Version 1
: Received: 25 August 2020 / Approved: 26 August 2020 / Online: 26 August 2020 (04:05:47 CEST)
How to cite:
Ng, W. Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization. Preprints2020, 2020080543. https://doi.org/10.20944/preprints202008.0543.v1
Ng, W. Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization. Preprints 2020, 2020080543. https://doi.org/10.20944/preprints202008.0543.v1
Ng, W. Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization. Preprints2020, 2020080543. https://doi.org/10.20944/preprints202008.0543.v1
APA Style
Ng, W. (2020). Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization. Preprints. https://doi.org/10.20944/preprints202008.0543.v1
Chicago/Turabian Style
Ng, W. 2020 "Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization" Preprints. https://doi.org/10.20944/preprints202008.0543.v1
Abstract
Successful engineering of a microbial host for efficient production of a target product from a given substrate can be viewed as an extensive optimization task. Such a task involves the selection of high activity enzymes as well as their gene expression regulatory control elements (i.e., promoters and ribosome binding sites). Finally, there is also the need to tune expression of multiple genes along a heterologous pathway to relieve constraints from rate-limiting step and help reduce metabolic burden on cells from unnecessary over-expression of high activity enzymes. While the aforementioned tasks could be performed through combinatorial experiments, such an approach incurs significant cost, time and effort, which is a handicap that can be relieved by application of modern machine learning tools. Such tools could attempt to predict high activity enzymes from sequence, but they are currently most usefully applied in classifying strong promoters from weaker ones as well as combinatorial tuning of expression of multiple genes. This perspective reviews the application of machine learning tools to aid metabolic pathway optimization through identifying challenges in metabolic engineering that could be overcome with the help of machine learning tools.
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
