ReviewJournal of industrial microbiology & biotechnology2023
Optimizing the strain engineering process for industrial-scale production of bio-based molecules.
Review in Journal of industrial microbiology & biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 49 citations in OpenAlex.
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- Deletion of low-essentiality, secretion-associated genes enhances recombinant protein production in Komagataella phaffii.Microbial cell factories · 2026Article
- Process Engineering Strategies for Microbial Lipid Production: From Strain Evolution to Industrial-Scale Bioprocessing.International journal of molecular sciences · 2026Review
- Microorganism-Based Biological Products for Agriculture: From Strain Selection to Production Organization.Microorganisms · 2026Review
- Toward Sustainable Testosterone Manufacturing: Green Chemistry and Microbial Biotransformation Approaches.International journal of molecular sciences · 2026Review
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Phenotyping and Selection of Cells Using Mass Spectrometry and a Microfluidic Droplet Printer.Analytical chemistry · 2025Article
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- Endogenous Plant signals and human Health: Molecular mechanisms, ecological functions, and therapeutic Prospects.Biochemistry and biophysics reports · 2025Review
- Microbial Bioprocessing of Food and Agro-Industrial Residues for a Sustainable Circular Economy.Indian journal of microbiology · 2025Review
- A Review of Bioelectrochemical Strategies for Enhanced Polyhydroxyalkanoate Production.Bioengineering (Basel, Switzerland) · 2025Review
- Design principles for engineering bacteria to maximise chemical production from batch cultures.Nature communications · 2025Article
- Recent trends in the elucidation of complex triterpene biosynthetic pathways in horticultural trees.Horticulture research · 2025Article
- Elucidation of genes enhancing natural product biosynthesis through co-evolution analysis.Nature metabolism · 2024Article
- Research Advances of Non-Noble Metal Catalysts for Oxygen Evolution Reaction in Acid.Materials (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
26 authors at 1 institution in 1 country.
Funding
Abstract
Biomanufacturing could contribute as much as ${\$}$30 trillion to the global economy by 2030. However, the success of the growing bioeconomy depends on our ability to manufacture high-performing strains in a time- and cost-effective manner. The Design-Build-Test-Learn (DBTL) framework has proven to be an effective strain engineering approach. Significant improvements have been made in genome engineering, genotyping, and phenotyping throughput over the last couple of decades that have greatly accelerated the DBTL cycles. However, to achieve a radical reduction in strain development time and cost, we need to look at the strain engineering process through a lens of optimizing the whole cycle, as opposed to simply increasing throughput at each stage. We propose an approach that integrates all 4 stages of the DBTL cycle and takes advantage of the advances in computational design, high-throughput genome engineering, and phenotyping methods, as well as machine learning tools for making predictions about strain scale-up performance. In this perspective, we discuss the challenges of industrial strain engineering, outline the best approaches to overcoming these challenges, and showcase examples of successful strain engineering projects for production of heterologous proteins, amino acids, and small molecules, as well as improving tolerance, fitness, and de-risking the scale-up of industrial strains.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.