Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity

• Published in: Trends in biotechnology (Regular ed.) Vol. 32; no. 12; pp. 608 - 616
• Main Authors: Delvigne, Frank, Zune, Quentin, Lara, Alvaro R., Al-Soud, Waleed, Sørensen, Søren J.
• Format: Journal Article Web Resource
• Language: English
• Published: Cambridge, MA Elsevier Ltd 01.12.2014; Cell Press; Elsevier Limited; Elsevier Science
• Subjects: Bacteria - growth & development; Bacteria - metabolism; Batch processes; Biological and medical sciences; bioprocess optimization; Bioprocessing; Bioreactors; Bioreactors - microbiology; Biotechnologie; Biotechnology; Cell cycle; Fundamental and applied biological sciences. Psychology; Fungi - growth & development; Fungi - metabolism; Gene expression; Genotype; Heterogeneity; Industrial Microbiology; Internal Medicine; Life sciences; metabolic engineering; Metabolic Engineering - methods; Metabolism; Metabolites; microbial stress; Microorganisms; Monitoring; Noise; Phenotype; phenotypic variation; Physiology; Population; Proteins; Sciences du vivant; Stochasticity; Strategy; Studies; stochasticity; microbial stress; metabolic engineering; bioprocess optimization; Heterogeneity; Phenotype; Microorganism; Metabolism; Variability
• ISSN: 0167-7799; 1879-3096; 1879-3096
• DOI: 10.1016/j.tibtech.2014.10.002

•Microbial phenotypic heterogeneity impacts upon bioprocess performance.•Microbial phenotypic heterogeneity depends not only on stochasticity of gene expression but also on stochasticity at the level of metabolic reactions.•Metabolic variability and specialization in bioprocessing can be a source of new metabolic engineering strategies.•New analytical tools and bioreactor technologies for studying/controlling microbial phenotypic heterogeneity are considered. Phenotypic heterogeneity is a major issue in the context of industrial bioprocessing. Stochasticity of gene expression is usually considered to be the main source of heterogeneity among microbial population, but recent evidence demonstrates that metabolic reactions can also be subject to stochasticity without any intervention of gene expression. Although metabolic heterogeneity can be encountered in laboratory-scale cultivation devices, stochasticity at the level of metabolic reactions is perturbed directly by microenvironmental heterogeneities occurring in large-scale bioreactors. Accordingly, analytical tools are needed for the determination of metabolic variability in bioprocessing conditions and for the efficient design of metabolic engineering strategies. In this context, implementation of single cell technologies for bioprocess monitoring would benefit from knowledge acquired in more fundamental studies.