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 language | English (US) |
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Article number | 313 |
Journal | Frontiers in Microbiology |
Volume | 9 |
Issue number | FEB |
DOIs | |
State | Published - Feb 27 2018 |
Bibliographical note
Publisher Copyright:© 2018 Heinsch, Das and Smanski.
Keywords
- Biosynthesis
- Landscape ruggednes
- Metabolic engineering
- Modeling
- Numerical optimization