Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China.
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
PLoS Comput Biol. 2019 Mar 8;15(3):e1006835. doi: 10.1371/journal.pcbi.1006835. eCollection 2019 Mar.
The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.
代谢工程的最终目标是以具有成本效益的方式在工业规模上生产所需的化合物。为了解决代谢工程中的挑战，基于基因组规模代谢模型的计算菌株优化算法越来越多地被用于辅助过量生产感兴趣的产物。然而，大多数这些菌株优化算法仅利用代谢网络，很少有方法提供还包括转录调控的策略。此外，以前的综合方法通常需要预先存在的调控网络。在这项研究中，我们开发了一种新的菌株设计算法，名为 OptRAM（调控和代谢网络的优化），它可以识别包括代谢基因和转录因子的过表达、敲低或敲除在内的组合优化策略。OptRAM 基于我们之前的 IDREAM 综合网络框架，使其能够从数据中推断出调控网络。OptRAM 使用带有新颖目标函数的模拟退火，该函数可以确保所需化学物质和细胞生长之间的良好耦合。我们提出的另一个进展是通过考虑必需基因、通量变化和工程操作成本，对多个解决方案进行系统评估指标。我们将 OptRAM 应用于酵母中琥珀酸、2,3-丁二醇和乙醇的过量生产菌株设计，与其他方法和以前的文献值相比，预测的最小目标生产速率较高。此外，OptRAM 在这些情况下提出要改变的大多数基因和 TF 已经通过对 LASER 数据库中列出的体内实验中这些所需化合物的过量生产进行精确基因或受 TF 调控的靶基因的修饰来验证。特别是，我们通过发酵实验成功验证了预测的乙醇生产菌株优化策略。总之，OptRAM 可以提供一种有用的方法，利用综合的转录调控网络和代谢网络来指导代谢工程应用。
