The optimization of microbial functions through rational environmental manipulations
- PMID: 38372207
- DOI: 10.1111/mmi.15236
The optimization of microbial functions through rational environmental manipulations
Abstract
Microorganisms play a central role in biotechnology and it is key that we develop strategies to engineer and optimize their functionality. To this end, most efforts have focused on introducing genetic manipulations in microorganisms which are then grown either in monoculture or in mixed-species consortia. An alternative strategy to optimize microbial processes is to rationally engineer the environment in which microbes grow. The microbial environment is multidimensional, including factors such as temperature, pH, salinity, nutrient composition, etc. These environmental factors all influence the growth and phenotypes of microorganisms and they generally "interact" with one another, combining their effects in complex, non-additive ways. In this piece, we overview the origins and consequences of these "interactions" between environmental factors and discuss how they have been built into statistical, bottom-up predictive models of microbial function to identify optimal environmental conditions for monocultures and microbial consortia. We also overview alternative "top-down" approaches, such as genetic algorithms, to finding optimal combinations of environmental factors. By providing a brief summary of the state of this field, we hope to stimulate further work on the rational manipulation and optimization of the microbial environment.
Keywords: community optimization; environmental interactions; genetic algorithms; microbial consortia.
© 2024 The Authors. Molecular Microbiology published by John Wiley & Sons Ltd.
References
REFERENCES
-
- Aidelberg, G., Towbin, B.D., Rothschild, D., Dekel, E., Bren, A. & Alon, U. (2014) Hierarchy of non‐glucose sugars in Escherichia coli. BMC Systems Biology, 8, 133. Available from: https://doi.org/10.1186/s12918‐014‐0133‐z
-
- Bajic, D. & Sanchez, A. (2020) The ecology and evolution of microbial metabolic strategies. Current Opinion in Biotechnology, 62, 123–128. Available from: https://doi.org/10.1016/j.copbio.2019.09.003
-
- Bintu, L., Buchler, N.E., Garcia, H.G., Gerland, U., Hwa, T., Kondev, J. et al. (2005) Transcriptional regulation by the numbers: applications. Current Opinion in Genetics and Development, 15, 125–135. Available from: https://doi.org/10.1016/j.gde.2005.02.006
-
- Blaiseau, P.L. & Holmes, A.M. (2021) Diauxic inhibition: Jacques Monod's ignored work. Journal of the History of Biology, 54, 175–196. Available from: https://doi.org/10.1007/s10739‐021‐09639‐4
-
- Blouin, M., Karimi, B., Mathieu, J. & Lerch, T.Z. (2015) Levels and limits in artificial selection of communities. Ecology Letters, 18, 1040–1048. Available from: https://doi.org/10.1111/ele.12486
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