Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO 63130, USA.
Microb Cell Fact. 2011 Jun 21;10:45. doi: 10.1186/1475-2859-10-45.
The robustness of Saccharomyces cerevisiae in facilitating industrial-scale production of ethanol extends its utilization as a platform to synthesize other metabolites. Metabolic engineering strategies, typically via pathway overexpression and deletion, continue to play a key role for optimizing the conversion efficiency of substrates into the desired products. However, chemical production titer or yield remains difficult to predict based on reaction stoichiometry and mass balance. We sampled a large space of data of chemical production from S. cerevisiae, and developed a statistics-based model to calculate production yield using input variables that represent the number of enzymatic steps in the key biosynthetic pathway of interest, metabolic modifications, cultivation modes, nutrition and oxygen availability.
Based on the production data of about 40 chemicals produced from S. cerevisiae, metabolic engineering methods, nutrient supplementation, and fermentation conditions described therein, we generated mathematical models with numerical and categorical variables to predict production yield. Statistically, the models showed that: 1. Chemical production from central metabolic precursors decreased exponentially with increasing number of enzymatic steps for biosynthesis (>30% loss of yield per enzymatic step, P-value = 0); 2. Categorical variables of gene overexpression and knockout improved product yield by 2~4 folds (P-value < 0.1); 3. Addition of notable amount of intermediate precursors or nutrients improved product yield by over five folds (P-value < 0.05); 4. Performing the cultivation in a well-controlled bioreactor enhanced the yield of product by three folds (P-value < 0.05); 5. Contribution of oxygen to product yield was not statistically significant. Yield calculations for various chemicals using the linear model were in fairly good agreement with the experimental values. The model generally underestimated the ethanol production as compared to other chemicals, which supported the notion that the metabolism of Saccharomyces cerevisiae has historically evolved for robust alcohol fermentation.
We generated simple mathematical models for first-order approximation of chemical production yield from S. cerevisiae. These linear models provide empirical insights to the effects of strain engineering and cultivation conditions toward biosynthetic efficiency. These models may not only provide guidelines for metabolic engineers to synthesize desired products, but also be useful to compare the biosynthesis performance among different research papers.
酿酒酵母在促进工业规模生产乙醇方面的稳健性使其能够作为平台来合成其他代谢产物。代谢工程策略，通常通过途径过表达和缺失，继续在优化底物转化为所需产物的转化效率方面发挥关键作用。然而，基于反应化学计量和质量平衡，化学产物的产率或产量仍然难以预测。我们对来自酿酒酵母的大量化学产物生产数据进行了采样，并开发了一种基于统计学的模型，该模型使用表示感兴趣的关键生物合成途径中的酶促步骤数量、代谢修饰、培养方式、营养和氧气可用性的输入变量来计算产物产率。
基于约 40 种来自酿酒酵母的化学产物的生产数据、其中描述的代谢工程方法、营养补充和发酵条件，我们使用数值和分类变量生成了用于预测产物产率的数学模型。统计上，这些模型表明：1. 来自中心代谢前体的化学产物的生产随着生物合成中酶促步骤数量的增加呈指数级下降（每增加一个酶促步骤，产量损失超过 30%，P 值=0）；2. 基因过表达和敲除的分类变量将产物产率提高了 2~4 倍（P 值<0.1）；3. 添加大量中间前体或营养物质可使产物产率提高 5 倍以上（P 值<0.05）；4. 在控制良好的生物反应器中进行培养可使产物产率提高 3 倍（P 值<0.05）；5. 氧气对产物产率的贡献没有统计学意义。使用线性模型计算各种化学物质的产率与实验值相当吻合。与其他化学物质相比，该模型通常低估了乙醇的产量，这支持了酿酒酵母的代谢已进化为稳健的酒精发酵的观点。
我们生成了用于酿酒酵母化学产物产率一阶近似的简单数学模型。这些线性模型为菌株工程和培养条件对生物合成效率的影响提供了经验性的见解。这些模型不仅可以为代谢工程师合成所需产物提供指导，而且还可以用于比较不同研究论文之间的生物合成性能。
