Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses

Biotechnology Journal

Volumn 12, Issue 7, 2017, Pages

  Delvigne, Frank ^a   Baert, Jonathan ^a   Sassi, Hosni ^a   Fickers, Patrick ^a   Grünberger, Alexander ^b,c   Dusny, Christian ^d  

^a UNIVERSITY OF LIÈGE   (Belgium)

^b FORSCHUNGSZENTRUM JÜLICH   (Germany)

^c BIELEFELD UNIVERSITY   (Germany)

^d HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

Author keywords

Flow cytometry; Microbial stress; Microfluidics; Phenotypic heterogeneity; Single cell

Indexed keywords

CYTOLOGY; ENVIRONMENTAL TECHNOLOGY; FLOW CYTOMETRY; MICROFLUIDICS; PHYSIOLOGY; SIGNAL PROCESSING;

EXPERIMENTAL IDENTIFICATION; MATHEMATICAL EXPRESSIONS; MATHEMATICAL REPRESENTATIONS; MICROBIAL PHYSIOLOGY; MICROBIAL POPULATIONS; PHENOTYPIC HETEROGENEITY; PHENOTYPIC PLASTICITY; SINGLE CELLS;

CELLS;

MESSENGER RNA;

BIOPROCESS; COMPUTATIONAL FLUID DYNAMICS; ENVIRONMENTAL FACTOR; GENE REGULATORY NETWORK; GENETIC ENGINEERING; GROWTH RATE; MICROBIAL POPULATION DYNAMICS; MICROFLUIDICS; NOISE; NONHUMAN; OPERATOR GENE; PH; PHENOTYPE; PHENOTYPIC PLASTICITY; PREDICTION; PRIORITY JOURNAL; PROMOTER REGION; PROTEIN SECONDARY STRUCTURE; REPRESSOR GENE; REVIEW; SIMPLE SEQUENCE REPEAT; STOCHASTIC MODEL; TRANSLATION INITIATION; BACTERIUM; BIOREACTOR; FLOW CYTOMETRY; GROWTH, DEVELOPMENT AND AGING; MICROBIOLOGY; MICROFLUIDIC ANALYSIS; PROCEDURES; SINGLE CELL ANALYSIS; THEORETICAL MODEL;

BACTERIA; BIOREACTORS; FLOW CYTOMETRY; MICROFLUIDIC ANALYTICAL TECHNIQUES; MODELS, THEORETICAL; PHENOTYPE; SINGLE-CELL ANALYSIS;

EID: 85019932273     PISSN: 18606768     EISSN: 18607314     Source Type: Journal    
DOI: 10.1002/biot.201600549     Document Type: Review

References (136)

