Improving metabolic pathway efficiency by statistical model-based multivariate regulatory metabolic engineering

Peng Xu, Elizabeth Anne Rizzoni, Se Yeong Sul, Gregory Stephanopoulos*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

Metabolic engineering entails target modification of cell metabolism to maximize the production of a specific compound. For empowering combinatorial optimization in strain engineering, tools and algorithms are needed to efficiently sample the multidimensional gene expression space and locate the desirable overproduction phenotype. We addressed this challenge by employing design of experiment (DoE) models to quantitatively correlate gene expression with strain performance. By fractionally sampling the gene expression landscape, we statistically screened the dominant enzyme targets that determine metabolic pathway efficiency. An empirical quadratic regression model was subsequently used to identify the optimal gene expression patterns of the investigated pathway. As a proof of concept, our approach yielded the natural product violacein at 525.4 mg/L in shake flasks, a 3.2-fold increase from the baseline strain. Violacein production was further increased to 1.31 g/L in a controlled benchtop bioreactor. We found that formulating discretized gene expression levels into logarithmic variables (Linlog transformation) was essential for implementing this DoE-based optimization procedure. The reported methodology can aid multivariate combinatorial pathway engineering and may be generalized as a standard procedure for accelerating strain engineering and improving metabolic pathway efficiency.

Original languageEnglish
Pages (from-to)148-158
Number of pages11
JournalACS Synthetic Biology
Volume6
Issue number1
DOIs
StatePublished - 20 Jan 2017
Externally publishedYes

Keywords

  • Combinatorial optimization
  • Metabolic engineering
  • Promoter library
  • Statistical models and response surface methodology
  • Synthetic biology

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