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Bioprocess and Biosystems Engineering

, Volume 41, Issue 7, pp 889–916 | Cite as

Population heterogeneity in microbial bioprocesses: origin, analysis, mechanisms, and future perspectives

  • Anna-Lena Heins
  • Dirk Weuster-Botz
Critical Review
  • 300 Downloads

Abstract

Population heterogeneity is omnipresent in all bioprocesses even in homogenous environments. Its origin, however, is only so well understood that potential strategies like bet-hedging, noise in gene expression and division of labour that lead to population heterogeneity can be derived from experimental studies simulating the dynamics in industrial scale bioprocesses. This review aims at summarizing the current state of the different parts of single cell studies in bioprocesses. This includes setups to visualize different phenotypes of single cells, computational approaches connecting single cell physiology with environmental influence and special cultivation setups like scale-down reactors that have been proven to be useful to simulate large-scale conditions. A step in between investigation of populations and single cells is studying subpopulations with distinct properties that differ from the rest of the population with sub-omics methods which are also presented here. Moreover, the current knowledge about population heterogeneity in bioprocesses is summarized for relevant industrial production hosts and mixed cultures, as they provide the unique opportunity to distribute metabolic burden and optimize production processes in a way that is impossible in traditional monocultures. In the end, approaches to explain the underlying mechanism of population heterogeneity and the evidences found to support each hypothesis are presented. For instance, population heterogeneity serving as a bet-hedging strategy that is used as coordinated action against bioprocess-related stresses while at the same time spreading the risk between individual cells as it ensures the survival of least a part of the population in any environment the cells encounter.

Keywords

Population heterogeneity Scale-down reactors Sub-omics Reporter strains Bet-hedging Noise in gene expression Mixed culture 

References

  1. 1.
    Schlüter JP, Czuppon P, Schauer O, Pfaffelhuber P, McIntosh M, Becker A (2015) Classification of phenotypic subpopulations in isogenic bacterial cultures by triple promoter probing at single cell level. J Biotechnol 198:3–14.  https://doi.org/10.1016/j.jbiotec.2015.01.021 Google Scholar
  2. 2.
    Delvigne F, Baert J, Sassi H, Fickers P, Grünberger A, Dusny C (2017) Taking control over microbial populations: current approaches for exploiting biological noise in bioprocesses. Biotechnol J.  https://doi.org/10.1002/biot.201600549 Google Scholar
  3. 3.
    Nikel PI, Silva-Rocha R, Benedetti I, de Lorenzo V (2014) The private life of environmental bacteria: pollutant biodegradation at the single cell level. Environ Microbiol 16(3):628–642.  https://doi.org/10.1111/1462-2920.12360 Google Scholar
  4. 4.
    Delvigne F, Zune Q, Lara AR, Al-Soud W, Sorensen SJ (2014) Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity. Trends Biotechnol 32(12):608–616.  https://doi.org/10.1016/j.tibtech.2014.10.002 Google Scholar
  5. 5.
    Lemoine A, Delvigne F, Bockisch A, Neubauer P, Junne S (2017) Tools for the determination of population heterogeneity caused by inhomogeneous cultivation conditions. J Biotechnol 251:84–93.  https://doi.org/10.1016/j.jbiotec.2017.03.020 Google Scholar
  6. 6.
    Yin H, Marshall D (2012) Microfluidics for single cell analysis. Curr Opin Biotechnol 23(1):110–119.  https://doi.org/10.1016/j.copbio.2011.11.002 Google Scholar
  7. 7.
    Huang NT, Zhang HL, Chung M-T, Seo JH, Kurabayashi K (2014) Recent advancements in optofluidics-based single-cell analysis: optical on-chip cellular manipulation, treatment, and property detection. Lab Chip 14(7):1230–1245.  https://doi.org/10.1039/c3lc51211h Google Scholar
  8. 8.
    Dusny C, Schmid A (2015) Microfluidic single-cell analysis links boundary environments and individual microbial phenotypes. Environ Microbiol 17(6):1839–1856.  https://doi.org/10.1111/1462-2920.12667 Google Scholar
  9. 9.
    Mustafi N, Grünberger A, Mahr R, Helfrich S, Nöh K, Blombach B, Kohlheyer D, Frunzke J (2014) Application of a genetically encoded biosensor for live cell imaging of L-valine production in pyruvate dehydrogenase complex-deficient Corynebacterium glutamicum strains. PLoS One 9(1):e85731.  https://doi.org/10.1371/journal.pone.0085731 Google Scholar
  10. 10.
    Martins BM, Locke JC (2015) Microbial individuality: how single-cell heterogeneity enables population level strategies. Curr Opin Microbiol 24:104–112.  https://doi.org/10.1016/j.mib.2015.01.003 Google Scholar
  11. 11.
    Ackermann M (2015) A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol 13(8):497–508.  https://doi.org/10.1038/nrmicro3491 Google Scholar
  12. 12.
    Kell D, Potgieter M, Pretorius E (2015) Individuality, phenotypic differentiation, dormancy and ‘persistence’ in culturable bacterial systems: commonalities shared by environmental, laboratory, and clinical microbiology. F1000Res 4:179.  https://doi.org/10.12688/f1000research.6709.2 Google Scholar
  13. 13.
    Shi X, Gao W, Wang J, Chao SH, Zhang W, Meldrum DR (2015) Measuring gene expression in single bacterial cells: recent advances in methods and micro-devices. Crit Rev Biotechnol 35(4):448–460.  https://doi.org/10.3109/07388551.2014.899556 Google Scholar
  14. 14.
    Delvigne F, Goffin P (2014) Microbial heterogeneity affects bioprocess robustness: dynamic single-cell analysis contributes to understanding of microbial populations. Biotechnol J 9(1):61–72.  https://doi.org/10.1002/biot.201300119 Google Scholar
  15. 15.
    Wang G, Tang W, Xia J, Chu J, Noorman H, van Gulik WM (2015) Integration of microbial kinetics and fluid dynamics toward model-driven scale-up of industrial bioprocesses. Eng Life Sci 15(1):20–29.  https://doi.org/10.1002/elsc.201400172 Google Scholar
  16. 16.
    de Jonge LP, Buijs NA, ten Pierick A, Deshmukh A, Zhao Z, Kiel JA, Heijnen JJ, van Gulik WM (2011) Scale-down of penicillin production in Penicillium chrysogenum. Biotechnol J 6(8):944–958.  https://doi.org/10.1002/biot.201000409 Google Scholar
  17. 17.
    Lara AR, Galindo E, Ramirez OT, Palomares LA (2006) Living with heterogeneities in bioreactors. Mol Biotechnol 34Google Scholar
  18. 18.
    Fritzsch FS, Dusny C, Frick O, Schmid A (2012) Single-cell analysis in biotechnology, systems biology, and biocatalysis. Annu Rev Chem Biomol Eng 3:129–155.  https://doi.org/10.1146/annurev-chembioeng-062011-081056 Google Scholar
  19. 19.
    Lidstrom ME, Konopka MC (2010) The role of physiological heterogeneity in microbial population behavior. Nat Chem Biol 6(10):705–712Google Scholar
  20. 20.
    Campbell K, Vowinckel J, Ralser M (2016) Cell-to-cell heterogeneity emerges as consequence of metabolic cooperation in a synthetic yeast community. Biotechnol J 11(9):1169–1178.  https://doi.org/10.1002/biot.201500301 Google Scholar
  21. 21.
    Buchholz J, Graf M, Freund A, Busche T, Kalinowski J, Blombach B, Takors R (2014) CO2/HCO3—perturbations of simulated large scale gradients in a scale-down device cause fast transcriptional responses in Corynebacterium glutamicum. Appl Microbiol Biotechnol.  https://doi.org/10.1007/s00253-014-6014-y)Google Scholar
  22. 22.
    Delvigne F, Pecheux H, Tarayre C (2015) Fluorescent reporter libraries as useful tools for optimizing microbial cell factories: a review of the current methods and applications. Front Bioeng Biotechnol 3:147.  https://doi.org/10.3389/fbioe.2015.00147 Google Scholar
  23. 23.
    Müller J, Hense BA, Fuchs TM, Utz M, Pötzsche C (2013) Bet-hedging in stochastically switching environments. J Theor Biol 336:144–157.  https://doi.org/10.1016/j.jtbi.2013.07.017 Google Scholar
  24. 24.
    Binder D, Probst C, Grünberger A, Hilgers F, Loeschcke A, Jaeger KE, Kohlheyer D, Drepper T (2016) Comparative single-cell analysis of different E. coli expression systems during microfluidic cultivation. PLoS One 11(8):e0160711.  https://doi.org/10.1371/journal.pone.0160711 Google Scholar
  25. 25.
    Levy SF (2016) Cellular heterogeneity: benefits besides bet-hedging. Curr Biol 26(9):R355-357.  https://doi.org/10.1016/j.cub.2016.03.034 Google Scholar
  26. 26.
    Schmidt AM, Fagerer SR, Jefimovs K, Buettner F, Marro C, Siringil EC, Boehlen KL, Pabst M, Ibanez AJ (2014) Molecular phenotypic profiling of a Saccharomyces cerevisiae strain at the single-cell level. Analyst 139(22):5709–5717.  https://doi.org/10.1039/c4an01119h Google Scholar
  27. 27.
    Löffler M, Simen JD, Jäger G, Schäferhoff K, Freund A, Takors R (2016) Engineering E. coli for large-scale production—strategies considering ATP expenses and transcriptional responses. Metab Eng 38:73–85.  https://doi.org/10.1016/j.ymben.2016.06.008 Google Scholar
  28. 28.
    Lindmeyer M, Jahn M, Vorpahl C, Müller S, Schmid A, Bühler B (2015) Variability in subpopulation formation propagates into biocatalytic variability of engineered Pseudomonas putida strains. Front Microbiol 6:1042.  https://doi.org/10.3389/fmicb.2015.01042 Google Scholar
  29. 29.
    Wielgoss S, Barrick JE, Tenaillon O, Wiser MJ, Dittmar WJ, Cruveiller S, Chane-Woon-Ming B, Medigue C, Lenski RE, Schneider D (2013) Mutation rate dynamics in a bacterial population reflect tension between adaptation and genetic load. Proc Natl Acad Sci USA 110(1):222–227.  https://doi.org/10.1073/pnas.1219574110 Google Scholar
  30. 30.
    Wiacek C, Müller S, Benndorf D (2006) A cytomic approach reveals population heterogeneity of Cupriavidus necator in response to harmful phenol concentrations. Proteomics 6(22):5983–5994.  https://doi.org/10.1002/pmic.200600244 Google Scholar
  31. 31.
    Kiviet DJ, Nghe P, Walker N, Boulineau S, Sunderlikova V, Tans SJ (2014) Stochasticity of metabolism and growth at the single-cell level. Nature 514(7522):376–379.  https://doi.org/10.1038/nature13582 Google Scholar
  32. 32.
