合物的生长耦合生产。我们通过表明该算法能够为一种此前未与生长相耦合的化合物找到在计算机模拟中与生长相耦合的新策略，以及重现两种不同目标化合物已知的生长耦合菌株设计，从而验证了该算法。此外，我们使用OptCouple构建了一种具有更高生产潜力的替代设计。我们在用于计算菌株设计的cameo Python包中提供了OptCouple算法的高效且易于使用的实现方式。
