Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
Warwick Integrative Synthetic Biology Centre, School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.
Bioinformatics. 2022 Jul 11;38(14):3657-3659. doi: 10.1093/bioinformatics/btac376.
A widely applicable strategy to create cell factories is to knockout (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesize at high rates. Genome-scale metabolic models can be used to explore and identify KOs that growth-couple synthesis, but these are rare in an immense design space, making the search difficult and slow.
To address this multi-objective optimization problem, we developed a software tool named gcFront-using a genetic algorithm it explores KOs that maximize cell growth, product synthesis and coupling strength. Moreover, our measure of coupling strength facilitates the search so that gcFront not only finds a growth-coupled design in minutes but also outputs many alternative Pareto optimal designs from a single run-granting users flexibility in selecting designs to take to the lab.
gcFront, with documentation and a workable tutorial, is freely available at GitHub: https://github.com/lLegon/gcFront and archived at Zenodo, DOI: 10.5281/zenodo.5557755.
Supplementary data are available at Bioinformatics online.
创造细胞工厂的一种广泛适用的策略是敲除（KO）基因或反应，以重新定向细胞代谢，从而使细胞在最大速率下生长时必须进行化学合成。因此，合成与生长偶联，偶联越强，合成中的任何障碍对细胞生长的有害性就越大，从而使高产表型在进化上具有强大的稳健性。此外，我们希望这些菌株能够以高速度生长和合成。基因组规模的代谢模型可用于探索和鉴定与合成偶联的 KO，但在巨大的设计空间中，这些 KO 很少，因此搜索既困难又缓慢。
为了解决这个多目标优化问题，我们开发了一个名为 gcFront 的软件工具，它使用遗传算法来探索使细胞生长、产物合成和偶联强度最大化的 KO。此外，我们的偶联强度度量有助于搜索，因此 gcFront 不仅可以在几分钟内找到一个与生长偶联的设计，而且还可以从单个运行中输出许多替代 Pareto 最优设计，从而使用户在选择要带到实验室的设计时有更大的灵活性。
gcFront 带有文档和可操作的教程，可在 GitHub 上免费获得：https://github.com/lLegon/gcFront，并在 Zenodo 上存档，DOI：10.5281/zenodo.5557755。
补充数据可在 Bioinformatics 在线获得。
