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 journalArticlepeer-review

4 Citations (Scopus)
2 Downloads (Pure)


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 languageEnglish
Pages (from-to)1531–1541
Number of pages11
JournalACS Synthetic Biology
Issue number4
Early online date7 Apr 2022
Publication statusPublished - 15 Apr 2022

Bibliographical note

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.


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


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