    Davis KM, Isberg RR (2016) Defining heterogeneity within bacterial populations via single cell approaches. Bioessays 38(8):782–790.  https://doi.org/10.1002/bies.201500121 Google Scholar
  33. 33.
    Ghosh S, Chowdhury R, Bhattacharya P (2016) Mixed consortia in bioprocesses: role of microbial interactions. Appl Microbiol Biotechnol 100(10):4283–4295.  https://doi.org/10.1007/s00253-016-7448-1 Google Scholar
  34. 34.
    Hays SG, Patrick WG, Ziesack M, Oxman N, Silver PA (2015) Better together: engineering and application of microbial symbioses. Curr Opin Biotechnol 36:40–49.  https://doi.org/10.1016/j.copbio.2015.08.008 Google Scholar
  35. 35.
    Fernandes RL, Nierychlo M, Lundin L, Pedersen AE, Puentes Tellez PE, Dutta A, Carlquist M, Bolic A, Schäpper D, Brunetti AC, Helmark S, Heins AL, Jensen AD, Nopens I, Rottwitt K, Szita N, van Elsas JD, Nielsen PH, Martinussen J, Sorensen SJ, Lantz AE, Gernaey KV (2011) Experimental methods and modeling techniques for description of cell population heterogeneity. Biotechnol Adv 29(6):575–599.  https://doi.org/10.1016/j.biotechadv.2011.03.007 Google Scholar
  36. 36.
    Ambriz-Avina V, Contreras-Garduno JA, Pedraza-Reyes M (2014) Applications of flow cytometry to characterize bacterial physiological responses. Biomed Res Int 2014:461941.  https://doi.org/10.1155/2014/461941 Google Scholar
  37. 37.
    Davey HM, Winson MK (2003) Using flow cytometry to quantify microbial heterogeneity. Curr Issues Mol Biol (5):9–15Google Scholar
  38. 38.
    Spitzer MH, Nolan GP (2016) Mass cytometry: single cells, many features. Cell 165(4):780–791.  https://doi.org/10.1016/j.cell.2016.04.019 Google Scholar
  39. 39.
    Nebe-von-Caron G (2009) Standardization in microbial cytometry. Cytometry A 75(2):86–89.  https://doi.org/10.1002/cyto.a.20696 Google Scholar
  40. 40.
    Nebe-von-Caron G, Stephens PJ, Hewitt CJ, Powell JR, Badley RA (2000) Analysis of bacterial function by multi-colour fluorescence flow cytometry and single cell sorting. J Microbiol Methods (42):97–114Google Scholar
  41. 41.
    Kacmar J, Zamamiri A, Carlson R, Abu-Absi NR, Srienc F (2004) Single-cell variability in growing Saccharomyces cerevisiae cell populations measured with automated flow cytometry. J Biotechnol 109(3):239–254.  https://doi.org/10.1016/j.jbiotec.2004.01.003 Google Scholar
  42. 42.
    Bouchedja DN, Danthine S, Kar T, Fickers P, Boudjellal A, Delvigne F (2017) Online flow cytometry, an interesting investigation process for monitoring lipid accumulation, dimorphism, and cells’ growth in the oleaginous yeast Yarrowia lipolytica JMY 775. Bioresour Bioprocess 4(1):3.  https://doi.org/10.1186/s40643-016-0132-6 Google Scholar
  43. 43.
    Davey HM (2010) Prospects for the automation of analysis and interpretation of flow cytometric data. Cytometry A 77(1):3–5.  https://doi.org/10.1002/cyto.a.20835 Google Scholar
  44. 44.
    Binder D, Drepper T, Jaeger KE, Delvigne F, Wiechert W, Kohlheyer D, Grünberger A (2017) Homogenizing bacterial cell factories: analysis and engineering of phenotypic heterogeneity. Metab Eng 42:145–156.  https://doi.org/10.1016/j.ymben.2017.06.009 Google Scholar
  45. 45.
    Steffen V, Otten J, Engelmann S, Radek A, Limberg M, Koenig BW, Noack S, Wiechert W, Pohl M (2016) A toolbox of genetically encoded FRET-based biosensors for rapid l-Lysine analysis. Sens (Basel) 16(10.  https://doi.org/10.3390/s16101604
  46. 46.
    Mahr R, Frunzke J (2016) Transcription factor-based biosensors in biotechnology: current state and future prospects. Appl Microbiol Biotechnol 100(1):79–90.  https://doi.org/10.1007/s00253-015-7090-3 Google Scholar
  47. 47.
    Rogers JK, Chruch GM (2016) Genetically encoded sensors enable real-time observation of metabolite production. PNAS 113(9)Google Scholar
  48. 48.
    Longo D, Hasty J (2006) Dynamics of single-cell gene expression. Mol Syst Biol 2:64.  https://doi.org/10.1038/msb4100110 Google Scholar
  49. 49.
    Polizzi KM, Kontoravdi C (2015) Genetically-encoded biosensors for monitoring cellular stress in bioprocessing. Curr Opin Biotechnol 31:50–56.  https://doi.org/10.1016/j.copbio.2014.07.011 Google Scholar
  50. 50.
    Cheng Vollmer A, Van Dyk TK (2004) Advances in microbial physiology, vol 49. Elsevier, OxfordGoogle Scholar
  51. 51.
    Delvigne F, Boxus M, Ingels S, Thonart P (2009) Bioreactor mixing efficiency modulates the activity of a prpoS::GFP reporter gene in E. coli. Microb Cell Fact 8:15.  https://doi.org/10.1186/1475-2859-8-15 Google Scholar
  52. 52.
    Sunya S, Delvigne F, Uribelarrea J-L, Molina-Jouve C, Gorret N (2012) Comparison of the transient responses of Escherichia coli to a glucose pulse of various intensities. Appl Microbiol Biotechnol 95:1021–1034.  https://doi.org/10.1007/s00253-012-3938-y)Google Scholar
  53. 53.
    Attfield PV, Choi HY, Veal DA, Bell PJL (2001) Heterogeneity of stress gene expression and stress resistance among individual cells of Saccharomyces cerevisiae. Mol Microbiol 40(4):1000–1008Google Scholar
  54. 54.
    Nisamedtinov I, Lindsey GG, Karreman R, Orumets K, Koplimaa M, Kevvai K, Paalme T (2008) The response of the yeast Saccharomyces cerevisiae to sudden vs. gradual changes in environmental stress monitored by expression of the stress response protein Hsp12p. FEMS Yeast Res 8(6):829–838.  https://doi.org/10.1111/j.1567-1364.2008.00391.x Google Scholar
  55. 55.
    Carlquist M, Lencastre Fernandes R, Helmark S, Heins AL, Lundin L, Sorensen SJ, Gernaey KV, Eliasson Lantz A (2012) Physiological heterogeneities in microbial populations and implications for physical stress tolerance. Microbial Cell Fact 11:(94)Google Scholar
  56. 56.
    Han S, Delvigne F, Brognaux A, Charbon GE, Sorensen SJ (2013) Design of growth-dependent biosensors based on destabilized GFP for the detection of physiological behavior of Escherichia coli in heterogeneous bioreactors. Biotechnol Prog 29(2):553–563.  https://doi.org/10.1002/btpr.1694 Google Scholar
  57. 57.
    Brognaux A, Han S, Sorensen SJ, Lebeau F, Thonart P, Delvigne F (2013) A low-cost, multiplexable, automated flow cytometry procedure for the characterization of microbial stress dynamics in bioreactors. Microbial Cell Fact 12:100Google Scholar
  58. 58.
    Garcia JR, Cha HJ, Rao G, Marten MR, Bentley WE (2009) Microbial nar-GFP cell sensors reveal oxygen limitations in highly agitated and aerated laboratory-scale fermentors. Microb Cell Fact 8:6.  https://doi.org/10.1186/1475-2859-8-6 Google Scholar
  59. 59.
    Delvigne F, Brognaux A, Francis F, Twizere JC, Gorret N, Sorensen SJ, Thonart P (2011) Green fluorescent protein (GFP) leakage from microbial biosensors provides useful information for the evaluation of the scale-down effect. Biotechnol J 6(8):968–978.  https://doi.org/10.1002/biot.201000410 Google Scholar
  60. 60.
    Brognaux A, Francis F, Twizere JC, Thonart P, Delvigne F (2014) Scale-down effect on the extracellular proteome of Escherichia coli: correlation with membrane permeability and modulation according to substrate heterogeneities. Bioprocess Biosyst Eng 37:1469–1485.  https://doi.org/10.1007/s00449-013-1119-8)Google Scholar
  61. 61.
    de Jong IG, Veening JW, Kuipers OP (2012) Single cell analysis of gene expression patterns during carbon starvation in Bacillus subtilis reveals large phenotypic variation. Environ Microbiol 14(12):3110–3121.  https://doi.org/10.1111/j.1462-2920.2012.02892.x Google Scholar
  62. 62.
    Acar M, Mettetal JT, van Oudenaarden A (2008) Stochastic switching as a survival strategy in fluctuating environments. Nat Genet 40(4):471–475.  https://doi.org/10.1038/ng.110 Google Scholar
  63. 63.
    Knudsen JD, Carlquist M, Gorwa-Grauslund M (2014) NADH-dependent biosensor in Saccharomyces cerevisiae: principle and validation at the single cell level. AMB Express 4:(81)Google Scholar
  64. 64.
    Knudsen JD, Johanson T, Eliasson Lantz A, Carlquist M (2015) Exploring the potential of the glycerol-3-phosphate dehydrogenase 2 (GPD2) promoter for recombinant gene expression in Saccharomyces cerevisiae. Biotechnol Rep (Amst) 7:107–119.  https://doi.org/10.1016/j.btre.2015.06.001 Google Scholar
  65. 65.
    Gustavsson R, Mandenius CF (2013) Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein. Bioprocess Biosyst Eng 36(10):1375–1384.  https://doi.org/10.1007/s00449-012-0840-z Google Scholar
  66. 66.
    Ganesh I, Ravikumar S, Yoo I, Hong SH (2015) Construction of malate-sensing Escherichia coli by introduction of a novel chimeric two-component system. Bioprocess Biosyst Eng 38:797–804.  https://doi.org/10.1007/s00449-014-1321-3)Google Scholar
  67. 67.
    Binder S, Schendzielorz G, Stäbler N, Krumbach K, Hoffmann K, Bott M, Eggeling L (2012) A high-throughput approach to identify genomic variants of bacterial metabolite producers at the single-cell level. Genome Biol 13:(R40)Google Scholar
  68. 68.
    Eggeling L, Bott M, Marienhagen J (2015) Novel screening methods–biosensors. Curr Opin Biotechnol 35:30–36.  https://doi.org/10.1016/j.copbio.2014.12.021 Google Scholar
  69. 69.
    Schallmey M, Frunzke J, Eggeling L, Marienhagen J (2014) Looking for the pick of the bunch: high-throughput screening of producing microorganisms with biosensors. Curr Opin Biotechnol 26:148–154.  https://doi.org/10.1016/j.copbio.2014.01.005 Google Scholar
  70. 70.