1. Holtz, W. J., Keasling, J. D., Engineering static and dynamic control of synthetic pathways. Cell 2010, 140, 19–23.
2. Brockman, I. M., Prather K. L. J., Dynamic metabolic engineering: New strategies for developing responsive cell factories. Biotechnol. J. 2015, 10, 1360–9.
3. McNerney, M. P., Watstein, D. M., Styczynski, M. P., Precision metabolic engineering: The design of responsive, selective, and controllable metabolic systems. Metab. Eng. 2015, 31, 123–131.
4. Kobayashi, H., Kaern, M., Araki, M., Chung, K. et al., Programmable cells: interfacing natural and engineered gene networks. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 8414–8419.
5. Silva-Rocha, R., de Lorenzo, V., Noise and robustness in prokaryotic regulatory networks. Annu. Rev. Microbiol. 2010, 64, 257–275.
6. 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.
7. Carey, L. B., van Dijk, D., Sloot, P. M., Kaandorp, J. A., Segal E., Promoter sequence determines the relationship between expression level and noise. PLoS Biol. 2013, 11, e1001528.
8. Ferguson, M. L., Le Coq, D., Jules, M., Aymerich, S. et al., Reconciling molecular regulatory mechanisms with noise patterns of bacterial metabolic promoters in induced and repressed states. Proc. Natl. Acad. Sci. USA. 2012, 109, 155–160.
9. Lillacci, G., Khammash, M., The signal within the noise: efficient inference of stochastic gene regulation models using fluorescence histograms and stochastic simulations. Bioinformatics 2013, 29, 2311–2319.
10. Mustafi, N., Grunberger, A., Mahr, R., Helfrich, S. et al., Application of a genetically encoded biosensor for live cell imaging of L-valine production in pyruvate dehydrogenase complex-deficient Corynebacterium glutamicum strains. PLoS One 2014, 9, e85731.
11. Lindmeyer, M., Meyer, D., Kuhn, D., Buhler, B., Schmid, A., Making variability less variable: matching expression system and host for oxygenase-based biotransformations. J. Ind. Microbiol. Biotechnol. 2015, 42, 851–866.
12. 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.
13. Muller, S., Tarnok, A., Microbes' heterogeneousness - a focus issue on cytometric technologies in microbial single cell analytics. Biotechnol. J. 2009, 4, 591–592.
14. Ackermann, M., Schreiber, F., A growing focus on bacterial individuality. Environ. Microbiol. 2015, 17, 2193–2195.
15. Ackermann, M., A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 2015, 13, 497–508.
16. Balazsi, G., van Oudenaarden, A., Collins, J. J,. Cellular decision making and biological noise: from microbes to mammals. Cell 2011, 144, 910–925.
17. van Vliet, S., Ackermann, M., Bacterial ventures into multicellularity: Collectivism through individuality. PLoS Biol. 2015, 13, e1002162.
18. Martins, B. M., Locke, J. C., Microbial individuality: how single-cell heterogeneity enables population level strategies. Curr. Opin. Microbiol. 2015, 24, 104–112.
19. Lara, A. R., Living with heterogeneities in bioreactors - Understanding the effects of environmental gradients on cells. Mol. Biotechnol. 2006, 34, 355–381.
20. 3 aerobic S. cerevisiae fermentation. Chem. Eng. Sci. 2017, doi.org/.10.1016/j.ces.2017.01.014
21. 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.
22. Brognaux, A., Francis, F., Twizere, J. C., Thonart, P., Delvigne, F., Scale-down effect on the extracellular proteome of Escherichia coli: correlation with membrane permeability and modulation according to substrate heterogeneities. Bioprocess Biosyst Eng. 2014, 14, 14.
23. Lara, A. R., Transcriptional and metabolic response of recombinant Escherichia coli to spatial dissolved oxygen tension gradients simulated in a scale-down system. Biotechnol. Bioeng. 2005, 93, 372–385.
