Brown Steven R, Staff Marta, Lee Rob, Love John, Parker David A, Aves Stephen J, Howard Thomas P
Biosciences, Geoffrey Pope Building, College of Life and Environmental Sciences , University of Exeter , Exeter EX4 4QD , U.K.
Biodomain , Shell Technology Center Houston , 3333 Highway 6 South , Houston , Texas 77082-3101 , United States.
ACS Synth Biol. 2018 Jul 20;7(7):1676-1684. doi: 10.1021/acssynbio.8b00112. Epub 2018 Jul 10.
Multifactorial approaches can quickly and efficiently model complex, interacting natural or engineered biological systems in a way that traditional one-factor-at-a-time experimentation can fail to do. We applied a Design of Experiments (DOE) approach to model ethanol biosynthesis in yeast, which is well-understood and genetically tractable, yet complex. Six alcohol dehydrogenase (ADH) isozymes catalyze ethanol synthesis, differing in their transcriptional and post-translational regulation, subcellular localization, and enzyme kinetics. We generated a combinatorial library of all ADH gene deletions and measured the impact of gene deletion(s) and environmental context on ethanol production of a subset of this library. The data were used to build a statistical model that described known behaviors of ADH isozymes and identified novel interactions. Importantly, the model described features of ADH metabolic behavior without explicit a priori knowledge. The method is therefore highly suited to understanding and optimizing metabolic pathways in less well-understood systems.
多因素方法能够快速且高效地对复杂的、相互作用的自然或工程生物系统进行建模，而传统的一次一个因素的实验方法则无法做到这一点。我们应用实验设计（DOE）方法对酵母中的乙醇生物合成进行建模，酵母中的乙醇生物合成虽然已被充分了解且具有遗传易处理性，但仍很复杂。六种乙醇脱氢酶（ADH）同工酶催化乙醇合成，它们在转录和翻译后调控、亚细胞定位以及酶动力学方面存在差异。我们构建了所有ADH基因缺失的组合文库，并测量了基因缺失和环境背景对该文库一部分菌株乙醇产量的影响。这些数据被用于构建一个统计模型，该模型描述了ADH同工酶的已知行为并识别出了新的相互作用。重要的是，该模型在没有明确先验知识的情况下描述了ADH代谢行为的特征。因此，该方法非常适合于理解和优化较难理解的系统中的代谢途径。