    Melendez J, Patel M, Oakes BL, Xu P, Morton P, McClean MN (2014) Real-time optogenetic control of intracellular protein concentration in microbial cell cultures. Integr Biol (Camb) 6(3):366–372.  https://doi.org/10.1039/c3ib40102b Google Scholar
  71. 71.
    Zadran S, Standley S, Wong K, Otiniano E, Amighi A, Baudry M (2012) Fluorescence resonance energy transfer (FRET)-based biosensors: visualizing cellular dynamics and bioenergetics. Appl Microbiol Biotechnol 96(4):895–902.  https://doi.org/10.1007/s00253-012-4449-6 Google Scholar
  72. 72.
    Mohsin M, Ahmad A, Iqbal M (2015) FRET-based genetically-encoded sensors for quantitative monitoring of metabolites. Biotechnol Lett 37(10):1919–1928.  https://doi.org/10.1007/s10529-015-1873-6 Google Scholar
  73. 73.
    Miesenböck G, De Angelis DA, Rothman JE (1998) Visualizing secretion and synaptic transmission with pH-sensitive green fluorescent proteins. Nature 394Google Scholar
  74. 74.
    van Beilen JW, Brul S (2013) Compartment-specific pH monitoring in Bacillus subtilis using fluorescent sensor proteins: a tool to analyze the antibacterial effect of weak organic acids. Front Microbiol 4:157.  https://doi.org/10.3389/fmicb.2013.00157 Google Scholar
  75. 75.
    Valkonen M, Mojzita D, Penttila M, Bencina M (2013) Noninvasive high-throughput single-cell analysis of the intracellular pH of Saccharomyces cerevisiae by ratiometric flow cytometry. Appl Environ Microbiol 79(23):7179–7187.  https://doi.org/10.1128/AEM.02515-13 Google Scholar
  76. 76.
    Pandey R, Vischer NO, Smelt JP, van Beilen JW, Ter Beek A, De Vos WH, Brul S, Manders EM (2016) Intracellular pH response to weak acid stress in individual vegetative Bacillus subtilis cells. Appl Environ Microbiol 82(21):6463–6471.  https://doi.org/10.1128/AEM.02063-16 Google Scholar
  77. 77.
    Maresova L, Hoskova B, Urbankova E, Chaloupka R, Sychrova H (2010) New applications of pHluorin–measuring intracellular pH of prototrophic yeasts and determining changes in the buffering capacity of strains with affected potassium homeostasis. Yeast 27(6):317–325.  https://doi.org/10.1002/yea.1755 Google Scholar
  78. 78.
    Orij R, Postmus J, Ter Beek A, Brul S, Smits GJ (2009) In vivo measurement of cytosolic and mitochondrial pH using a pH-sensitive GFP derivative in Saccharomyces cerevisiae reveals a relation between intracellular pH and growth. Microbiology 155(Pt 1):268–278.  https://doi.org/10.1099/mic.0.022038-0 Google Scholar
  79. 79.
    Schuster S, Enzelberger M, Trauthwein H, Schmid RD, Urlacher VB (2005) pHluorin-based in vivo assay for hydrolase screening. Anal Chem 77:2727–2732Google Scholar
  80. 80.
    Ayer A, Sanwald J, Pillay BA, Meyer AJ, Perrone GG, Dawes IW (2013) Distinct redox regulation in sub-cellular compartments in response to various stress conditions in Saccharomyces cerevisiae. PLoS One 8(6):e65240.  https://doi.org/10.1371/journal.pone.0065240 Google Scholar
  81. 81.
    Mahon MJ (2011) pHluorin2: an enhanced, ratiometric, pH-sensitive green florescent protein. Adv Biosci Biotechnol 2(3):132–137.  https://doi.org/10.4236/abb.2011.23021 Google Scholar
  82. 82.
    Mahr R, von Boeselager RF, Wiechert J, Frunzke J (2016) Screening of an Escherichia coli promoter library for a phenylalanine biosensor. Appl Microbiol Biotechnol 100(15):6739–6753.  https://doi.org/10.1007/s00253-016-7575-8 Google Scholar
  83. 83.
    Stiefel P, Schmidt-Emrich S, Maniura-Weber K, Ren Q (2015) Critical aspects of using bacterial cell viability assays with the fluorophores SYTO9 and propidium iodide. BMC Microbiol 15:36.  https://doi.org/10.1186/s12866-015-0376-x Google Scholar
  84. 84.
    Marba-Adebol AM, Turon X, Neubauer P, Junne S (2015) Application of flow cytometry analysis to elucidate the impact of scale-down conditions in Escherichia coli cultivationsGoogle Scholar
  85. 85.
    Shi L, Günther S, Hübschmann T, Wick LY, Harms H, Müller S (2007) Limits of propidium iodide as a cell viability indicator for environmental bacteria. Cytometry A 71(8):592–598.  https://doi.org/10.1002/cyto.a.20402 Google Scholar
  86. 86.
    Rezaeinejad S, Ivanov V (2011) Heterogeneity of Escherichia coli population by respiratory activity and membrane potential of cells during growth and long-term starvation. Microbiol Res 166(2):129–135.  https://doi.org/10.1016/j.micres.2010.01.007 Google Scholar
  87. 87.
    Müller S, Nebe-von-Caron G (2010) Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities. FEMS Microbiol Rev 34(4):554–587.  https://doi.org/10.1111/j.1574-6976.2010.00214.x Google Scholar
  88. 88.
    Sträuber H, Müller S (2010) Viability states of bacteria-specific mechanisms of selected probes. Cytometry A 77(7):623–634.  https://doi.org/10.1002/cyto.a.20920 Google Scholar
  89. 89.
    Buysschaert B, Byloos B, Leys N, Van Houdt R, Boon N (2016) Reevaluating multicolor flow cytometry to assess microbial viability. Appl Microbiol Biotechnol 100(21):9037–9051.  https://doi.org/10.1007/s00253-016-7837-5 Google Scholar
  90. 90.
    Quiros C, Herrero M, Garcia LA, Diaz M (2007) Application of flow cytometry to segregated kinetic modeling based on the physiological states of microorganisms. Appl Environ Microbiol 73(12):3993–4000.  https://doi.org/10.1128/AEM.00171-07 Google Scholar
  91. 91.
    Alonso S, Rendueles M, Diaz M (2012) Physiological heterogeneity of Pseudomonas taetrolens during lactobionic acid production. Appl Microbiol Biotechnol 96(6):1465–1477.  https://doi.org/10.1007/s00253-012-4254-2 Google Scholar
  92. 92.
    Benbadis L, Cot M, Rigoulet M, Francois J (2009) Isolation of two cell populations from yeast during high-level alcoholic fermentation that resemble quiescent and nonquiescent cells from the stationary phase on glucose. FEMS Yeast Res 9(8):1172–1186.  https://doi.org/10.1111/j.1567-1364.2009.00553.x Google Scholar
  93. 93.
    Freitas C, Neves E, Reis A, Passarinho PC, da Silva TL (2013) Use of multi-parameter flow cytometry as tool to monitor the impact of formic acid on Saccharomyces carlsbergensis batch ethanol fermentations. Appl Biochem Biotechnol 169(7):2038–2048.  https://doi.org/10.1007/s12010-012-0055-4 Google Scholar
  94. 94.
    Tibayrenc P, Preziosi-Belloy L, Ghommidh C (2011) Single-cell analysis of S. cerevisiae growth recovery after a sublethal heat-stress applied during an alcoholic fermentation. J Ind Microbiol Biotechnol 38:687–696.  https://doi.org/10.1007/s10295-010-0814-6)Google Scholar
  95. 95.
    Amillastre E, Aceves-Lara CA, Uribelarrea JL, Alfenore S, Guillouet SE (2012) Dynamic model of temperature impact on cell viability and major product formation during fed-batch and continuous ethanolic fermentation in Saccharomyces cerevisiae. Bioresour Technol 117:242–250.  https://doi.org/10.1016/j.biortech.2012.04.013 Google Scholar
  96. 96.
    Tashyreva D, Elster J, Billi D (2013) A novel staining protocol for multiparameter assessment of cell heterogeneity in Phormidium populations (cyanobacteria) employing fluorescent dyes. PLoS One 8(2):e55283.  https://doi.org/10.1371/journal.pone.0055283 Google Scholar
  97. 97.
    Müller S, Babel W (2003) Analysis of bacterial DNA patterns—an approach for controlling biotechnological processes. J Microbiol Methods 55(3):851–858.  https://doi.org/10.1016/j.mimet.2003.08.003 Google Scholar
  98. 98.
    Müller S (2007) Modes of cytometric bacterial DNA pattern: a tool for pursuing growth. Cell Prolif 40(5):621–639.  https://doi.org/10.1111/j.1365-2184.2007.00465.x Google Scholar
  99. 99.
    Cipollina C, Alberghina L, Porro D, Vai M (2005) SFP1 is involved in cell size modulation in respiro-fermentative growth conditions. Yeast 22(5):385–399.  https://doi.org/10.1002/yea.1218 Google Scholar
  100. 100.
    Porro D, Vai M, Vanoni M, Alberghina L, Hatzis C (2009) Analysis and modeling of growing budding yeast populations at the single cell level. Cytometry A 75(2):114–120.  https://doi.org/10.1002/cyto.a.20689 Google Scholar
  101. 101.
    Lieder S, Jahn M, Seifert J, Von Bergen M, Müller S, Takors R (2014) Subpopulation-proteomics reveal growth rate, but not cell cycling, as a major impact on protein composition in Psudomonas putida KT2440. AMB Express 4:(71)Google Scholar
  102. 102.
    Jahn M, Seifert J, Hübschmann T, Von Bergen M, Harms H, Müller S (2013) Comparison of preservation methods for bacterial cells in cytomics and proteomics. J Integr OMICS.  https://doi.org/10.5584/jiomics.v3i1.115 Google Scholar
  103. 103.
    Jehmlich N, Hübschmann T, Gesell Salazar M, Völker U, Benndorf D, Müller S, von Bergen M, Schmidt F (2010) Advanced tool for characterization of microbial cultures by combining cytomics and proteomics. Appl Microbiol Biotechnol 88(2):575–584.  https://doi.org/10.1007/s00253-010-2753-6 Google Scholar
  104. 104.
    Nebe-von-Caron G, Stephens PJ, Hewitt CJ, Powell JR, Badley RA (2000) Analysis of bacterial function by multi-colour fluorescence flow cytometry and single cell sorting. J Microbiol Methods 42(1):97–114.  https://doi.org/10.1016/S0167-7012(00)00181-0 Google Scholar
  105. 105.
    Hewitt CJ, Nebe-von-Caron G (2001) An industrial application of multiparameter flow cytometry: assessment of cell physiological state and its application to the study of microbial fermentations. Cytometry 44(17):179–187Google Scholar
  106. 106.
