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Lookup NU author(s): Emeritus Professor David Parker, Dr Thomas HowardORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by American Chemical Society, 2018.
For re-use rights please refer to the publisher's terms and conditions.
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 catalyse ethanol synthesis, differing in their transcriptional and post-translational regulation, subcellular localisation, 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 behaviours of ADH isozymes and identified novel interactions. Importantly, the model described features of ADH metabolic behaviour without explicit a priori knowledge. The method is therefore highly suited to understanding and optimising metabolic pathways in less well understood systems.
Author(s): Brown SR, Staff M, Lee R, Love J, Parker DA, Aves SJ, Howard TP
Publication type: Article
Publication status: Published
Journal: ACS Synthetic Biology
Year: 2018
Volume: 7
Issue: 7
Pages: 1676-1684
Print publication date: 20/07/2018
Online publication date: 06/07/2018
Acceptance date: 06/07/2018
Date deposited: 10/07/2018
ISSN (electronic): 2161-5063
Publisher: American Chemical Society
URL: https://doi.org/10.1021/acssynbio.8b00112
DOI: 10.1021/acssynbio.8b00112
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