Simulation modeling to compare high-throughput, low-iteration optimization strategies for metabolic engineering

Stephen C. Heinsch, Siba Das, Michael J Smanski

Research output: Contribution to journalArticle

Abstract

Increasing the final titer of a multi-gene metabolic pathway can be viewed as a multivariate optimization problem. While numerous multivariate optimization algorithms exist, few are specifically designed to accommodate the constraints posed by genetic engineering workflows. We present a strategy for optimizing expression levels across an arbitrary number of genes that requires few design-build-test iterations. We compare the performance of several optimization algorithms on a series of simulated expression landscapes. We show that optimal experimental design parameters depend on the degree of landscape ruggedness. This work provides a theoretical framework for designing and executing numerical optimization on multi-gene systems.

Original languageEnglish (US)
Article number313
JournalFrontiers in Microbiology
Volume9
Issue numberFEB
DOIs
StatePublished - Feb 27 2018

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Metabolic Engineering
Genes
Genetic Engineering
Workflow
Metabolic Networks and Pathways
Research Design

Keywords

  • Biosynthesis
  • Landscape ruggednes
  • Metabolic engineering
  • Modeling
  • Numerical optimization

PubMed: MeSH publication types

  • Journal Article

Cite this

Simulation modeling to compare high-throughput, low-iteration optimization strategies for metabolic engineering. / Heinsch, Stephen C.; Das, Siba; Smanski, Michael J.

In: Frontiers in Microbiology, Vol. 9, No. FEB, 313, 27.02.2018.

Research output: Contribution to journalArticle

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