24. Loffler, M., Simen, J. D., Jager, G., Schaferhoff, K. et al., Engineering E. coli for large-scale production - Strategies considering ATP expenses and transcriptional responses. Metab. Eng. 2016, 38, 73–85.
25. 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.
26. Delvigne, F., Zune, Q., Lara, A. R., Al-Soud, W., Sorensen, S. J., Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity. Trends Biotechnol. 2014, 32, 608–616.
27. Lidstrom, M. E., Konopka, M. C., The role of physiological heterogeneity in microbial population behavior. Nat. Chem. Biol. 2010, 6, 705–712.
28. Hilfinger, A., Paulsson, J., Separating intrinsic from extrinsic fluctuations in dynamic biological systems. Proc. Natl. Acad. Sci. USA. 2011, 108, 12167–12172.
29. Alon, U., Network motifs: theory and experimental approaches. Nature 2007, 8, 450–461.
30. Hornung, G., Bar-Ziv, R., Rosin, D., Tokuriki, N. et al., Noise-mean relationship in mutated promoters. Genome Res. 2012, 22, 2409–2417.
31. Sanchez, A., Golding, I., Genetic determinants and cellular constraints in noisy gene expression. Science 2013, 342, 1188–1193.
32. Sanchez, A., Choubey, S., Kondev, J., Regulation of noise in gene expression. Annu. Rev. Biophys. 2013, 42, 469–491.
33. Shahrezaei, V., Swain, P. S., The stochastic nature of biochemical networks. Curr. Opin. Biotechnol. 2008, 19, 369–374.
34. Dadiani, M., van Dijk, D., Segal, B., Field, Y. et al., Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise. Genome Res. 2013, 23, 966–976.
35. Blake, W. J., Kaern, M., Cantor, C. R., Collins, J. J., Noise in eukaryotic gene expression. Nature 2003, 422, 633–637.
36. Bai, L., Charvin, G., Siggia, E. D., Cross, F. R., Nucleosome-depleted regions in cell-cycle-regulated promoters ensure reliable gene expression in every cell cycle. Dev. Cell 2010, 18, 544–555.
37. Segal, E., Widom, J., Poly(dA:dT) tracts: major determinants of nucleosome organization. Curr. Opin. Struct. Biol. 2009, 19, 65–71.
38. Pilpel, Y., Noise in biological systems: pros, cons, and mechanisms of control. Methods Mol. Biol., 2011, 759, 407–425.
39. Tian, T., Salis, H. M., A predictive biophysical model of translational coupling to coordinate and control protein expression in bacterial operons. Nucleic Acids Res. 2015, 43, 7137–7151.
40. Salis, H. M., The ribosome binding site calculator. Methods Enzymol. 2011, 498, 19–42.
41. Egbert, R. G., Klavins, E., Fine-tuning gene networks using simple sequence repeats. Proc. Natl. Acad. Sci. USA. 2012, 109, 16817–16822.
42. Zur, H., Tuller, T., Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution. Nucleic Acids Res. 2016, 44, 9031–9049.
43. Diament, A., Tuller, T., Estimation of ribosome profiling performance and reproducibility at various levels of resolution. Biol. Direct. 2016, 11, 24.
44. Bremer, H., Dennis, P., Feedback control of ribosome function in Escherichia coli. Biochimie 2008, 90, 493–499.
45. Delvigne, F., Goffin, P., Microbial heterogeneity affects bioprocess robustness: Dynamic single cell analysis contribute to understanding microbial populations. Biotechnol. J. 2014, 9, 61–72.
46. Delvigne, F., Brognaux, A., Gorret, N., Neubauer, P. et al., Characterization of the response of GFP microbial biosensors sensitive to substrate limitation in scale-down bioreactors. Biochem. Eng. J. 2011, 55, 131–139.
47. Jang, S., Yang, J., Seo, S. W., Jung, G. Y., Riboselector: riboswitch-based synthetic selection device to expedite evolution of metabolite-producing microorganisms. Methods Enzymol. 2015, 550, 341–362.
48. Mahr, R., Frunzke, J., Transcription factor-based biosensors in biotechnology: current state and future prospects. Appl. Microbiol. Biotechnol. 2016, 100, 79–90.
49. Pedelacq, J.-D., Cabantous, S., Tran, T., Terwilliger, T. C., Waldo, G. S., Engineering and characterization of a superfolder green fluorescent protein. Nat. Biotechnol. 2006, 24, 79–88.
