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

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ID: 221676
2018
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Ranked #584 of 875 articles by views in journal of magnetic resonance (san diego, calif : 1997)

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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.
Reference Key
heinsch2018frontierssimulation Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Stephen C. Heinsch;Stephen C. Heinsch;Siba R. Das;Michael J. Smanski;Michael J. Smanski;Michael J. Smanski
Journal journal of magnetic resonance (san diego, calif : 1997)
Year 2018
DOI
10.3389/fmicb.2018.00313
URL
http://journal.frontiersin.org/article/10.3389/fmicb.2018.00313/full
Keywords
modeling metabolic engineering

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