Metabolomics-based development of bioproduction processes toward industrial-scale production
Microbial biomanufacturing offers a promising, environment-friendly platform for next-generation chemical production. However, its limited industrial implementation is attributed to the slow production rates of target compounds and the time-intensive engineering of high-yield strains. This review hi...
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| Published in | Current opinion in biotechnology Vol. 85; p. 103057 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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England
Elsevier Ltd
01.02.2024
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| Online Access | Get full text |
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| Abstract | Microbial biomanufacturing offers a promising, environment-friendly platform for next-generation chemical production. However, its limited industrial implementation is attributed to the slow production rates of target compounds and the time-intensive engineering of high-yield strains. This review highlights how metabolomics expedites bioproduction development, as demonstrated through case studies of its integration into microbial strain engineering, culture optimization, and model construction. The Design–Build–Test–Learn (DBTL) cycle serves as a standard workflow for strain engineering. Process development, including the optimization of culture conditions and scale-up, is crucial for industrial production. In silico models facilitate the development of strains and processes. Metabolomics is a powerful driver of the DBTL framework, process development, and model construction.
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•Metabolomics accelerates bioproduction via strain, culture, and model improvement.•Metabolomics is a powerful driving force for the DBTL-based strain engineering.•Metabolomics can reveal pathway bottlenecks and metabolic mechanisms.•Machine learning expedites the processing and analysis of metabolome data.•Case studies show metabolomics impact on strain engineering and process development. |
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| AbstractList | Microbial biomanufacturing offers a promising, environment-friendly platform for next-generation chemical production. However, its limited industrial implementation is attributed to the slow production rates of target compounds and the time-intensive engineering of high-yield strains. This review highlights how metabolomics expedites bioproduction development, as demonstrated through case studies of its integration into microbial strain engineering, culture optimization, and model construction. The Design-Build-Test-Learn (DBTL) cycle serves as a standard workflow for strain engineering. Process development, including the optimization of culture conditions and scale-up, is crucial for industrial production. In silico models facilitate the development of strains and processes. Metabolomics is a powerful driver of the DBTL framework, process development, and model construction.Microbial biomanufacturing offers a promising, environment-friendly platform for next-generation chemical production. However, its limited industrial implementation is attributed to the slow production rates of target compounds and the time-intensive engineering of high-yield strains. This review highlights how metabolomics expedites bioproduction development, as demonstrated through case studies of its integration into microbial strain engineering, culture optimization, and model construction. The Design-Build-Test-Learn (DBTL) cycle serves as a standard workflow for strain engineering. Process development, including the optimization of culture conditions and scale-up, is crucial for industrial production. In silico models facilitate the development of strains and processes. Metabolomics is a powerful driver of the DBTL framework, process development, and model construction. Microbial biomanufacturing offers a promising, environment-friendly platform for next-generation chemical production. However, its limited industrial implementation is attributed to the slow production rates of target compounds and the time-intensive engineering of high-yield strains. This review highlights how metabolomics expedites bioproduction development, as demonstrated through case studies of its integration into microbial strain engineering, culture optimization, and model construction. The Design-Build-Test-Learn (DBTL) cycle serves as a standard workflow for strain engineering. Process development, including the optimization of culture conditions and scale-up, is crucial for industrial production. In silico models facilitate the development of strains and processes. Metabolomics is a powerful driver of the DBTL framework, process development, and model construction. Microbial biomanufacturing offers a promising, environment-friendly platform for next-generation chemical production. However, its limited industrial implementation is attributed to the slow production rates of target compounds and the time-intensive engineering of high-yield strains. This review highlights how metabolomics expedites bioproduction development, as demonstrated through case studies of its integration into microbial strain engineering, culture optimization, and model construction. The Design–Build–Test–Learn (DBTL) cycle serves as a standard workflow for strain engineering. Process development, including the optimization of culture conditions and scale-up, is crucial for industrial production. In silico models facilitate the development of strains and processes. Metabolomics is a powerful driver of the DBTL framework, process development, and model construction. [Display omitted] •Metabolomics accelerates bioproduction via strain, culture, and model improvement.•Metabolomics is a powerful driving force for the DBTL-based strain engineering.•Metabolomics can reveal pathway bottlenecks and metabolic mechanisms.•Machine learning expedites the processing and analysis of metabolome data.•Case studies show metabolomics impact on strain engineering and process development. |
| ArticleNumber | 103057 |
| Author | Tanaka, Kenya Kondo, Akihiko Bamba, Takahiro Hasunuma, Tomohisa |
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| Title | Metabolomics-based development of bioproduction processes toward industrial-scale production |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0958166923001672 https://dx.doi.org/10.1016/j.copbio.2023.103057 https://www.ncbi.nlm.nih.gov/pubmed/38154323 https://www.proquest.com/docview/2908123824 |
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