50. McGinness, K. E., Baker, T. A., Sauer, R. T., Engineering controllable protein degradation. Mol. Cell 2006, 22, 701–707.
51. Hsiao, V., Hori, Y., Rothemund, P. W., Murray, R. M., A population-based temporal logic gate for timing and recording chemical events. Mol. Syst. Biol. 2016, 12, 869.
52. Munsky, B., Trinh, B., Khammash, M., Listening to the noise: random fluctuations reveal gene network parameters. Mol. Syst. Biol. 2009, 5, 318.
53. Dusny, C., Schmid, A., Microfluidic single-cell analysis links boundary environments and individual microbial phenotypes. Environ. Microbiol. 2015, 17, 1839–1856.
54. Grunberger, A., Wiechert, W., Kohlheyer, D., Single-cell microfluidics: opportunity for bioprocess development. Curr. Opin. Biotechnol. 2014, 29, 15–23.
55. Eggeling, L., Bott, M., Marienhagen, J., Novel screening methods-biosensors. Curr. Opin. Biotechnol. 2015, 35, 30–36.
56. Liu, D., Evans, T., Zhang, F., Applications and advances of metabolite biosensors for metabolic engineering. Metab. Eng. 2015, 31, 35–43.
57. Polizzi, K. M., Kontoravdi, C., Genetically-encoded biosensors for monitoring cellular stress in bioprocessing. Curr. Opin. Biotechnol. 2014, 31, 50–56.
58. Strauber, H., Viability states of bacteria–specific mechanisms of selected probes. Cytometry A 2009, 77, 623–634.
59. Baert, J., Delepierre, A., Telek, S., Fickers, P. et al., Microbial population heterogeneity versus bioreactor heterogeneity: Evaluation of Redox Sensor Green as an exogenous metabolic biosensor. Eng. Life Sci. 2016, 16, 643–651.
60. Delvigne, F., Brognaux, A., Francis, F., Twizere, J. C. et al., Green fluorescent protein (GFP) leakage from microbial biosensors provides useful information for the evaluation of the scale-down effect. Biotechnol. J. 2011, 6, 968–978.
61. Beebe, D. J., Mensing, G. A., Walker, G. M., Physics and applications of microfluidics in biology. Annu. Rev. Biomed. Eng. 2002, 4, 261–286.
62. Damodaran, S. P., Eberhard, S., Boitard, L., Rodriguez, J. G. et al., A millifluidic study of cell-to-cell heterogeneity in growth-rate and cell-division capability in populations of isogenic cells of Chlamydomonas reinhardtii. PLoS One 2015, 10, e0118987.
63. Fritzsch, F. S. O., Dusny, C., Frick, O., Schmid, A., Single-cell analysis in biotechnology, systems biology, and biocatalysis. Annu. Rev. Chem. Biomol. Eng. 2012, 3, 129–155.
64. 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, 12 100.
65. Broger, T., Odermatt, R. P., Huber, P., Sonnleitner, B., Real-time on-line flow cytometry for bioprocess monitoring. J. Biotechnol. 2011, 154, 240–247.
66. Arnoldini, M., Heck, T., Blanco-Fernandez, A., Hammes, F., Monitoring of dynamic microbiological processes using real-time flow cytometry. PLoS One 2013, 8, e80117.
67. 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.
68. Ladner, T., Grunberger, A., Probst, C., Kohlheyer, D. et al., Application of mini- and micro-bioreactors for microbial bioprocesses, in Larroche, C., Sanroman Angeles, M., Du, G. Pandey, A. (Ed.). Current Developments i. Biotechnology and Bioengineering. Elsevier B.V. 2016, pp. 433–461.
69. Helfrich, S., et al., Vizardous: Interactive visualization and analysis of microbial populations with single cell resolution. Bioinformatics 2015, 31, 3875–3877.
70. Bray, M.-A., Carpenter, A. E., CellProfiler Tracer, exploring and validating high-throughput, time-lapse microscopy image data. BMC Bioinformatics 2015, 16, 368.
71. Goni-Moreno, A., Kim, J., de Lorenzo, V., CellShape: A user-friendly image analysis tool for quantitative visualization of bacterial cell factories inside. Biotechnol. J. 2016, 12, 1600323.
72. Lambert, G., Kussell, E., Memory and fitness optimization of bacteria under fluctuating environments. PLoS Genet. 2014, 10, e1004556.
