Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Microfluidic devices for measuring gene network dynamics in single cells

Nature Reviews Genetics volume 10, pages 628–638 (2009)Cite this article

Key Points

  • This Review examines the ways in which microfluidic devices have helped to reveal the dynamics of gene regulation and intracellular signalling.

  • Gene regulatory networks often operate through highly dynamic processes that cannot be studied by stationary measurements.

  • Microfluidic devices can trap cells for long periods of time, which allows time-lapse imaging of single cells. When these devices are combined with fluorescent reporters, the time-dependent activity of a network can be measured.

  • New designs for microfludic devices allow the growth environments of cellular populations to be perturbed in non-trivial ways, such as through the creation of spatial gradients or temporal waves of chemical concentrations.

  • Mathematical models that have been created from data obtained through time-lapse fluorescence microscopy have revealed novel functions of gene networks and new regulatory pathways.

  • Multicellular and multispecies studies have also been conducted using microfluidic devices that have been designed for research in intercellular signalling.

  • It is hoped that these new technologies will eventually help to identify techniques that can more accurately model genetic regulatory networks.

Abstract

The dynamics governing gene regulation have an important role in determining the phenotype of a cell or organism. From processing extracellular signals to generating internal rhythms, gene networks are central to many time-dependent cellular processes. Recent technological advances now make it possible to track the dynamics of gene networks in single cells under various environmental conditions using microfluidic 'lab-on-a-chip' devices, and researchers are using these new techniques to analyse cellular dynamics and discover regulatory mechanisms. These technologies are expected to yield novel insights and allow the construction of mathematical models that more accurately describe the complex dynamics of gene regulation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: A microfluidic device for monitoring Escherichia coli.
Figure 2: The Tesla microchemostat.
Figure 3: Microfluidic trapping devices designed for the long-term acquisition of single-cell data.
Figure 4: A microfluidic device designed for generating concentration gradients.
Figure 5: The microfluidic 'dial-a-wave' device.
Figure 6: Temporal driving with microfluidic chips to test mathematical models.

Similar content being viewed by others

References

  1. Koide, T., Pang, W. L. & Baliga, N. S. The role of predictive modelling in rationally re-engineering biological systems. Nature Rev. Microbiol. 7, 297–305 (2009).

    CAS  Google Scholar 

  2. Reder, C. Metabolic control theory: a structural approach. J. Theor. Biol. 135, 175–201 (1988).

    CAS  PubMed  Google Scholar 

  3. Edwards, J. S., Covert, M. & Palsson, B. Metabolic modelling of microbes: the flux-balance approach. Environ. Microbiol. 4, 133–140 (2002).

    PubMed  Google Scholar 

  4. Cline, M. S. et al. Integration of biological networks and gene expression data using Cytoscape. Nature Protoc. 2, 2366–2382 (2007).

    CAS  Google Scholar 

  5. Hasty, J., McMillen, D. & Collins, J. J. Engineered gene circuits. Nature 420, 224–230 (2002).

    CAS  PubMed  Google Scholar 

  6. McDonald, J. C. et al. Fabrication of microfluidic systems in poly(dimethylsiloxane). Electrophoresis 21, 27–40 (2000).

    CAS  PubMed  Google Scholar 

  7. Whitesides, G. M., Ostuni, E., Takayama, S., Jiang, X. Y. & Ingber, D. E. Soft lithography in biology and biochemistry. Ann. Rev. Biomed. Eng. 3, 335–373 (2001).

    CAS  Google Scholar 

  8. Ng, J. M., Gitlin, I., Stroock, A. D. & Whitesides, G. M. Components for integrated poly(dimethylsiloxane) microfluidic systems. Electrophoresis 23, 3461–3473 (2002). References 6–8 are good reviews covering the design and manufacture of microfluidic devices.

    CAS  PubMed  Google Scholar 

  9. Sia, S. K. & Whitesides, G. M. Microfluidic devices fabricated in poly(dimethylsiloxane) for biological studies. Electrophoresis 24, 3563–3576 (2003).

    CAS  PubMed  Google Scholar 

  10. Lidstrom, M. E. & Meldrum, D. R. Life-on-a-chip. Nature Rev. Microbiol. 1, 158–164 (2003).

    CAS  Google Scholar 

  11. Weibel, D. B., Diluzio, W. R. & Whitesides, G. M. Microfabrication meets microbiology. Nature Rev. Microbiol. 5, 209–218 (2007).