    Hewitt CJ, Nebe-Von Caron G, Nienow AW, McFarlane CM (1999) Use of multi-staining flow cytometry to characterise the physiological state of Escherichia coli W3110 in high cell density fed-batch cultures. Biotechnol Bioeng 63 (6):705–711. https://doi.org/10.1002/(SICI)1097-0290(19990620)63:6<705::AID-BIT8>3.0.CO;2-MGoogle Scholar
  107. 107.
    Want A, Thomas OR, Kara B, Liddell J, Hewitt CJ (2009) Studies related to antibody fragment (Fab) production in Escherichia coli W3110 fed-batch fermentation processes using multiparameter flow cytometry. Cytometry A 75(2):148–154.  https://doi.org/10.1002/cyto.a.20683 Google Scholar
  108. 108.
    Gonzalez-Penas H, Lu-Chau TA, Moreira MT, Lema JM (2015) Assessment of morphological changes of Clostridium acetobutylicum by flow cytometry during acetone/butanol/ethanol extractive fermentation. Biotechnol Lett 37(3):577–584.  https://doi.org/10.1007/s10529-014-1702-3 Google Scholar
  109. 109.
    Kolek J, Branska B, Drahokoupil M, Patakova P, Melzoch K (2016) Evaluation of viability, metabolic activity and spore quantity in clostridial cultures during ABE fermentation. FEMS Microbiol Lett 363(6.  https://doi.org/10.1093/femsle/fnw031
  110. 110.
    Garcia-Torreiro M, Lopez-Abelairas M, Lu-Chau TA, Lema JM (2017) Application of flow cytometry for monitoring the production of poly(3-hydroxybutyrate) by Halomonas boliviensis. Biotechnol Prog 33(2):276–284.  https://doi.org/10.1002/btpr.2373 Google Scholar
  111. 111.
    Kacmar J, Carlson R, Balogh SJ, Srienc F (2006) Staining and quantification of poly-3-hydroxybutyrate in Saccharomyces cerevisiae and Cupriavidus necator cell populations using automated flow cytometry. Cytometry A 69(1):27–35.  https://doi.org/10.1002/cyto.a.20197 Google Scholar
  112. 112.
    Rubbens P, Props R, Boon N, Waegeman W (2017) Flow cytometric single-cell identification of populations in synthetic bacterial communities. PLoS One 12(1):e0169754.  https://doi.org/10.1371/journal.pone.0169754 Google Scholar
  113. 113.
    Pawelczyk S, Abraham WR, Harms H, Müller S (2008) Community-based degradation of 4-chorosalicylate tracked on the single cell level. J Microbiol Methods 75(1):117–126.  https://doi.org/10.1016/j.mimet.2008.05.018 Google Scholar
  114. 114.
    Garcia C, Rendueles M, Diaz M (2017) Microbial amensalism in Lactobacillus casei and Pseudomonas taetrolens mixed culture. Bioprocess Biosyst Eng 40(7):1111–1122.  https://doi.org/10.1007/s00449-017-1773-3 Google Scholar
  115. 115.
    Noorman H (2011) An industrial perspective on bioreactor scale-down: what we can learn from combined large-scale bioprocess and model fluid studies. Biotechnol J 6(8):934–943.  https://doi.org/10.1002/biot.201000406 Google Scholar
  116. 116.
    Papagianni M (2015) Methodologies for scale-down of microbial bioprocesses. J Microbial Biochem Technol.  https://doi.org/10.4172/1948-5948.s5-001 Google Scholar
  117. 117.
    Lorantfy B, Jazini M, Herwig C (2013) Investigation of the physiological response to oxygen limited process conditions of Pichia pastoris Mut(+) strain using a two-compartment scale-down system. J Biosci Bioeng 116(3):371–379.  https://doi.org/10.1016/j.jbiosc.2013.03.021 Google Scholar
  118. 118.
    Brognaux A, Neubauer P, Twizere JC, Francis F, Gorret N, Thonart P, Delvigne F (2013) Direct and indirect use of GFP whole cell biosensors for the assessment of bioprocess performances: design of milliliter scale-down bioreactors. Biotechnol Prog 29(1):48–59.  https://doi.org/10.1002/btpr.1660 Google Scholar
  119. 119.
    Käß F, Junne S, Neubauer P, Wiechert W, Oldiges M (2014) Process inhomogeneity leads to rapid side product turnover in cultivation of Corynebacterium glutamicum. Microbial Cell Fact 13:6Google Scholar
  120. 120.
    Simen JD, Löffler M, Jäger G, Schäferhoff K, Freund A, Matthes J, Müller J, Takors R, RecogNice T (2017) Transcriptional response of Escherichia coli to ammonia and glucose fluctuations. Microb Biotechnol.  https://doi.org/10.1111/1751-7915.12713 Google Scholar
  121. 121.
    Lemoine A, Maya Martiotanez-Iturralde N, Spann R, Neubauer P, Junne S (2015) Response of Corynebacterium glutamicum exposed to oscillating cultivation conditions in a two- and a novel three-compartment scale-down bioreactor. Biotechnol Bioeng 112(6):1220–1231.  https://doi.org/10.1002/bit.25543 Google Scholar
  122. 122.
    Lara AR, Vazquez-Limon C, Gosset G, Bolivar F, Lopez-Munguia A, Ramirez OT (2006) Engineering Escherichia coli to improve culture performance and reduce formation of by-products during recombinant protein production under transient intermittent anaerobic conditions. Biotechnol Bioeng 94(6):1164–1175.  https://doi.org/10.1002/bit.20954 Google Scholar
  123. 123.
    Lara AR, Leal L, Flores N, Gosset G, Bolivar F, Ramirez OT (2006) Transcriptional and metabolic response of recombinant Escherichia coli to spatial dissolved oxygen tension gradients simulated in a scale-down system. Biotechnol Bioeng 93(2):372–385.  https://doi.org/10.1002/bit.20704 Google Scholar
  124. 124.
    Heins A-L, Lencastre Fernandes R, Gernaey KV, Lantz AE (2015) Experimental andin silicoinvestigation of population heterogeneity in continuous Saccharomyces cerevisiae scale-down fermentation in a two-compartment setup. J Chem Technol Biotechnol 90(2):324–340.  https://doi.org/10.1002/jctb.4532 Google Scholar
  125. 125.
    de Jonge L, Buijs NA, Heijnen JJ, van Gulik WM, Abate A, Wahl SA (2014) Flux response of glycolysis and storage metabolism during rapid feast/famine conditions in Penicillium chrysogenum using dynamic (13)C labeling. Biotechnol J 9(3):372–385.  https://doi.org/10.1002/biot.201200260 Google Scholar
  126. 126.
    Schäpper D, Alam MN, Szita N, Eliasson Lantz A, Gernaey KV (2009) Application of microbioreactors in fermentation process development: a review. Anal Bioanal Chem 395(3):679–695.  https://doi.org/10.1007/s00216-009-2955-x Google Scholar
  127. 127.
    Bolic A, Larsson H, Hugelier S, Eliasson Lantz A, Krühne U, Gernaey KV (2016) A flexible well-mixed milliliter-scale reactor with high oxygen transfer rate for microbial cultivations. Chem Eng J 303:655–666.  https://doi.org/10.1016/j.cej.2016.05.117 Google Scholar
  128. 128.
    Weuster-Botz D, Puskeiler R, Kusterer A, Kaufmann K, John GT, Arnold M (2005) Methods and milliliter scale devices for high-throughput bioprocess design. Bioprocess Biosyst Eng 28(2):109–119.  https://doi.org/10.1007/s00449-005-0011-6 Google Scholar
  129. 129.
    Puskeiler R, Kaufmann K, Weuster-Botz D (2005) Development, parallelization, and automation of a gas-inducing milliliter-scale bioreactor for high-throughput bioprocess design (HTBD). Biotechnol Bioeng 89(5):512–523.  https://doi.org/10.1002/bit.20352 Google Scholar
  130. 130.
    Markert S, Joeris K (2017) Establishment of a fully automated microtiter plate-based system for suspension cell culture and its application for enhanced process optimization. Biotechnol Bioeng 114(1):113–121.  https://doi.org/10.1002/bit.26044 Google Scholar
  131. 131.
    Zhang Z, Szita N, Boccazzi P, Sinskey AJ, Jensen KF (2006) A well-mixed, polymer-based microbioreactor with integrated optical measurements. Biotechnol Bioeng 93(2):286–296.  https://doi.org/10.1002/bit.20678 Google Scholar
  132. 132.
    Funke M, Buchenauer A, Mokwa W, Kluge S, Hein L, Müller C, Kensy F, Büchs J (2010) bioprocess control in microscale: scalable fermentations in disposible and user-friendly microfluidic systems. Microbial Cell Fact 9:(86)Google Scholar
  133. 133.
    Kensy F, Engelbrecht C, Büchs J (2009) Scale-up from microtiter plate to laboratory fermenter: evaluation by online monitoring techniques of growth and protein expression in Escherichia coli and Hansenula polymorpha fermentations. Microb Cell Fact 8:68.  https://doi.org/10.1186/1475-2859-8-68 Google Scholar
  134. 134.
    Edlich A, Magdanz V, Rasch D, Demming S, Zadeh SA, Segura R, Kähler C, Radespiel R, Büttgenbach S, Franco-Lara E, Krull R (2010) Microfluidic reactor for continuous cultivation of Saccharomyces cerevisiae. Biotechnol Prog 26(5)Google Scholar
  135. 135.
    Figallo E, Cannizzaro C, Gerecht S, Burdick JA, Langer R, Elvassore N, Vunjak-Novakovic G (2007) Micro-bioreactor array for controlling cellular microenvironments. Lab Chip 7(6):710–719.  https://doi.org/10.1039/b700063d Google Scholar
  136. 136.
    Puskeiler R, Kusterer A, John GT, Weuster-Botz D (2005) Miniature bioreactors for automated high-throughput bioprocess design (HTBD): reproducibility of parallel fed-batch cultivations with Escherichia coli. Biotechnol Appl Biochem 42(Pt 3):227–235.  https://doi.org/10.1042/BA20040197 Google Scholar
  137. 137.
    Hortsch R, Weuster-Botz D (2010) Milliliter-scale stirred tank reactors for the cultivation of microorganisms. 73:61–82  https://doi.org/10.1016/s0065-2164(10)73003-3
  138. 138.
    Bower DM, Lee KS, Ram RJ, Prather KL (2012) Fed-batch microbioreactor platform for scale down and analysis of a plasmid DNA production process. Biotechnol Bioeng 109(8):1976–1986.  https://doi.org/10.1002/bit.24498 Google Scholar
  139. 139.
    Kortmann H, Chasanis P, Blank LM, Franzke J, Kenig EY, Schmid A (2009) The Envirostat—a new bioreactor concept. Lab Chip 9(4):576–585.  https://doi.org/10.1039/b809150a Google Scholar
  140. 140.