73. Probst, C., Grunberger, A., Wiechert, W., Kohlheyer, D., Microfluidic growth chambers with optical tweezers for full spatial single-cell control and analysis of evolving microbes. J. Microbiol. Methods 2013, 95, 470–476.
74. Reyes, L. H., Visualizing evolution in real time to determine the molecular mechanisms of n-butanol tolerance in Escherichia coli. Metab. Eng. 2012, 14, 579–590.
75. Gilbert, A., Rapid strain improvement through optimized evolution in the cytostat. Biotechnol. Bioeng. 2009, 103, 500–512.
76. Toprak, E., Veres, A., Yildiz, S., Pedraza, J. M. et al., Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition. Nat. Protoc. 2013, 8, 555–567.
77. Almario, M. P., Evolutionary engineering of Saccharomyces cerevisiae for enhanced tolerance to hydrolysates of lignocellulosic biomass. Biotechnol. Bioeng. 2013, 110, 2616–2623.
78. Sitton, G., Mammalian cell culture scale-up and fed-batch control using automated flow cytometry. J. Biotechnol. 2008, 135, 174–180.
79. Hammes, F., Broger, T., Weilenmann, H.-U., Vital, M. et al., Development and laboratory-scale testing of a fully automated online flow cytometer for drinking water analysis. Cytometry A 2012, 81, 508–516.
80. Fiore, G., Perrino, G., diBernardo, M., diBernardo, D., In vivo real-time control of gene expression: A comparative analysis of feedback control strategies in yeast. ACS Synth. Biol. 2016, 5, 154–162.
81. Dusny, C., Fritzsch, F. S. O., Frick, O., Schmid, A., Isolated microbial single cells and resulting micropopulations grow faster in controlled environments. Appl. Environ. Microbiol. 2012, 78, 7132–7136.
82. Milias-Argeitis, A., Rullan, M., Aoki, S. K., Buchmann, P., Khammash, M., Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nat. Commun. 2016, 7, 12546.
83. Zhang, K., Cui, B., Optogenetic control of intracellular signaling pathways. Trends Biotechnol. 2015, 33, 92–100.
84. Milias-Argeitis, A., Summers, S., Stewart-Ornstein, J., Zuleta, I. et al., In silico feedback for in vivo regulation of a gene expression circuit. Nat. Biotechnol. 2011, 29, 1114–1116.
85. Binder, D., Bier, C., Grunberger, A., Drobietz, D., et al., Photocaged arabinose: A novel optogenetic switch for rapid and gradual control of microbial gene expression. Chembiochem 2016, 17, 296–299.
86. Binder, D., Frohwitter, J., Mahr, R., Bier, C. et al., 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. 2016, 82, 6141–6149.
87. Melendez, J., Patel, M., Oakes, B. L., Xu, P. et al., Real-time optogenetic control of intracellular protein concentration in microbial cell cultures. Integr. Biol. 2014, 6, 366–372.
88. Menolascina, F., Fiore, G., Orabona, E., De Stefano, L. et al., In-vivo real-time control of protein expression from endogenous and synthetic gene networks. PLoS Comput. Biol. 2014, 10, e1003625.
89. Anesiadis, N., Kobayashi, H., Cluett, W. R., Mahadevan, R., Analysis and design of a genetic circuit for dynamic metabolic engineering. ACS Synth. Biol. 2013, 2, 442–452.
90. Nelson, E. M., Kurz, V., Perry, N., Kyrouac, D., Timp, G., Biological noise abatement: coordinating the responses of autonomous bacteria in a synthetic biofilm to a fluctuating environment using a stochastic bistable switch. ACS Synth. Biol. 2014, 3, 286–297.
91. Rosche, B., Li, X. Z., Hauer, B., Schmid, A., Buehler, K., Microbial biofilms: a concept for industrial catalysis. Trends Biotechnol. 2009, 27, 636–643.
92. Lindmeyer, M., Jahn, M., Vorpahl, C., Muller, S. et al., Variability in subpopulation formation propagates into biocatalytic variability of engineered Pseudomonas putida strains. Front. Microbiol. 2015, 6, 1042.
93. Jahn, M., Vorpahl, C., Turkowsky, D., Lindmeyer, M. et al., Accurate determination of plasmid copy number of flow-sorted cells using droplet digital PCR. Anal. Chem. 2014, 86, 5969–5976.
