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OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

Lookup NU author(s): Dr Shouyong Jiang, Professor Marcus Kaiser, Professor Natalio KrasnogorORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Computational tools have been widely adopted for strain optimization in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximization of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout), leading to high biochemical production. The usefulness and capabilities of OptDesign are demonstrated for the production of three biochemicals in Escherichia coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. The source code is available at https://github.com/chang88ye/OptDesign.


Publication metadata

Author(s): Jiang S, Otero-Muras I, Banga JR, Wang Y, Kaiser M, Krasnogor N

Publication type: Article

Publication status: Published

Journal: ACS Synthetic Biology

Year: 2022

Volume: 11

Issue: 4

Pages: 1531-1541

Print publication date: 15/04/2022

Online publication date: 07/04/2022

Acceptance date: 02/04/2018

Date deposited: 09/05/2022

ISSN (electronic): 2161-5063

Publisher: American Chemical Society

URL: https://doi.org/10.1021/acssynbio.1c00610

DOI: 10.1021/acssynbio.1c00610

PubMed id: 35389631


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Funding

Funder referenceFunder name
61976225
DPI2017-82896-C2-2-R
EP/N031962/1EPSRC
MCIN/AEI/10.13039/501100011033
PID2020-117271RB-C22
RG16134-18
Royal Academy of Engineering Chair in Emerging Technology award

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