ecBSU1: A Genome-Scale Enzyme-Constrained Model of Bacillus subtilis Based on the ECMpy Workflow
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Abstract
Genome-scale metabolic models (GEMs) play an important role in the phenotype prediction of microorganisms, and their accuracy can be further improved by integrating other types of biological data such as enzyme concentrations and kinetic coefficients. Enzyme-constrained models (ecModels) have been constructed for several species and were successfully applied to increase the production of commodity chemicals. However, there was still no genome-scale ecModel for the important model organism Bacillus subtilis prior to this study. Here, we integrated enzyme kinetic and proteomic data to construct the first genome-scale ecModel of B. subtilis (ecBSU1) using the ECMpy workflow. We first used ecBSU1 to simulate overflow metabolism and explore the trade-off between biomass yield and enzyme usage efficiency. Next, we simulated the growth rate on eight previously published substrates and found that the simulation results of ecBSU1 were in good agreement with the literature. Finally, we identified target genes that enhance the yield of commodity chemicals using ecBSU1, most of which were consistent with the experimental data, and some of which may be potential novel targets for metabolic engineering. This work demonstrates that the integration of enzymatic constraints is an effective method to improve the performance of GEMs. The ecModel can predict overflow metabolism more precisely and can be used for the identification of target genes to guide the rational design of microbial cell factories.
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- Authors
- Wu, K., Mao, Z., Mao, Y., Niu, J., Cai, J., Yuan, Q., Yun, L., Liao, X., Wang, Z., & Ma, H.
- Affiliations
- Tianjin University (CN)
- Chinese Academy of Sciences (CN)
- BGI Group (China) (CN)
- Corresponding author
- Wang, Zhiwen
- Ma, Hongwu
- Venue
- Microorganisms
- Publisher
- MDPI AG
- Published
- 2023-01-11
- Volume
- 11
- Issue
- 1
- Pages
- 178
- ISSN
- 2076-2607 (ISSN-L)
- PMC
- PMC9864840
- OpenAlex
- W4315648613
- Cited by
- 25
- References
- 49
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 11 · 2023: 5
- FWCI
- 2.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Microorganisms (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2021YFC2100700, NSFC-21621004, 32101186, 21908239, 32100035, TSBICIP-PTJS-001, 153D31KYSB20170121, 2021M693351)
- National Natural Science Foundation of China (2021YFC2100700, NSFC-21621004, 32101186, 21908239, 32100035, TSBICIP-PTJS-001, 153D31KYSB20170121, 2021M693351)
- Tianjin Synthetic Biotechnology Innovation Capacity Improvement Projects (2021YFC2100700, NSFC-21621004, 32101186, 21908239, 32100035, TSBICIP-PTJS-001, 153D31KYSB20170121, 2021M693351)
- International Partnership Program of Chinese Academy of Sciences (2021YFC2100700, NSFC-21621004, 32101186, 21908239, 32100035, TSBICIP-PTJS-001, 153D31KYSB20170121, 2021M693351)
- China Postdoctoral Science Foundation (2021YFC2100700, NSFC-21621004, 32101186, 21908239, 32100035, TSBICIP-PTJS-001, 153D31KYSB20170121, 2021M693351)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Bacillus subtilis, genome-scale metabolic models, enzyme kinetics, proteomics, overflow metabolism, biomass yield, metabolic engineering targets, microbial cell factories
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