94. Gross, R., Lang, K., Bühler, K., Schmid, A., Characterization of a biofilm membrane reactor and its prospects for fine chemical synthesis. Biotechnol. Bioeng. 2009, 105, 705–717.
95. Rao, C. V., Wolf, D. M., Arkin, A., Control, exploitation and tolerance of intracellular noise. Nature 2002, 420, 231–237.
96. Zimmermann, M., Escrig, S., Hubschmann, T., Kirf, M. K. et al., Phenotypic heterogeneity in metabolic traits among single cells of a rare bacterial species in its natural environment quantified with a combination of flow cell sorting and NanoSIMS. Front. Microbiol. 2015, 6, 243.
97. Junker, J. P., vanOudenaarden, A., Every cell is special: Genome-wide studies add a new dimension to single-cell biology. Cell 2014, 157, 8–11.
98. Dunlop, E. H., Ye, S. J., Micromixing in fermentors: metabolic changes in Saccharomyces cerevisiae and their relationship to fluid turbulence. Biotechnol. Bioeng. 1990, 36, 854–864.
99. Dusny, C., Schmid, A., The MOX promoter in Hansenula polymorpha is ultrasensitive to glucose-mediated carbon catabolite repression. FEMS Yeast Res. 2016, 16, fow067.
100. Gough, A. H., Chen, N., Shun, T. Y., Lezon, T. R. et al., Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery. PLoS One 2014, 9, e102678.
101. Besmer, M. D., Weissbrodt, D. G., Kratochvil, B. E., Sigrist, J. A. et al., The feasibility of automated online flow cytometry for in-situ monitoring of microbial dynamics in aquatic ecosystems. Front. Microbiol. 2014, 5 265.
102. Kacmar, J., Zamamiri, A., Carlson, R., Abu-Absi, N. R., Srienc, F., Single-cell variability in growing Saccharomyces cerevisiae cell populations measured with automated flow cytometry. J. Biotechnol. 2004, 109, 239–254.
103. Koch, C., Harnisch, F., Schroder, U., Muller, S., Cytometric fingerprints: evaluation of new tools for analyzing microbial community dynamics. Front. Microbiol. 2014, 5, 273.
104. LeMeur, N., Computational methods for evaluation of cell-based data assessment--Bioconductor. Curr. Opin. Biotechnol. 2013, 24, 105–111.
105. Koch, C., Fetzer, I., Harms, H., Muller, S. CHIC-an automated approach for the detection of dynamic variations in complex microbial communities. Cytometry A 2013, 83, 561–567.
106. Kinet, R., Dzaomuho, P., Baert, J., Taminiau, B. et al., Flow cytometry community fingerprinting and amplicon sequencing for the assessment of landfill leachate cellulolytic bioaugmentation. Bioresour. Technol. 2016, 214, 450–459.
107. De Roy, K., Clement, L., Thas, O., Wang, Y., Boon, N., Flow cytometry for fast microbial community fingerprinting. Water Res. 2012, 46, 907–919.
108. 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.
109. 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.
110. Baert, J., Kinet, R., Brognaux, A., Delepierre, A. et al., 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.
111. Grunberger, A., Probst, C., Helfrich, S., Nanda, A. et al., Spatiotemporal microbial single-cell analysis using a high-throughput microfluidics cultivation platform. Cytometry A 2015, 87, 1101–1115.
112. Westerwalbesloh, C., Grunberger, A., Stute, B., Weber, S. et al., Modeling and CFD simulation of nutrient distribution in picoliter bioreactors for bacterial growth studies on single-cell level. Lab Chip 2015, 15, 4177–4186.
113. Dusny, C., Fritzsch, F. S. O., Frick, O., Schmid, A., Isolated microbial single cells and resulting micropopulations grow faster in controlled environments. Appl. Environ. Microbiol. 2012, 78, 7132–7136.
114. Ruess, J., Parise, F., Milias-Argeitis, A., Khammash, M., Lygeros, J., Iterative experiment design guides the characterization of a light-inducible gene expression circuit. Proc. Natl. Acad. Sci. USA. 2015, 112, 8148–8153.