    Jang K, Thi Ngo HT, Tanaka Y, Xu Y, Mawatari K, Kitamori T (2011) Development of a microfluidic platform for single-cell scretion analysis using a direct photoactive cell-attaching method. Anal Sci 27Google Scholar
  141. 141.
    Fritzsch FS, Rosenthal K, Kampert A, Howitz S, Dusny C, Blank LM, Schmid A (2013) Picoliter nDEP traps enable time-resolved contactless single bacterial cell analysis in controlled microenvironments. Lab Chip 13(3):397–408.  https://doi.org/10.1039/c2lc41092c Google Scholar
  142. 142.
    Grünberger A, Wiechert W, Kohlheyer D (2014) Single-cell microfluidics: opportunity for bioprocess development. Curr Opin Biotechnol 29:15–23.  https://doi.org/10.1016/j.copbio.2014.02.008 Google Scholar
  143. 143.
    Liu Y, Singh AK (2013) Microfluidic platforms for single-cell protein analysis. J Lab Autom 18(6):446–454.  https://doi.org/10.1177/2211068213494389 Google Scholar
  144. 144.
    Dusny C, Grünberger A, Probst C, Wiechert W, Kohlheyer D, Schmid A (2015) Technical bias of microcultivation environments on single-cell physiology. Lab Chip 15(8):1822–1834.  https://doi.org/10.1039/c4lc01270d Google Scholar
  145. 145.
    Au SH, Shih SC, Wheeler AR (2011) Integrated microbioreactor for culture and analysis of bacteria, algae and yeast. Biomed Microdev 13(1):41–50.  https://doi.org/10.1007/s10544-010-9469-3 Google Scholar
  146. 146.
    Grünberger A, Probst C, Helfrich S, Nanda A, Stute B, Wiechert W, von Lieres E, Nöh K, Frunzke J, Kohlheyer D (2015) Spatiotemporal microbial single-cell analysis using a high-throughput microfluidics cultivation platform. Cytometry A 87(12):1101–1115.  https://doi.org/10.1002/cyto.a.22779 Google Scholar
  147. 147.
    Long Z, Nugent E, Javer A, Cicuta P, Sclavi B, Cosentino Lagomarsino M, Dorfman KD (2013) Microfluidic chemostat for measuring single cell dynamics in bacteria. Lab Chip 13(5):947–954.  https://doi.org/10.1039/c2lc41196b Google Scholar
  148. 148.
    Grünberger A, Probst C, Heyer A, Wiechert W, Frunzke J, Kohlheyer D (2013) Microfluidic picoliter bioreactor for microbial single-cell analysis: fabrication, system setup, and operation. J Vis Exp (82):50560.  https://doi.org/10.3791/50560 Google Scholar
  149. 149.
    Kortmann H, Blank LM, Schmid A (2009) Single cell analysis reveals unexpected growth phenotype of S. cerevisiae. Cytometry A 75(2):130–139.  https://doi.org/10.1002/cyto.a.20684 Google Scholar
  150. 150.
    Dusny C, Fritzsch FS, Frick O, Schmid A (2012) Isolated microbial single cells and resulting micropopulations grow faster in controlled environments. Appl Environ Microbiol 78(19):7132–7136.  https://doi.org/10.1128/AEM.01624-12 Google Scholar
  151. 151.
    Taniguchi Y, Choi PJ, Li G-W, Chen H, Babu M, Hearn J, Emili A, Xie XS (2011) Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329:533–538Google Scholar
  152. 152.
    Achilles J, Stahl F, Harms H, Müller S (2007) Isolation of intact RNA from cytometrically sorted Saccharomyces cerevisiae for the analysis of intrapopulation diversity of gene expression. Nat Protoc 2(9):2203–2211.  https://doi.org/10.1038/nprot.2007.322 Google Scholar
  153. 153.
    Denervaud N, Becker J, Delgado-Gonzalo R, Damay P, Rajkumar AS, Unser M, Shore D, Naef F, Maerki SJ (2013) A cheomstat array enables the spatio-temporal analysis of the yeast genome. PNAS 110(39):15842–15847Google Scholar
  154. 154.
    Newman JR, Ghaemmaghami S, Ihmels J, Breslow DK, Noble M, DeRisi JL, Weissman JS (2006) Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441(7095):840–846.  https://doi.org/10.1038/nature04785 Google Scholar
  155. 155.
    Shahrezaei V, Marguerat S (2015) Connecting growth with gene expression: of noise and numbers. Curr Opin Microbiol 25:127–135.  https://doi.org/10.1016/j.mib.2015.05.012 Google Scholar
  156. 156.
    Wu M, Singh AK (2012) Single-cell protein analysis. Curr Opin Biotechnol 23(1):83–88.  https://doi.org/10.1016/j.copbio.2011.11.023 Google Scholar
  157. 157.
    Bendall SC, Simonds EF, Qiu P, Amir el AD, Krutzik PO, Finck R, Bruggner RV, Melamed R, Trejo A, Ornatsky OI, Balderas RS, Plevritis SK, Sachs K, Pe’er D, Tanner SD, Nolan GP (2011) Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332(6030):687–696.  https://doi.org/10.1126/science.1198704 Google Scholar
  158. 158.
    Jahn M, Seifert J, von Bergen M, Schmid A, Bühler B, Müller S (2013) Subpopulation-proteomics in prokaryotic populations. Curr Opin Biotechnol 24(1):79–87.  https://doi.org/10.1016/j.copbio.2012.10.017 Google Scholar
  159. 159.
    DeGennaro CM, Savir Y, Springer M (2016) Identifying metabolic subpopulations from population level mass spectrometry. PLoS One 11(3):e0151659.  https://doi.org/10.1371/journal.pone.0151659 Google Scholar
  160. 160.
    Rubakhin SS, Lanni EJ, Sweedler JV (2013) Progress toward single cell metabolomics. Curr Opin Biotechnol 24(1):95–104.  https://doi.org/10.1016/j.copbio.2012.10.021 Google Scholar
  161. 161.
    Guo Y, Baumgart S, Stärk H-J, Harms H, Müller S (2017) Mass cytometry for detection of silver at the bacterial single cell level. Front Microbiol.  https://doi.org/10.3389/fmicb.2017.01326 Google Scholar
  162. 162.
    Alonso AA, Molina I, Theodoropoulos C (2014) Modeling bacterial population growth from stochastic single-cell dynamics. Appl Environ Microbiol 80(17):5241–5253.  https://doi.org/10.1128/AEM.01423-14 Google Scholar
  163. 163.
    Lencastre Fernandes R, Carlquist M, Lundin L, Heins AL, Dutta A, Sorensen SJ, Jensen AD, Nopens I, Lantz AE, Gernaey KV (2013) Cell mass and cell cycle dynamics of an asynchronous budding yeast population: experimental observations, flow cytometry data analysis, and multi-scale modeling. Biotechnol Bioeng 110(3):812–826.  https://doi.org/10.1002/bit.24749 Google Scholar
  164. 164.
    Delvigne F, Destain J, Thonart P (2006) Toward a stochastic formulation of microbial growth in relation to bioreactor performances: case study of an E. coli fed-batch process. Biotechnol Prog 22:1114–1124Google Scholar
  165. 165.
    Delvigne F, Destain J, Thonart P (2006) A methodology for the design of scale-down bioreactors by the use of mixing and circulation stochastic models. Biochem Eng J 28(3):256–268.  https://doi.org/10.1016/j.bej.2005.11.009 Google Scholar
  166. 166.
    Melbinger A, Cremer J, Frey E (2010) Evolutionary game theory in growing populations. Phys Rev Lett 105(17):178101.  https://doi.org/10.1103/PhysRevLett.105.178101 Google Scholar
  167. 167.
    Hellweger FL, Frederick ND, Berges JA (2014) Age-correlated stress resistance improves fitness of yeast: support from agent-based simulations. BMC Syst Biol 8:(18)Google Scholar
  168. 168.
    Spetsieris K, Zygourakis K (2012) Single-cell behavior and population heterogeneity: solving an inverse problem to compute the intrinsic physiological state functions. J Biotechnol 158(3):80–90.  https://doi.org/10.1016/j.jbiotec.2011.08.018 Google Scholar
  169. 169.
    Henson MA (2003) Dynamic modeling of microbial cell populations. Curr Opin Biotechnol 14(5):460–467.  https://doi.org/10.1016/s0958-1669(03)00104-6 Google Scholar
  170. 170.
    Gonzalez-Cabaleiro R, Mitchell AM, Smith W, Wipat A, Ofiteru ID (2017) Heterogeneity in pure microbial systems: experimental measurements and modeling. Front Microbiol 8:1813.  https://doi.org/10.3389/fmicb.2017.01813 Google Scholar
  171. 171.
    Lapin A, Klann M, Reuss M (2010) Multi-scale spatio-temporal modeling: lifelines of microorganisms in bioreactors and tracking molecules in cells. Adv Biochem Eng Biotechnol 121:23–43.  https://doi.org/10.1007/10_2009_53 Google Scholar
  172. 172.
    Toedling J, Rhein P, Ratei R, Karawajew L, Spang R (2006) Automated in-silico detection of cell populations in flow cytometry readouts and its application to leukemia disease monitoring. BMC Bioinf 7:282.  https://doi.org/10.1186/1471-2105-7-282 Google Scholar
  173. 173.
    Roederer M, Hardy RR (2001) Frequency difference gating: a multivariate method for identifying subsets that differ between samples. Cytometry 45:56–64Google Scholar
  174. 174.
    Kelly WJ (2008) Using computational fluid dynamics to characterize and improve bioreactor performance. Biotechnol Appl Biochem 49(Pt 4):225–238.  https://doi.org/10.1042/BA20070177 Google Scholar
  175. 175.
    Sarkar J, Shekhawat LK, Loomba V, Rathore AS (2016) CFD of mixing of multi-phase flow in a bioreactor using population balance model. Biotechnol Prog 32(3):613–628.  https://doi.org/10.1002/btpr.2242 Google Scholar
  176. 176.
    Zou X, Xia JY, Chu J, Zhuang YP, Zhang SL (2012) Real-time fluid dynamics investigation and physiological response for erythromycin fermentation scale-up from 50 L to 132 m3 fermenter. Bioprocess Biosyst Eng 35(5):789–800.  https://doi.org/10.1007/s00449-011-0659-z Google Scholar
  177. 177.
    Enfors SO, Jahic M, Rozkov A, Xu B, Hecker M, Jürgen B, Krüger E, Schweder T, Hamer G, O’Beirne D, Noisommit-Rizzi N, Reuss M, Boone L, Hewitt CJ, McFarlane C, Nienow A, Tragardh TK, Fuchs C, Revstedt L, Friberg J, Hjertager PC, Blomsten B, Skogman G, Hjort H, Hoeks S, Lin F, Neubauer H-Y, van der Lans P, Luyben R, Vrabel K, Manelius PA (2001) Physiological responses to mixing in large scale bioreactors. J Biotechnol 85:175–185Google Scholar
  178. 178.