    CAS  Google Scholar 

  12. Chao, T. C. & Ros, A. Microfluidic single-cell analysis of intracellular compounds. J. R. Soc. Interface 5 (Suppl. 2), S139–S150 (2008).

    Google Scholar 

  13. Kim, S. M., Lee, S. H. & Suh, K. Y. Cell research with physically modified microfluidic channels: a review. Lab Chip 8, 1015–1023 (2008).

    CAS  PubMed  Google Scholar 

  14. Wang, C. J. & Levchenko, A. Microfluidics technology for systems biology research. Methods Mol. Biol. 500, 203–219 (2009).

    CAS  PubMed  Google Scholar 

  15. Shimomura, O., Johnson, F. H. & Saiga, Y. Extraction, purification and properties of aequorin, a bioluminescent protein from the luminous hydromedusan, Aequorea. J. Cell Comp. Physiol. 59, 223–239 (1962).

    CAS  PubMed  Google Scholar 

  16. Zhang, J., Campbell, R. E., Ting, A. Y. & Tsien, R. Y. Creating new fluorescent probes for cell biology. Nature Rev. Mol. Cell Biol. 3, 906–918 (2002).

    CAS  Google Scholar 

  17. Shaner, N. C., Steinbach, P. A. & Tsien, R. Y. A guide to choosing fluorescent proteins. Nature Methods 2, 905–909 (2005). References 15–17 detail the properties of various fluorescent proteins that are commonly used in synthetic biology.

    CAS  PubMed  Google Scholar 

  18. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

    CAS  PubMed  Google Scholar 

  19. Golding, I., Paulsson, J., Zawilski, S. M. & Cox, E. C. Real-time kinetics of gene activity in individual bacteria. Cell 123, 1025–1036 (2005).

    CAS  PubMed  Google Scholar 

  20. Valencia-Burton, M., McCullough, R. M., Cantor, C. R. & Broude, N. E. RNA visualization in live bacterial cells using fluorescent protein complementation. Nature Methods 4, 421–427 (2007).

    CAS  PubMed  Google Scholar 

  21. Tyagi, S. Splitting or stacking fluorescent proteins to visualize mRNA in living cells. Nature Methods 4, 391–392 (2007).

    CAS  PubMed  Google Scholar 

  22. Haim, L., Zipor, G., Aronov, S. & Gerst, J. E. A genomic integration method to visualize localization of endogenous mRNAs in living yeast. Nature Methods 4, 409–412 (2007).

    CAS  PubMed  Google Scholar 

  23. Stricker, J. et al. A fast, robust and tunable synthetic gene oscillator. Nature 456, 516–519 (2008). This study illustrates the maturity of synthetic biology; it reports the creation of a robust and tunable synthetic gene oscillator in E. coli.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl Acad. Sci. USA 99, 12795–12800 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Locke, J. C. & Elowitz, M. B. Using movies to analyse gene circuit dynamics in single cells. Nature Rev. Microbiol. 7, 383–392 (2009).

    CAS  Google Scholar 

  26. Austin, D. W. et al. Gene network shaping of inherent noise spectra. Nature 439, 608–611 (2006).

    CAS  PubMed  Google Scholar 

  27. Simpson, M. L., Cox, C. D. & Sayler, G. S. Frequency domain analysis of noise in autoregulated gene circuits. Proc. Natl Acad. Sci. USA 100, 4551–4556 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Cubitt, A. B. et al. Understanding, improving and using green fluorescent proteins. Trends Biochem. Sci. 20, 448–455 (1995).

    CAS  PubMed  Google Scholar 

  29. Nagai, T. et al. A variant of yellow fluorescent protein with fast and efficient maturation for cell-biological applications. Nature Biotech. 20, 87–90 (2002).

    CAS  Google Scholar 

  30. Rogers, S., Wells, R. & Rechsteiner, M. Amino acid sequences common to rapidly degraded proteins: the PEST hypothesis. Science 234, 364–368 (1986).

    CAS  PubMed  Google Scholar 

  31. Andersen, J. B. et al. New unstable variants of green fluorescent protein for studies of transient gene expression in bacteria. Appl. Environ. Microbiol. 64, 2240–2246 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Grilly, C., Stricker, J., Pang, W. L., Bennett, M. R. & Hasty, J. A synthetic gene network for tuning protein degradation in Saccharomyces cerevisiae. Mol. Syst. Biol. 3, 127 (2007).

