Algorithmic co-optimization of genetic constructs and growth conditions: application to 6-ACA, a potential nylon-6 precursor
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Zhou-2015-Algorithmic co-optim.pdf
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2.21 MB
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Author(s)
Zhou, Hui
Vonk, Brenda
Roubos, Johannes A.
Voigt, Christopher A.
Bovenberg, Roel A. L.
Date Issued
October 2015
Journal
Nucleic Acids Research
Publisher
Oxford University Press
Citation
Zhou, Hui, Brenda Vonk, Johannes A. Roubos, Roel A.L. Bovenberg, and Christopher A. Voigt. “Algorithmic Co-Optimization of Genetic Constructs and Growth Conditions: Application to 6-ACA, a Potential Nylon-6 Precursor.” Nucleic Acids Research (October 30, 2015): gkv1071.
Version
Final published version
Abstract
Optimizing bio-production involves strain and process improvements performed as discrete steps. However, environment impacts genotype and a strain that is optimal under one set of conditions may not be under different conditions. We present a methodology to simultaneously vary genetic and process factors, so that both can be guided by design of experiments (DOE). Advances in DNA assembly and gene insulation facilitate this approach by accelerating multi-gene pathway construction and the statistical interpretation of screening data. This is applied to a 6-aminocaproic acid (6-ACA) pathway in Escherichia coli consisting of six heterologous enzymes. A 32-member fraction factorial library is designed that simultaneously perturbs expression and media composition. This is compared to a 64-member full factorial library just varying expression (0.64 Mb of DNA assembly). Statistical analysis of the screening data from these libraries leads to different predictions as to whether the expression of enzymes needs to increase or decrease. Therefore, if genotype and media were varied separately this would lead to a suboptimal combination. This is applied to the design of a strain and media composition that increases 6-ACA from 9 to 48 mg/l in a single optimization step. This work introduces a generalizable platform to co-optimize genetic and non-genetic factors.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Synthetic Biology Center
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DOI of Published Version
https://doi.org/10.1093/nar/gkv1071