    McClure DD, Kavanagh JM, Fletcher DF, Barton GW (2016) Characterizing bubble column bioreactor performance using computational fluid dynamics. Chem Eng Sci 144:58–74.  https://doi.org/10.1016/j.ces.2016.01.016 Google Scholar
  179. 179.
    Haringa C, Tang W, Deshmukh AT, Xia J, Reuss M, Heijnen JJ, Mudde RF, Noorman HJ (2016) Euler–Lagrange computational fluid dynamics for (bio)reactor scale down: an analysis of organism lifelines. Eng Life Sci 16(7):652–663.  https://doi.org/10.1002/elsc.201600061 Google Scholar
  180. 180.
    Um B-H, Hanley TR (2008) A CFD model for predicting the flow patterns of viscous fluids in a bioreactor under various operating conditions. Korean J Chem Eng 25(5):1094–1102Google Scholar
  181. 181.
    Tang W, Deshmukh AT, Haringa C, Wang G, Van Gulik WM, Van Winden WA, Reuss M, Heijnen JJ, Xia JY, Chu J, Noorman H (2017) A 9-pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation by Penicillium chrysogenum. Biotechnol Bioeng.  https://doi.org/10.1002/bit.26294 Google Scholar
  182. 182.
    Chau TL, Guillan A, Roca E, Nunez MJ, Lema JM (2001) Population dynamics of a continuous fermentation of recombinant Saccharomyces cerevisiae using flow cytometry. Biotechnol Prog (17):951–957Google Scholar
  183. 183.
    Boender LG, Almering MJ, Dijk M, van Maris AJ, de Winde JH, Pronk JT, Daran-Lapujade P (2011) Extreme calorie restriction and energy source starvation in Saccharomyces cerevisiae represent distinct physiological states. Biochim Biophys Acta 1813(12):2133–2144.  https://doi.org/10.1016/j.bbamcr.2011.07.008 Google Scholar
  184. 184.
    van Heerden JH, Wortel MT, Bruggeman FJ, Heijnen JJ, Bollen YJ, Planque R, Hulshof J, O’Toole TG, Wahl SA, Teusink B (2014) Lost in transition: start-up of glycolysis yields subpopulations of nongrowing cells. Science 343(6174):1245114.  https://doi.org/10.1126/science.1245114 Google Scholar
  185. 185.
    Kar RK, Qureshi MT, DasAdhikari AK, Zahir T, Venkatesh KV, Bhat PJ (2014) Stochastic galactokinase expression underlies GAL gene induction in a GAL3 mutant of Saccharomyces cerevisiae. FEBS J 281(7):1798–1817.  https://doi.org/10.1111/febs.12741 Google Scholar
  186. 186.
    Bishop AL, Rab FA, Sumner ER, Avery SV (2007) Phenotypic heterogeneity can enhance rare-cell survival in ‘stress-sensitive’ yeast populations. Mol Microbiol 63(2):507–520.  https://doi.org/10.1111/j.1365-2958.2006.05504.x Google Scholar
  187. 187.
    Narayanan V, Schelin J, Gorwa-Grauslund M, van Niel EW, Carlquist M (2017) Increased lignocellulosic inhibitor tolerance of Saccharomyces cerevisiae cell populations in early stationary phase. Biotechnol Biofuels 10:114.  https://doi.org/10.1186/s13068-017-0794-0 Google Scholar
  188. 188.
    Swinnen S, Fernandez-Nino M, Gonzalez-Ramos D, van Maris AJ, Nevoigt E (2014) The fraction of cells that resume growth after acetic acid addition is a strain-dependent parameter of acetic acid tolerance in Saccharomyces cerevisiae. FEMS Yeast Res 14(4):642–653.  https://doi.org/10.1111/1567-1364.12151 Google Scholar
  189. 189.
    Vital-Lopez FG, Wallqvist A, Reifman J (2013) Bridging the gap between gene expression and metabolic phenotype via kinetic models. BMC Syst Biol 7:(63)Google Scholar
  190. 190.
    Levy SF, Ziv N, Siegal ML (2012) Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLoS Biol 10(5):e1001325.  https://doi.org/10.1371/journal.pbio.1001325 Google Scholar
  191. 191.
    Avraham N, Soifer I, Carmi M, Barkai N (2013) Increasing population growth by asymmetric segregation of a limiting resource during cell division. Mol Syst Biol 9:656.  https://doi.org/10.1038/msb.2013.13 Google Scholar
  192. 192.
    Lewis G, Taylor IW, Nienow AW, Hewitt CJ (2004) The application of multi-parameter flow cytometry to the study of recombinant Escherichia coli batch fermentation processes. J Ind Microbiol Biotechnol 31(7):311–322.  https://doi.org/10.1007/s10295-004-0151-8 Google Scholar
  193. 193.
    Széliová D, Krahulec J, Šafránek M, Lišková V, Turňa J (2016) Modulation of heterologous expression from PBAD promoter in Escherichia coli production strains. J Biotechnol 236:1–9.  https://doi.org/10.1016/j.jbiotec.2016.08.004 Google Scholar
  194. 194.
    Wyre C, Overton TW (2014) Flow cytometric analysis of E. coli on agar plates: implications for recombinant protein production. Biotechnol Lett 36(7):1485–1494.  https://doi.org/10.1007/s10529-014-1511-8 Google Scholar
  195. 195.
    Zhao R, Natajaran A, Srienc F (1999) A Flow injection flow cytometry system for on-line monitoring of bioreactors. Biotechnol Bioeng 62:5Google Scholar
  196. 196.
    Wallberg F, Sundström H, Ledung E, Hewitt CJ, Enfors SO (2005) Monitoring and quantification of inclusion body formation in Escherichia coli by multi-parameter flow cytometry. Biotechnol Lett 27(13):919–926.  https://doi.org/10.1007/s10529-005-7184-6 Google Scholar
  197. 197.
    Labhsetwar P, Cole JA, Roberts E, Price ND, Luthey-Schulten ZA (2013) Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population. Proc Natl Acad Sci USA 110(34):14006–14011.  https://doi.org/10.1073/pnas.1222569110 Google Scholar
  198. 198.
    Taymaz-Nikerel H, van Gulik WM, Heijnen JJ (2011) Escherichia coli responds with a rapid and large change in growth rate upon a shift from glucose-limited to glucose-excess conditions. Metab Eng 13(3):307–318.  https://doi.org/10.1016/j.ymben.2011.03.003 Google Scholar
  199. 199.
    Grefen O, Fridman O, Ronin I, Balaban NQ (2013) Direct observation of single stationary-phase bacteria reveals a surprisingly long period of constant protein production activity. PNAS 111(1):556–561Google Scholar
  200. 200.
    Roostalu J, Joers A, Luidalepp H, Kaldalu N, Tenson T (2008) Cell division in Escherichia coli cultures monitored at single cell resolution. BMC Microbiol 8:68.  https://doi.org/10.1186/1471-2180-8-68 Google Scholar
  201. 201.
    Joers A, Tenson T (2016) Growth resumption from stationary phase reveals memory in Escherichia coli cultures. Sci Rep 6:24055.  https://doi.org/10.1038/srep24055 Google Scholar
  202. 202.
    Inoue I, Wakamoto Y, Moriguchi H, Okano K, Yasuda K (2001) On-chip culture system for observation of isolated individual cells. Lab Chip 1(1):50–55.  https://doi.org/10.1039/b103931h Google Scholar
  203. 203.
    Hashimoto M, Nozoe T, Nakaoka H, Okura R, Akiyoshi S, Kaneko K, Kussell E, Wakamoto Y (2016) Noise-driven growth rate gain in clonal cellular populations. Proc Natl Acad Sci USA 113(12):3251–3256.  https://doi.org/10.1073/pnas.1519412113 Google Scholar
  204. 204.
    Saint-Ruf C, Garfa-Traore M, Collin V, Cordier C, Franceschi C, Matic I (2014) Massive diversification in aging colonies of Escherichia coli. J Bacteriol 196(17):3059–3073.  https://doi.org/10.1128/JB.01421-13 Google Scholar
  205. 205.
    Figueira R, Brown DR, Ferreira D, Eldridge MJ, Burchell L, Pan Z, Helaine S, Wigneshweraraj S (2015) Adaptation to sustained nitrogen starvation by Escherichia coli requires the eukaryote-like serine/threonine kinase YeaG. Sci Rep 5:17524.  https://doi.org/10.1038/srep17524 Google Scholar
  206. 206.
    Van Derlinden E, Boons K, Van Impe JF (2011) Escherichia coli population heterogeneity: subpopulation dynamics at super-optimal temperatures. Food Microbiol 28(4):667–677.  https://doi.org/10.1016/j.fm.2010.06.015 Google Scholar
  207. 207.
    Takahashi H, Oshima T, Hobman JL, Doherty N, Clayton SR, Iqbal M, Hill PJ, Tobe T, Ogasawara N, Kanaya S, Stekel DJ (2015) The dynamic balance of import and export of zinc in Escherichia coli suggests a heterogeneous population response to stress. J R Soc Interface 12(106.  https://doi.org/10.1098/rsif.2015.0069
  208. 208.
    Maharjan R, Ferenci T (2016) Metastable coexistence of multiple genotypes in a constant environment with a single resource through fixed settings of a multiplication-survival trade-off. Res Microbiol 167(3):240–246.  https://doi.org/10.1016/j.resmic.2015.12.001 Google Scholar
  209. 209.
    Lindsey HA, Gallie J, Taylor S, Kerr B (2013) Evolutionary rescue from extinction is contingent on a lower rate of environmental change. Nature 494(7438):463–467.  https://doi.org/10.1038/nature11879 Google Scholar
  210. 210.
    Kamensek S, Podlesek Z, Gillor O, Zgur-Bertok D (2010) Genes regulated by the Escherichia coli SOS repressor LexA exhibit heterogeneous expression. BMC Microbiol 10:283.  https://doi.org/10.1186/1471-2180-10-283 Google Scholar
  211. 211.
    Bott M, Brocker M (2012) Two-component signal transduction in Corynebacterium glutamicum and other corynebacteria: on the way towards stimuli and targets. Appl Microbiol Biotechnol 94(5):1131–1150.  https://doi.org/10.1007/s00253-012-4060-x Google Scholar
  212. 212.
    Binder D, Frohwitter J, Mahr R, Bier C, Grünberger A, Loeschcke A, Peters-Wendisch P, Kohlheyer D, Pietruszka J, Frunzke J, Jaeger KE, Wendisch VF, Drepper T (2016) Light-controlled cell factories: employing photocaged isopropyl-beta-d-thiogalactopyranoside for light-mediated optimization of lac promoter-based gene expression and (+)-valencene biosynthesis in Corynebacterium glutamicum. Appl Environ Microbiol 82(20):6141–6149.  https://doi.org/10.1128/AEM.01457-16 Google Scholar
  213. 213.