    PubMed  PubMed Central  Google Scholar 

  33. Charvin, G., Cross, F. R. & Siggia, E. D. A microfluidic device for temporally controlled gene expression and long-term fluorescent imaging in unperturbed dividing yeast cells. PLoS ONE 3, e1468 (2008).

    PubMed  PubMed Central  Google Scholar 

  34. Khandurina, J. et al. Integrated system for rapid PCR-based DNA analysis in microfluidic devices. Anal. Chem. 72, 2995–3000 (2000).

    CAS  PubMed  Google Scholar 

  35. Sanders, G. H. W. & Manz, A. Chip-based microsystems for genomic and proteomic analysis. Trends Analyt. Chem. 19, 364–378 (2000).

    CAS  Google Scholar 

  36. Lagally, E. T., Medintz, I. & Mathies, R. A. Single-molecule DNA amplification and analysis in an integrated microfluidic device. Anal. Chem. 73, 565–570 (2001).

    CAS  PubMed  Google Scholar 

  37. Ramsey, J. D., Jacobson, S. C., Culbertson, C. T. & Ramsey, J. M. High-efficiency, two-dimensional separations of protein digests on microfluidic devices. Anal. Chem. 75, 3758–3764 (2003).

    CAS  PubMed  Google Scholar 

  38. McClain, M. A. et al. Microfluidic devices for the high-throughput chemical analysis of cells. Anal. Chem. 75, 5646–5655 (2003).

    CAS  PubMed  Google Scholar 

  39. Hong, J. W. & Quake, S. R. Integrated nanoliter systems. Nature Biotechnol. 21, 1179–1183 (2003). This review discusses the use of microfluidic devices for high-throughput biochemical assays.

    CAS  Google Scholar 

  40. Anderson, J. R. et al. Fabrication of topologically complex three-dimensional microfluidic systems in PDMS by rapid prototyping. Anal. Chem. 72, 3158–3164 (2000).

    CAS  PubMed  Google Scholar 

  41. Chiu, D. T., Pezzoli, E., Wu, H., Stroock, A. D. & Whitesides, G. M. Using three-dimensional microfluidic networks for solving computationally hard problems. Proc. Natl Acad. Sci. USA 98, 2961–2966 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Thorsen, T., Maerkl, S. J. & Quake, S. R. Microfluidic large-scale integration. Science 298, 580–584 (2002).

    CAS  PubMed  Google Scholar 

  43. Balagaddé, F. K., You, L., Hansen, C. L., Arnold, F. H. & Quake, S. R. Long-term monitoring of bacteria undergoing programmed population control in a microchemostat. Science 309, 137–140 (2005).

    PubMed  Google Scholar 

  44. Marcus, J. S., Anderson, W. F. & Quake, S. R. Microfluidic single-cell mRNA isolation and analysis. Anal. Chem. 78, 3084–3089 (2006).

    CAS  PubMed  Google Scholar 

  45. Maerkl, S. J. & Quake, S. R. A systems approach to measuring the binding energy landscapes of transcription factors. Science 315, 233–237 (2007).

    CAS  PubMed  Google Scholar 

  46. Fu, A. Y., Spence, C., Scherer, A., Arnold, F. H. & Quake, S. R. A microfabricated fluorescence-activated cell sorter. Nature Biotech. 17, 1109–1111 (1999).

    CAS  Google Scholar 

  47. Li, P. C. H. & Harrison, D. J. Transport, manipulation, and reaction of biological cells on-chip using electrokinetic effects. Anal. Chem. 69, 1564–1568 (1997).

    CAS  PubMed  Google Scholar 

  48. Fu, A. Y., Chou, H. P., Spence, C., Arnold, F. H. & Quake, S. R. An integrated microfabricated cell sorter. Anal. Chem. 74, 2451–2457 (2002).

    CAS  PubMed  Google Scholar 

  49. Prokop, A. et al. NanoLiterBioReactor: long-term mammalian cell culture at nanofabricated scale. Biomed. Microdevices 6, 325–339 (2004).

    CAS  PubMed  Google Scholar 

  50. Groisman, A. et al. A microfluidic chemostat for experiments with bacterial and yeast cells. Nature Methods 2, 685–689 (2005).

