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Front. Microbiol. | doi: 10.3389/fmicb.2018.00313

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

  • 1Department of Biochemistry, Molecular Biology, and Biophysics and Biotechnology Institute, University of Minnesota, United States

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 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.

Keywords: Metabolic Engineering, landscape ruggednes, numerical optimization, modeling, biosynthesis

Received: 04 Oct 2017; Accepted: 09 Feb 2018.

Edited by:

Xueyang Feng, Virginia Tech, United States

Reviewed by:

Qinhong Wang, Tianjin Institute of Industrial Biotechnology (CAS), China
Sun Xinxiao, Beijing University of Chemical Technology, China  

Copyright: © 2018 Heinsch, Das and Smanski. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Mike Smanski, University of Minnesota, Department of Biochemistry, Molecular Biology, and Biophysics and Biotechnology Institute, Minneapolis, United States, smanski@umn.edu