Xu Peng, Rizzoni Elizabeth Anne, Sul Se-Yeong, Stephanopoulos Gregory
Department of Chemical Engineering, Massachusetts Institute of Technology , 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
Department of Chemistry, Wellesley College , 106 Central Street, Wellesley, Massachusetts 02481, United States.
ACS Synth Biol. 2017 Jan 20;6(1):148-158. doi: 10.1021/acssynbio.6b00187. Epub 2016 Aug 15.
Metabolic engineering entails target modification of cell metabolism to maximize the production of a specific compound. For empowering combinatorial optimization in strain engineering, tools and algorithms are needed to efficiently sample the multidimensional gene expression space and locate the desirable overproduction phenotype. We addressed this challenge by employing design of experiment (DoE) models to quantitatively correlate gene expression with strain performance. By fractionally sampling the gene expression landscape, we statistically screened the dominant enzyme targets that determine metabolic pathway efficiency. An empirical quadratic regression model was subsequently used to identify the optimal gene expression patterns of the investigated pathway. As a proof of concept, our approach yielded the natural product violacein at 525.4 mg/L in shake flasks, a 3.2-fold increase from the baseline strain. Violacein production was further increased to 1.31 g/L in a controlled benchtop bioreactor. We found that formulating discretized gene expression levels into logarithmic variables (Linlog transformation) was essential for implementing this DoE-based optimization procedure. The reported methodology can aid multivariate combinatorial pathway engineering and may be generalized as a standard procedure for accelerating strain engineering and improving metabolic pathway efficiency.
代谢工程需要对细胞代谢进行靶向修饰，以最大限度地提高特定化合物的产量。为了在菌株工程中实现组合优化，需要工具和算法来有效地对多维基因表达空间进行采样，并定位理想的过量生产表型。我们通过采用实验设计（DoE）模型来定量关联基因表达与菌株性能，从而应对了这一挑战。通过对基因表达景观进行分数采样，我们从统计学上筛选了决定代谢途径效率的主要酶靶点。随后使用经验二次回归模型来确定所研究途径的最佳基因表达模式。作为概念验证，我们的方法在摇瓶中产生了525.4 mg/L的天然产物紫菌素，比基础菌株增加了3.2倍。在可控的台式生物反应器中，紫菌素产量进一步提高到1.31 g/L。我们发现，将离散的基因表达水平转化为对数变量（线性对数变换）对于实施这种基于DoE的优化程序至关重要。所报道的方法可以辅助多变量组合途径工程，并可推广为加速菌株工程和提高代谢途径效率的标准程序。
