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

Shouyong Jiang; Irene Otero Muras; Julio R. Banga; Yong Wang; Marcus Kaiser; Natalio Krasnogor

doi:10.1021/acssynbio.1c00610

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

Shouyong Jiang^* (Corresponding Author), Irene Otero Muras, Julio R. Banga, Yong Wang^* (Corresponding Author), Marcus Kaiser, Natalio Krasnogor

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

Computational tools have been widely adopted for strain optimisation 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., maximisation 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 E. coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. Source code is available at https://github.com/chang88ye/OptDesign.

Original language	English
Pages (from-to)	1531–1541
Number of pages	11
Journal	ACS Synthetic Biology
Volume	11
Issue number	4
Early online date	7 Apr 2022
DOIs	https://doi.org/10.1021/acssynbio.1c00610
Publication status	Published - 15 Apr 2022

Bibliographical note

Acknowledgements
This work has been supported by the Engineering and Physical Sciences Research Council (EPSRC) for funding project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)”. SJ acknowledges funding from BBSRC Mitigation Fund RG16134-18. IMO and JRB acknowledges funding from MCIN/AEI/ 10.13039/501100011033 and “ERDF A way of making Europe” through grant DPI2017-82896-C2-2-R YNBIOCONTROL). JRB acknowledges funding from MCIN/AEI/ 10.13039/501100011033 through grant PID2020-117271RB-C22 (BIODYNAMICS). YW acknowledges funding from National Natural Science Foundation of China (Grant No. 61976225). NK is funded by a Royal Academy of Engineering Chair in Emerging Technology award

Data Availability Statement

Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssynbio.1c00610.

Keywords

growth-coupled designed
flux change
genome-scale metabolic
systems biology
in silico strain design
biotechnology

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Cite this

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abstract = "Computational tools have been widely adopted for strain optimisation in metabolicengineering, 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., maximisation 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 E. coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. Source code is available at https://github.com/chang88ye/OptDesign.",

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note = "Acknowledgements This work has been supported by the Engineering and Physical Sciences Research Council (EPSRC) for funding project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)”. SJ acknowledges funding from BBSRC Mitigation Fund RG16134-18. IMO and JRB acknowledges funding from MCIN/AEI/ 10.13039/501100011033 and “ERDF A way of making Europe” through grant DPI2017-82896-C2-2-R YNBIOCONTROL). JRB acknowledges funding from MCIN/AEI/ 10.13039/501100011033 through grant PID2020-117271RB-C22 (BIODYNAMICS). YW acknowledges funding from National Natural Science Foundation of China (Grant No. 61976225). NK is funded by a Royal Academy of Engineering Chair in Emerging Technology award ",

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AU - Krasnogor, Natalio

N1 - Acknowledgements This work has been supported by the Engineering and Physical Sciences Research Council (EPSRC) for funding project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)”. SJ acknowledges funding from BBSRC Mitigation Fund RG16134-18. IMO and JRB acknowledges funding from MCIN/AEI/ 10.13039/501100011033 and “ERDF A way of making Europe” through grant DPI2017-82896-C2-2-R YNBIOCONTROL). JRB acknowledges funding from MCIN/AEI/ 10.13039/501100011033 through grant PID2020-117271RB-C22 (BIODYNAMICS). YW acknowledges funding from National Natural Science Foundation of China (Grant No. 61976225). NK is funded by a Royal Academy of Engineering Chair in Emerging Technology award

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N2 - Computational tools have been widely adopted for strain optimisation in metabolicengineering, 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., maximisation 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 E. coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. Source code is available at https://github.com/chang88ye/OptDesign.

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

Abstract

Bibliographical note

Data Availability Statement

Keywords

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