    Kass F, Hariskos I, Michel A, Brandt HJ, Spann R, Junne S, Wiechert W, Neubauer P, Oldiges M (2014) Assessment of robustness against dissolved oxygen/substrate oscillations for C. glutamicum DM1933 in two-compartment bioreactor. Bioprocess Biosyst Eng 37(6):1151–1162.  https://doi.org/10.1007/s00449-013-1086-0 Google Scholar
  214. 214.
    Limberg MH, Schulte J, Aryani T, Mahr R, Baumgart M, Bott M, Wiechert W, Oldiges M (2017) Metabolic profile of 1,5-diaminopentane producing Corynebacterium glutamicum under scale-down conditions: blueprint for robustness to bioreactor inhomogeneities. Biotechnol Bioeng 114(3):560–575.  https://doi.org/10.1002/bit.26184 Google Scholar
  215. 215.
    Seletzky JM, Noack U, Fricke J, Hahn S, Büchs J (2006) Metabolic activity of Corynebacterium glutamicum grown on L-lactic acid under stress. Appl Microbiol Biotechnol 72(6):1297–1307.  https://doi.org/10.1007/s00253-006-0436-0 Google Scholar
  216. 216.
    Neumeyer A, Hübschmann T, Müller S, Frunzke J (2013) Monitoring of population dynamics of Corynebacterium glutamicum by multiparameter flow cytometry. Microb Biotechnol 6(2):157–167.  https://doi.org/10.1111/1751-7915.12018 Google Scholar
  217. 217.
    Käß F, Prasad A, Tillack J, Moch M, Giese H, Büchs J, Wiechert W, Oldiges M (2014) Rapid assessment of oxygen transfer impact for Corynebacterium glutamicum. Bioprocess Biosyst Eng 37(12):2567–2577.  https://doi.org/10.1007/s00449-014-1234-1 Google Scholar
  218. 218.
    Krämer CE, Singh A, Helfrich S, Grünberger A, Wiechert W, Nöh K, Kohlheyer D (2015) Non-invasive microbial metabolic activity sensing at single cell level by perfusion of calcein acetoxymethyl ester. PLoS One 10(10):e0141768.  https://doi.org/10.1371/journal.pone.0141768 Google Scholar
  219. 219.
    Grünberger A, Paczia N, Probst C, Schendzielorz G, Eggeling L, Noack S, Wiechert W, Kohlheyer D (2012) A disposable picolitre bioreactor for cultivation and investigation of industrially relevant bacteria on the single cell level. Lab Chip 12(11):2060–2068.  https://doi.org/10.1039/c2lc40156h Google Scholar
  220. 220.
    Chamsartra S, Hewitt CJ, Nienow AW (2005) The impact of fluid mechanical stress on Corynebacterium glutamicum during continuous cultivation in an agitated bioreactor. Biotechnol Lett 27(10):693–700.  https://doi.org/10.1007/s10529-005-4690-5 Google Scholar
  221. 221.
    Junne S, Klingner A, Kabisch J, Schweder T, Neubauer P (2011) A two-compartment bioreactor system made of commercial parts for bioprocess scale-down studies: impact of oscillations on Bacillus subtilis fed-batch cultivations. Biotechnol J 6(8):1009–1017.  https://doi.org/10.1002/biot.201100293 Google Scholar
  222. 222.
    Amanullah A, McFarlane C, Emery AN, Nienow A (2001) Scale-down model to simulate spatial pH variations large-scale bioreactors. Biotechnol Bioeng 73(5)Google Scholar
  223. 223.
    Tasaki S, Nakayama M, Shoji W (2017) Self-organization of bacterial communities against environmental pH variation: controlled chemotactic motility arranges cell population structures in biofilms. PLoS One 12(3):e0173195.  https://doi.org/10.1371/journal.pone.0173195 Google Scholar
  224. 224.
    Reis A, da Silva TL, Kent CA, Kosseva M, Roseiro JC, Hewitt CJ (2005) Monitoring population dynamics of the thermophilic Bacillus licheniformis CCMI 1034 in batch and continuous cultures using multi-parameter flow cytometry. J Biotechnol 115(2):199–210.  https://doi.org/10.1016/j.jbiotec.2004.08.005 Google Scholar
  225. 225.
    Jahn M, Günther S, Müller S (2015) Non-random distribution of macromolecules as driving forces for phenotypic variation. Curr Opin Microbiol 25:49–55.  https://doi.org/10.1016/j.mib.2015.04.005 Google Scholar
  226. 226.
    de Jong IG, Veening JW, Kuipers OP (2010) Heterochronic phosphorelay gene expression as a source of heterogeneity in Bacillus subtilis spore formation. J Bacteriol 192(8):2053–2067.  https://doi.org/10.1128/JB.01484-09 Google Scholar
  227. 227.
    Morohashi M, Ohashi Y, Tani S, Ishii K, Itaya M, Nanamiya H, Kawamura F, Tomita M, Soga T (2007) Model-based definition of population heterogeneity and its effects on metabolism in sporulating Bacillus subtilis. J Biochem 142(2):183–191.  https://doi.org/10.1093/jb/mvm121 Google Scholar
  228. 228.
    Veening JW, Igoshin OA, Eijlander RT, Nijland R, Hamoen LW, Kuipers OP (2008) Transient heterogeneity in extracellular protease production by Bacillus subtilis. Mol Syst Biol 4:184.  https://doi.org/10.1038/msb.2008.18 Google Scholar
  229. 229.
    Davidson FA, Seon-Yi C, Stanley-Wall NR (2012) Selective heterogeneity in exoprotease production by Bacillus subtilis. PLoS One 7(6):e38574.  https://doi.org/10.1371/journal.pone.0038574 Google Scholar
  230. 230.
    Kearns DB (2008) Division of labour during Bacillus subtilis biofilm formation. Mol Microbiol 67(2):229–231.  https://doi.org/10.1111/j.1365-2958.2007.06053.x Google Scholar
  231. 231.
    Nagler K, Setlow P, Li YQ, Moeller R (2014) High salinity alters the germination behavior of Bacillus subtilis spores with nutrient and nonnutrient germinants. Appl Environ Microbiol 80(4):1314–1321.  https://doi.org/10.1128/AEM.03293-13 Google Scholar
  232. 232.
    Young JW, Locke JC, Elowitz MB (2013) Rate of environmental change determines stress response specificity. PNAS 110(10):4140–4145Google Scholar
  233. 233.
    Kesel S, Mader A, Höfler C, Mascher T, Leisner M (2013) Immediate and heterogeneous response of the LiaFSR two-component system of Bacillus subtilis to the peptide antibiotic bacitracin. PLoS One 8(1):e53457.  https://doi.org/10.1371/journal.pone.0053457 Google Scholar
  234. 234.
    Schmidt JK, Riedele C, Regestein L, Rausenberger J, Reichl U (2011) A novel concept combining experimental and mathematical analysis for the identification of unknown interspecies effects in a mixed culture. Biotechnol Bioeng 108(8):1900–1911.  https://doi.org/10.1002/bit.23126 Google Scholar
  235. 235.
    Jones JA, Vernacchio VR, Sinkoe AL, Collins SM, Ibrahim MH, Lachance DM, Hahn J, Koffas MA (2016) Experimental and computational optimization of an Escherichia coli co-culture for the efficient production of flavonoids. Metab Eng 35:55–63.  https://doi.org/10.1016/j.ymben.2016.01.006 Google Scholar
  236. 236.
    Jagmann N, Philipp B (2014) Reprint of Design of synthetic microbial communities for biotechnological production processes. J Biotechnol 192:293–301.  https://doi.org/10.1016/j.jbiotec.2014.11.005 Google Scholar
  237. 237.
    Dietz D, Zeng AP (2014) Efficient production of 1,3-propanediol from fermentation of crude glycerol with mixed cultures in a simple medium. Bioprocess Biosyst Eng 37(2):225–233.  https://doi.org/10.1007/s00449-013-0989-0 Google Scholar
  238. 238.
    Bernstein HC, Paulson SD, Carlson RP (2012) Synthetic Escherichia coli consortia engineered for syntrophy demonstrate enhanced biomass productivity. J Biotechnol 157(1):159–166.  https://doi.org/10.1016/j.jbiotec.2011.10.001 Google Scholar
  239. 239.
    Cibis E, Ryznar-Luty A, Krzywonos M, Lutosławski K, Miśkiewicz T (2011) Betaine removal during thermo- and mesophilic aerobic batch biodegradation of beet molasses vinasse: influence of temperature and pH on the progress and efficiency of the process. J Environ Manag 92(7):1733–1739.  https://doi.org/10.1016/j.jenvman.2011.02.009 Google Scholar
  240. 240.
    Goers L, Freemont P, Polizzi KM (2014) Co-culture systems and technologies: taking synthetic biology to the next level. J R Soc Interface 11(96.  https://doi.org/10.1098/rsif.2014.0065
  241. 241.
    Minty JJ, Singer ME, Scholz SA, Bae C-H, Ahn J-H, Foster CE, Liao JC, Lin XN (2013) Design and characterization of synthetic fungal-bacterial consortia for direct production of isobutanol from cellulosic biomass. PNAS 110(36):14592–14597Google Scholar
  242. 242.
    Valdez-Vazquez I, Morales AL, Escalante AE (2017) History of adaptation determines short-term shifts in performance and community structure of hydrogen-producing microbial communities degrading wheat straw. Microb Biotechnol.  https://doi.org/10.1111/1751-7915.12678 Google Scholar
  243. 243.
    Herrero M, Quiros C, Garcia LA, Diaz M (2006) Use of flow cytometry to follow the physiological states of microorganisms in cider fermentation processes. Appl Environ Microbiol 72(10):6725–6733.  https://doi.org/10.1128/AEM.01183-06 Google Scholar
  244. 244.
    Rüger M, Ackermann M, Reichl U (2014) Species-specific viability analysis of Pseudomonas aeruginosa, Burkholderia cepacia and Staphylococcus aureus in mixed culture by flow cytometry. BMC Microbiol 14:(56)Google Scholar
  245. 245.
    Rüger M, Bensch G, Tüngler R, Reichl U (2012) A flow cytometric method for viability assessment of Staphylococcus aureus and Burkholderia cepacia in mixed culture. Cytometry A 81(12):1055–1066.  https://doi.org/10.1002/cyto.a.22219 Google Scholar
  246. 246.
    Nunes LV, de Barros Correa FF, de Oliva Neto P, Mayer CR, Escaramboni B, Campioni TS, de Barros NR, Herculano RD, Fernandez Nunez EG (2017) Lactic acid production from submerged fermentation of broken rice using undefined mixed culture. World J Microbiol Biotechnol 33(4):79.  https://doi.org/10.1007/s11274-017-2240-7 Google Scholar
  247. 247.
