Enfors, S. O., Physiological responses to mixing in large scale bioreactors. J. Biotechnol. 2001, 85, 175–185.
Neubauer, P., Junne, S., Scale-down simulators for metabolic analysis of large-scale bioprocesses. Curr. Opin. Biotechnol. 2010, 21, 114–121.
Delvigne, F., Lejeune, A., Destain, J., Thonart, P., Modelling of the substrate heterogeneities experienced by a limited microbial population in scale-down and in large-scale bioreactors. Chem. Eng. J. 2006, 120, 157–167.
Lapin, A., Multi-scale spatio-temporal modeling: Lifelines of microorganisms in bioreactors and tracking molecules in cells. Adv. Biochem. Eng. Biotechnol. 2010, 121, 23–43.
Delvigne, F., Goffin, P., Microbial heterogeneity affects bioprocess robustness: Dynamic single cell analysis contribute to understanding microbial populations. Biotechnol. J. 2014, 9, 61–72.
Swain, P. S., Elowitz, M. B., Siggia, E. D., Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl. Acad. Sci. USA. 2002, 99, 12795–12800.
Silva-Rocha, R., de Lorenzo, V., Noise and robustness in prokaryotic regulatory networks. Annu. Rev. Microbiol. 2010, 64, 257–275.
Shahrezaei, V., Swain, P. S., The stochastic nature of biochemical networks. Curr. Opin. Biotechnol. 2008, 19, 369–374.
Delvigne, F., Zune, Q., Lara, A. R., Al-Soud, W. et al., Metabolic variability in bioprocessing: Implications of microbial phenotypic heterogeneity. Trends Biotechnol. 2014, 32, 608–616.
Boulineau, S., Tostevin, F., Kiviet, D. J., ten Wolde, P. R. et al., Single-cell dynamics reveals sustained growth during diauxic shifts. PLoS One 2013, 8, e61686.
Solopova, A., van Gestel, J., Weissing, F. J., Bachmann, H. et al., Bet-hedging during bacterial diauxic shift. Proc. Natl. Acad. Sci. USA 2014, 111, 7427–7432.
Van Heerden, J. H., Lost in transition: Startup of glycolysis yields subpopulations of nongrowing cells. Science 2014, 343, 1–9.
Grunberger, A., Wiechert, W., Kohlheyer, D., Single-cell microfluidics: Opportunity for bioprocess development. Curr. Opin. Biotechnol. 2014, 29, 15–23.
Brognaux, A., Han, S., Sorensen, S. J., Lebeau, F. et al., A low-cost, multiplexable, automated flow cytometry procedure for the characterization of microbial stress dynamics in bioreactors. Microb. Cell Fact. 2013, 12, 553–563.
Polizzi, K. M., Kontoravdi, C., Genetically-encoded biosensors for monitoring cellular stress in bioprocessing. Curr. Opin. Biotechnol. 2014, 31, 50–56.
Zhang, J., Jensen, M. K., Keasling, J. D., Development of biosensors and their application in metabolic engineering. Curr. Opin. Chem. Biol. 2015, 28, 1–8.
Delvigne, F., Ingels, S., Boxus, M., Thonart, P., Bioreactor mixing efficiency modulates the activity of a prpoS::GFP reporter gene in E. coli. Microb. Cell Fact. 2009, 8, 1475–2859.
Cabantous, S., Terwilliger, T. C., Waldo, G. S., Protein tagging and detection with engineered self-assembling fragments of green fluorescent protein. Nat. Biotechnol. 2005, 23, 102–107.
Nikolic, N., Barner, T., Ackermann, M., Analysis of fluorescent reporters indicates heterogeneity in glucose uptake and utilization in clonal bacterial populations. BMC Microbiol. 2013, 13, 258.
Han, S., Delvigne, F., Brognaux, A., Charbon, G. E. et al., Design of growth-dependent biosensors based on destabilized GFP for the detection of physiological behavior of Escherichia coli in heterogeneous bioreactors. Biotechnol. Prog. 2013, 29, 553–563.
Hentschel, E., Will, C., Mustafi, N., Burkovski, A. et al., Destabilized eYFP variants for dynamic gene expression studies in Corynebacterium glutamicum. Microb. Biotechnol. 2013, 6, 196–201.
