Trade-offs in biosensor optimization for dynamic pathway engineering

Babita Verma, Ahmad Mannan, Fuzhong Zhang, Diego A Oyarzún*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.
Original languageEnglish
Pages (from-to)228-240
Number of pages13
JournalACS Synthetic Biology
Volume11
Issue number1
Early online date30 Dec 2021
DOIs
Publication statusPublished - 21 Jan 2022

Keywords

  • metabolite biosensor
  • dynamic pathway control
  • metabolic engineering
  • biosensor optimization
  • pathway optimization
  • model-based design

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