    Kumar RS, Moorthy IMG, Baskar R (2013) Modeling and optimization of glutamic acid production using mixed culture of Corynebacterium glutamicum NCIM2168 and Pseudomonas reptilivora NCIM2598. Prepar Biochem Biotechnol 43(7):668–681.  https://doi.org/10.1080/10826068.2013.772064 Google Scholar
  248. 248.
    Hanly TJ, Urello M, Henson MA (2012) Dynamic flux balance modeling of S. cerevisiae and E. coli co-cultures for efficient consumption of glucose/xylose mixtures. Appl Microbiol Biotechnol 93(6):2529–2541.  https://doi.org/10.1007/s00253-011-3628-1 Google Scholar
  249. 249.
    de Jong IG, Haccou P, Kuipers OP (2011) Bet hedging or not? A guide to proper classification of microbial survival strategies. Bioessays 33(3):215–223.  https://doi.org/10.1002/bies.201000127 Google Scholar
  250. 250.
    Grimbergen AJ, Siebring J, Solopova A, Kuipers OP (2015) Microbial bet-hedging: the power of being different. Curr Opin Microbiol 25:67–72.  https://doi.org/10.1016/j.mib.2015.04.008 Google Scholar
  251. 251.
    Ferenci T, Maharjan R (2015) Mutational heterogeneity: a key ingredient of bet-hedging and evolutionary divergence?: the broad spectrum of mutations and their flexible frequency in populations provides a source of risk avoidance and alternative evolutionary strategies. Bioessays 37(2):123–130.  https://doi.org/10.1002/bies.201400153 Google Scholar
  252. 252.
    Simons AM (2011) Modes of response to environmental change and the elusive empirical evidence for bet hedging. Proc Biol Sci 278(1712):1601–1609.  https://doi.org/10.1098/rspb.2011.0176 Google Scholar
  253. 253.
    Mitchell A, Pilpel Y (2011) A mathematical model for adaptive prediction of environmental changes by microorganisms. Proc Natl Acad Sci USA 108(17):7271–7276.  https://doi.org/10.1073/pnas.1019754108 Google Scholar
  254. 254.
    Charlebois DA, Balazsi G (2016) Frequency-dependent selection: a diversifying force in microbial populations. Mol Syst Biol 12(8):880.  https://doi.org/10.15252/msb.20167133 Google Scholar
  255. 255.
    New AM, Cerulus B, Govers SK, Perez-Samper G, Zhu B, Boogmans S, Xavier JB, Verstrepen KJ (2014) Different levels of catabolite repression optimize growth in stable and variable environments. PLoS Biol 12(1):e1001764.  https://doi.org/10.1371/journal.pbio.1001764 Google Scholar
  256. 256.
    Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, Kuipers OP (2014) Bet-hedging during bacterial diauxic shift. Proc Natl Acad Sci USA 111(20):7427–7432.  https://doi.org/10.1073/pnas.1320063111 Google Scholar
  257. 257.
    Silander OK, Nikolic N, Zaslaver A, Bren A, Kikoin I, Alon U, Ackermann M (2012) A genome-wide analysis of promoter-mediated phenotypic noise in Escherichia coli. PLoS Genet 8(1):e1002443.  https://doi.org/10.1371/journal.pgen.1002443 Google Scholar
  258. 258.
    Keren L, van Dijk D, Weingarten-Gabbay S, Davidi D, Jona G, Weinberger A, Milo R, Segal E (2015) Noise in gene expression is coupled to growth rate. Genome Res 25(12):1893–1902.  https://doi.org/10.1101/gr.191635.115 Google Scholar
  259. 259.
    Baert J, Kinet R, Brognaux A, Delepierre A, Telek S, Sorensen SJ, Riber L, Fickers P, Delvigne F (2015) Phenotypic variability in bioprocessing conditions can be tracked on the basis of on-line flow cytometry and fits to a scaling law. Biotechnol J 10(8):1316–1325.  https://doi.org/10.1002/biot.201400537 Google Scholar
  260. 260.
    Dacheux E, Firczuk H, McCarthy John EG (2015) Rate control in yeast protein synthesis at the population and single-cell levels. Biochem Soc Trans 43(6):1266–1270.  https://doi.org/10.1042/bst20150169 Google Scholar
  261. 261.
    Ozbudak EM, Thattai M, Kurtser I, Grossman AD, van Oudenaarden A (2002) Regulation of noise in the expression of a single gene. Nat Genet 31(1):69–73.  https://doi.org/10.1038/ng869 Google Scholar
  262. 262.
    Elowitz MB, Levine AJ, Siggia ED, Swain PS (2002) Stochastic gene expression in a single cell. Science 297:1183–1186Google Scholar
  263. 263.
    Cerulus B, New AM, Pougach K, Verstrepen KJ (2016) Noise and epigenetic inheritance of single-cell division times influence population fitness. Curr Biol 26(9):1138–1147.  https://doi.org/10.1016/j.cub.2016.03.010 Google Scholar
  264. 264.
    Bandiera L, Furini S, Giordano E (2016) Phenotypic variability in synthetic biology applications: dealing with noise in microbial gene expression. Front Microbiol 7:479.  https://doi.org/10.3389/fmicb.2016.00479 Google Scholar
  265. 265.
    Selvarajoo K (2012) Understanding multimodal biological decisions from single cell and population dynamics. Wiley Interdiscip Rev Syst Biol Med 4(4):385–399.  https://doi.org/10.1002/wsbm.1175 Google Scholar
  266. 266.
    Liu J, Francois JM, Capp JP (2016) Use of noise in gene expression as an experimental parameter to test phenotypic effects. Yeast 33(6):209–216.  https://doi.org/10.1002/yea.3152 Google Scholar
  267. 267.
    Mugler A, Kittisopikul M, Hayden L, Liu J, Wiggins CH, Süel GM, Walczak AM (2016) Noise expands the response range of the Bacillus subtilis competence circuit. PLoS Comput Biol 12(3):e1004793.  https://doi.org/10.1371/journal.pcbi.1004793 Google Scholar
  268. 268.
    Stratford M, Steels H, Nebe-von-Caron G, Avery SV, Novodvorska M, Archer DB (2014) Population heterogeneity and dynamics in starter culture and lag phase adaptation of the spoilage yeast Zygosaccharomyces bailii to weak acid preservatives. Int J Food Microbiol 181:40–47.  https://doi.org/10.1016/j.ijfoodmicro.2014.04.017 Google Scholar
  269. 269.
    Guyot S, Gervais P, Young M, Winckler P, Dumont J, Davey HM (2015) Surviving the heat: heterogeneity of response in Saccharomyces cerevisiae provides insight into thermal damage to the membrane. Environ Microbiol 17(8):2982–2992.  https://doi.org/10.1111/1462-2920.12866 Google Scholar
  270. 270.
    Amato SM, Brynildsen MP (2015) Persister heterogeneity arising from a single metabolic stress. Curr Biol 25(16):2090–2098.  https://doi.org/10.1016/j.cub.2015.06.034 Google Scholar
  271. 271.
    Allison KR, Brynildsen MP, Collins JJ (2011) Heterogeneous bacterial persisters and engineering approaches to eliminate them. Curr Opin Microbiol 14(5):593–598.  https://doi.org/10.1016/j.mib.2011.09.002 Google Scholar
  272. 272.
    Amato SM, Fazen CH, Henry TC, Mok WW, Orman MA, Sandvik EL, Volzing KG, Brynildsen MP (2014) The role of metabolism in bacterial persistence. Front Microbiol 5:70.  https://doi.org/10.3389/fmicb.2014.00070 Google Scholar
  273. 273.
    Dubnau D, Losick R (2006) Bistability in bacteria. Mol Microbiol 61(3):564–572.  https://doi.org/10.1111/j.1365-2958.2006.05249.x Google Scholar
  274. 274.
    Poisson P, Bhalerao KD (2013) Hidden hysteresis—population dynamics can obscure gene network dynamics. J Biol Eng 7:(16)Google Scholar
  275. 275.
    Healey D, Axelrod K, Gore J (2016) Negative frequency-dependent interactions can underlie phenotypic heterogeneity in a clonal microbial population. Mol Syst Biol 12(8):877.  https://doi.org/10.15252/msb.20167033 Google Scholar
  276. 276.
    Fritz G, Megerle JA, Westermayer SA, Brick D, Heermann R, Jung K, Rädler JO, Gerland U (2014) Single cell kinetics of phenotypic switching in the arabinose utilization system of E. coli. PLoS One 9(2):e89532.  https://doi.org/10.1371/journal.pone.0089532 Google Scholar
  277. 277.
    Wang X, Kang Y, Luo C, Zhao T, Liu L, Jiang X, Fu R, An S, Chen J, Jiang N, Ren L, Wang Q, Baillie JK, Gao Z, Yu J (2014) Heteroresistance at the single-cell level: adapting to antibiotic stress through a population-based strategy and growth-controlled interphenotypic coordination. MBio 5(1):e00942-00913.  https://doi.org/10.1128/mBio.00942-13 Google Scholar
  278. 278.
    Van Nevel S, Koetzsch S, Weilenmann HU, Boon N, Hammes F (2013) Routine bacterial analysis with automated flow cytometry. J Microbiol Methods 94(2):73–76.  https://doi.org/10.1016/j.mimet.2013.05.007 Google Scholar
  279. 279.
    Hammes F, Broger T, Weilenmann HU, Vital M, Helbing J, Bosshart U, Huber P, Odermatt RP, Sonnleitner B (2012) Development and laboratory-scale testing of a fully automated online flow cytometer for drinking water analysis. Cytometry A 81(6):508–516.  https://doi.org/10.1002/cyto.a.22048 Google Scholar
  280. 280.
    Besmer MD, Epting J, Page RM, Sigrist JA, Huggenberger P, Hammes F (2016) Online flow cytometry reveals microbial dynamics influenced by concurrent natural and operational events in groundwater used for drinking water treatment. Sci Rep 6:38462.  https://doi.org/10.1038/srep38462 Google Scholar
  281. 281.
    Mears L, Stocks SM, Albaek MO, Cassells B, Sin G, Gernaey KV (2017) A novel model-based control strategy for aerobic filamentous fungal fed-batch fermentation processes. Biotechnol Bioeng.  https://doi.org/10.1002/bit.26274 Google Scholar
  282. 282.
    Rogers WT, Holyst HA (2009) FlowFP: a bioconductor package for fingerprinting flow cytometric data. Adv Bioinf.  https://doi.org/10.1155/2009/193947 Google Scholar
  283. 283.
    Koch C, Fetzer I, Harms H, Muller S (2013) CHIC-an automated approach for the detection of dynamic variations in complex microbial communities. Cytometry A 83(6):561–567.  https://doi.org/10.1002/cyto.a.22286 Google Scholar
  284. 284.
    Koch C, Fetzer I, Schmidt T, Harms H, Müller S (2013) Monitoring functions in managed microbial systems by cytometric bar coding. Environ Sci Technol 47(3):1753–1760.  https://doi.org/10.1021/es3041048 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Biochemical EngineeringTechnical University of MunichGarchingGermany

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