Müller, S., Origin and analysis of microbial population heterogeneity in bioprocesses. Curr. Opin. Biotechnol. 2010, 21, 100–113.
Konopka, M. C., Strovas, T. J., Ojala, D. S., Chistoserdova, L. et al., Respiration response imaging for real-time detection of microbial function at the single-cell level. Appl. Env. Microbiol. 2011, 77, 67–72.
Sabido, A., Martinez, L. M., de Anda, R., Martinez, A. et al., A novel plasmid vector designed for chromosomal gene integration and expression: Use for developing a genetically stable Escherichia coli melanin production strain. Plasmid 2013, 69, 16–23.
Dikshit, K. L., Orii, Y., Navani, N., Patel, S. et al., Site-directed mutagenesis of bacterial hemoglobin: The role of glutamine (E7) in oxygen-binding in the distal heme pocket. Arch. Biochem. Biophys. 1998, 349, 161–166.
Koebmann, B. J., Westerhoff, H. V., Snoep, J. L., Nilsson, D. et al., The glycolytic flux in Escherichia coli is controlled by the demand for ATP. J. Bacteriol. 2002, 184, 3909–3916.
Pablos, T. E., Mora, E. M., Le Borgne, S., Ramirez, O. T. et al., Vitreoscilla hemoglobin expression in engineered Escherichia coli: Improved performance in high cell-density batch cultivations. Biotechnol. J. 2011, 6, 993–1002.
Pablos, T. E., Sigala, J. C., Le Borgne, S., Lara, A. R., Aerobic expression of Vitreoscilla hemoglobin efficiently reduces overflow metabolism in Escherichia coli. Biotechnol. J. 2014, 9, 791–799.
Frey, A. D., Kallio, P. T., Bacterial hemoglobins and flavohemoglobins: Versatile proteins and their impact on microbiology and biotechnology. FEMS Microbiol. Rev. 2003, 27, 525–545.
Holm, A. K., Blank, L. M., Oldiges, M., Schmid, A. et al., Metabolic and transcriptional response to cofactor perturbations in Escherichia coli. J. Biol. Chem. 2010, 285, 17498–17506.
Silander, O. K., Nikolic, N., Zaslaver, A., Bren, A. et al., A genome-wide analysis of promoter-mediated phenotypic noise in Escherichia coli. PLos Genet. 2012, 8, e1002443.
Taniguchi, Y., Choi, P. J., Li, G. W., Chen, H. et al., Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 2010, 329, 533–538.
Bar-Even, A., Paulsson, J., Maheshri, N., Carmi, M. et al., Noise in protein expression scales with natural protein abundance. Nat. Genet. 2006, 38, 636–643.
Delvigne, F., Destain, J., Thonart, P., Structured mixing model for stirred bioreactor: An extension to the stochastic approach. Chem. Eng. J. 2005, 113, 1–12.
Henkel, S. G., Ter Beek, A., Steinsiek, S., Stagge, S. et al., Basic regulatory principles of Escherichia coli’s electron transport chain for varying oxygen conditions. PLoS One 2014, 9, e107640.
Delvigne, F., Baert, J., Gofflot, S., Lejeune, A. et al., Dynamic single-cell analysis of Saccharomyces cerevisiae under process perturbation: Comparison of different methods for monitoring the intensity of population heterogeneity. J. Chem. Technol. Biotechnol. 2015, 90, 314–323.
Newman, J. R. S., Ghaemmaghami, S., Ihmels, J., Breslow, D. K. et al., Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 2006, 441, 840–846.
Baert, J., Kinet, R., Brognaux, A., Delepierre, A. et al., Delvigne F: Phenotypic variability in bioprocessing conditions can be tracked on the basis of on-line flow cytometry and fits to a scaling law. Biotechnol. J. 2015, 10, 1316–1325.
Liu, J., Francois, J. M., Capp, J.-P., Use of noise in gene expression as an experimental parameter to test phenotypic effects. Yeast 2016, 33, 209–216.
Delvigne, F., Destain, J., Thonart, P., A methodology for the design of scale-down bioreactors by the use of mixing and circulation stochastic models. Biochem. Eng. J. 2006, 28, 256–268.
