Simulation Modeling to Compare High-Throughput, Low-Iteration Optimization Strategies for Metabolic Engineering.

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.

journal_name

Front Microbiol

authors

Heinsch SC,Das SR,Smanski MJ

doi

10.3389/fmicb.2018.00313

subject

Has Abstract

pub_date

2018-02-27 00:00:00

pages

313

issn

1664-302X

journal_volume

9

pub_type

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