115. Llamosi, A., Gonzalez-Vargas, A. M., Versari, C., Cinquemani, E. et al., What population reveals about individual cell identity: Single-cell parameter estimation of models of gene expression in yeast. PLoS Comput. Biol. 2016, 12, e1004706.
116. Gillespie, D. T., Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 1977, 81, 2340–2361.
117. Gillespie, D. T., Approximate accelerated stochastic simulation of chemically reacting systems. J. Chem. Phys. 2001, 115, 1716–1733.
118. Munsky, B., Khammash, M., The finite state projection algorithm for the solution of the chemical master equation. J. Chem. Phys. 2006, 124, 44104.
119. Munsky, B., Neuert, G., van Oudenaarden, A., Using gene expression noise to understand gene regulation. Science 2012, 336, 183–187.
120. Kotte, O., Volkmer, B., Radzikowski, J. L., Heinemann, M., Phenotypic bistability in Escherichia coli's central carbon metabolism. Mol. Syst. Biol. 2014, 10 736.
121. 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.
122. 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.
123. Grote, J., Krysciak, D., Streit, W. R., Phenotypic heterogeneity, a phenomenon that may explain why quorum sensing does not always result in truly homogenous cell behavior. Appl. Environ. Microbiol. 2015, 81, 5280–5289.
124. Gefen, O., Gabay, C., Mumcuoglu, M., Engel, G., Balaban, N. Q., Single-cell protein induction dynamics reveals a period of vulnerability to antibiotics in persister bacteria. Proc. Natl. Acad. Sci. USA. 2008, 105, 6145–6149.
125. Beal, J., Haddock-Angelli, T., Gershater, M., de Mora, K. et al., Reproducibility of fluorescent expression from engineered biological constructs in E. coli. PLoS One 2016, 11, e0150182.
126. Castillo-Hair, S. M., Sexton, J. T., Landry, B. P., Olson, E. J,, FlowCal: A user-friendly, open source software tool for automatically converting flow cytometry data from arbitrary to calibrated units. ACS Synth. Biol. 2016, 5, 774–780
127. Coffman, V. C., Wu, J.-Q., Every laboratory with a fluorescence microscope should consider counting molecules. Mol. Biol. Cell 2014, 25, 1545–1548.
128. Milo, R., Phillips, R., Cell Biology By The Numbers. Garland Science 2016.
129. Milo, R., What is the total number of protein molecules per cell volume? A call to rethink some published values. Bioessays 2013, 35, 1050–1055.
130. Liebermeister, W., Noor, E., Flamholz, A., Davidi, D. et al., Visual account of protein investment in cellular functions. Proc. Natl. Acad. Sci. USA. 2014, 111, 8488–8493.
131. Winkel, M., Salman-Carvalho, V., Woyke, T., Richter, M. et al., Single-cell sequencing of Thiomargarita reveals genomic flexibility for adaptation to dynamic redox conditions. Front. Microbiol. 2016, 7, 964.
132. Nakamura, K., Iizuka, R., Nishi, S., Yoshida, T. et al., Culture-independent method for identification of microbial enzyme-encoding genes by activity-based single-cell sequencing using a water-in-oil microdroplet platform. Sci. Rep. 2016, 6, 22259.
133. Hosokawa, M., Hoshino, Y., Nishikawa, Y., Hirose, T. et al., Droplet-based microfluidics for high-throughput screening of a metagenomic library for isolation of microbial enzymes. Biosens. Bioelectron. 2015, 67, 379–385.
134. Li, M., Xu, J., Romero-Gonzalez, M., Banwart, S. A., Huang, W. E., Single cell Raman spectroscopy for cell sorting and imaging. Curr. Opin. Biotechnol. 2012, 23, 56–63.
135. Andrews, J. S., Rolfe, S. A., Huang, W. E., Scholes, J. D., Banwart, S. A., Biofilm formation in environmental bacteria is influenced by different macromolecules depending on genus and species. Environ. Microbiol. 2010, 12, 2496–2507.
136. Pett-Ridge, J., Weber, P. K., NanoSIP: NanoSIMS applications for microbial biology. Methods Mol. Biol. 2012, 881, 375–408.