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Assessing the growth kinetics and stoichiometry of Escherichia coli at the single‐cell level
Microfluidic cultivation and single‐cell analysis are inherent parts of modern microbial biotechnology and microbiology. However, implementing biochemical engineering principles based on the kinetics and stoichiometry of growth in microscopic spaces remained unattained. We here present a novel integ...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9815083/ https://www.ncbi.nlm.nih.gov/pubmed/36619887 http://dx.doi.org/10.1002/elsc.202100157 |
Sumario: | Microfluidic cultivation and single‐cell analysis are inherent parts of modern microbial biotechnology and microbiology. However, implementing biochemical engineering principles based on the kinetics and stoichiometry of growth in microscopic spaces remained unattained. We here present a novel integrated framework that utilizes distinct microfluidic cultivation technologies and single‐cell analytics to make the fundamental math of process‐oriented biochemical engineering applicable at the single‐cell level. A combination of non‐invasive optical cell mass determination with sub‐pg sensitivity, microfluidic perfusion cultivations for establishing physiological steady‐states, and picoliter batch reactors, enabled the quantification of all physiological parameters relevant to approximate a material balance in microfluidic reaction environments. We determined state variables (biomass concentration based on single‐cell dry weight and mass density), biomass synthesis rates, and substrate affinities of cells grown in microfluidic environments. Based on this data, we mathematically derived the specific kinetics of substrate uptake and growth stoichiometry in glucose‐grown Escherichia coli with single‐cell resolution. This framework may initiate microscale material balancing beyond the averaged values obtained from populations as a basis for integrating heterogeneous kinetic and stoichiometric single‐cell data into generalized bioprocess models and descriptions. |
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