The Library
Trade-offs in biosensor optimization for dynamic pathway engineering
Tools
Tools
Tools
Verma, Babita K., Mannan, Ahmad A., Zhang, Fuzhong and Oyarzún, Diego A. (2022) Trade-offs in biosensor optimization for dynamic pathway engineering. ACS Synthetic Biology, 11 (1). pp. 228-240. doi:10.1021/acssynbio.1c00391 ISSN 2161-5063.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: https://doi.org/10.1021/acssynbio.1c00391
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.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Journal or Publication Title: | ACS Synthetic Biology | ||||||
| Publisher: | American Chemical Society (ACS) | ||||||
| ISSN: | 2161-5063 | ||||||
| Official Date: | 21 January 2022 | ||||||
| Dates: |
|
||||||
| Volume: | 11 | ||||||
| Number: | 1 | ||||||
| Page Range: | pp. 228-240 | ||||||
| DOI: | 10.1021/acssynbio.1c00391 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | ** From Crossref journal articles via Jisc Publications Router ** History: epub 30-12-2021; issued 30-12-2021. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Copyright Holders: | Copyright © 2021 American Chemical Society |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