    CAS  PubMed  Google Scholar 

  51. Cookson, S., Ostroff, N., Pang, W. L., Volfson, D. & Hasty, J. Monitoring dynamics of single-cell gene expression over multiple cell cycles. Mol. Syst. Biol. 1, 2005.0024 (2005).

  52. Ryley, J. & Pereira-Smith, O. M. Microfluidics device for single cell gene expression analysis in Saccharomyces cerevisiae. Yeast 23, 1065–1073 (2006).

    CAS  PubMed  Google Scholar 

  53. Cai, L., Friedman, N. & Xie, X. S. Stochastic protein expression in individual cells at the single molecule level. Nature 440, 358–362 (2006).

    CAS  PubMed  Google Scholar 

  54. Di Carlo, D., Aghdam, N. & Lee, L. P. Single-cell enzyme concentrations, kinetics, and inhibition analysis using high-density hydrodynamic cell isolation arrays. Anal. Chem. 78, 4925–4930 (2006).

    CAS  PubMed  Google Scholar 

  55. Jeon, N. L. et al. Generation of solution and surface gradients using microfluidic systems. Langmuir 16, 8311–8316 (2000). This was one of the first investigations to use a microfluidic device capable of generating spatial chemical gradients to study a biological phenomenon.

    CAS  Google Scholar 

  56. Dertinger, S. K. W., Chiu, D. T., Jeon, N. L. & Whitesides, G. M. Generation of gradients having complex shapes using microfluidic networks. Anal. Chem. 73, 1240–1246 (2001).

    CAS  Google Scholar 

  57. Jeon, N. L. et al. Neutrophil chemotaxis in linear and complex gradients of interleukin-8 formed in a microfabricated device. Nature Biotech. 20, 826–830 (2002).

    CAS  Google Scholar 

  58. Mettetal, J. T., Muzzey, D., Gomez-Uribe, C. & van Oudenaarden, A. The frequency dependence of osmo-adaptation in Saccharomyces cerevisiae. Science 319, 482–484 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Hersen, P., McClean, M. N., Mahadevan, L. & Ramanathan, S. Signal processing by the HOG MAP kinase pathway. Proc. Natl Acad. Sci. USA 105, 7165–7170 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Bennett, M. R. et al. Metabolic gene regulation in a dynamically changing environment. Nature 454, 1119–1122 (2008). References 33 and 58–60 are seminal studies that used microfluidic devices to create temporal changes in the growth medium to study dynamic biological phenomena.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Siegal-Gaskins, D. & Crosson, S. Tightly regulated and heritable division control in single bacterial cells. Biophys. J. 95, 2063–2072 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. McKnight, T. E. et al. Intracellular integration of synthetic nanostructures with viable cells for controlled biochemical manipulation. Nanotechnology 14, 551–556 (2003).

    CAS  Google Scholar 

  63. Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L. & Leibler, S. Bacterial persistence as a phenotypic switch. Science 305, 1622–1625 (2004).

    CAS  PubMed  Google Scholar 

  64. 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 105, 6145–6149 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Heo, J., Thomas, K. J., Seong, G. H. & Crooks, R. M. A microfluidic bioreactor based on hydrogel-entrapped E. coli: cell viability, lysis, and intracellular enzyme reactions. Anal. Chem. 75, 22–26 (2003).

    CAS  PubMed  Google Scholar 

  66. Zhang, Z. et al. Microchemostat–microbial continuous culture in a polymer-based, instrumented microbioreactor. Lab Chip 6, 906–913 (2006).

    CAS  PubMed  Google Scholar 

  67. Peng, X. Y. & Li, P. C. A three-dimensional flow control concept for single-cell experiments on a microchip. 1. Cell selection, cell retention, cell culture, cell balancing, and cell scanning. Anal. Chem. 76, 5273–5281 (2004).

    CAS  PubMed  Google Scholar 

  68. Schmitz, C. H. J., Rowat, A. C., Koster, S. & Weitz, D. A. Dropspots: a picoliter array in a microfluidic device. Lab Chip 9, 44–49 (2009).

    CAS  PubMed  Google Scholar 

  69. Park, M. C., Hur, J. Y., Kwon, K. W., Park, S. H. & Suh, K. Y. Pumpless, selective docking of yeast cells inside a microfluidic channel induced by receding meniscus. Lab Chip 6, 988–994 (2006).

    CAS  PubMed  Google Scholar 

  70. Yun, K. S. & Yoon, E. Micro/nanofluidic device for single-cell-based assay. Biomed. Microdevices 7, 35–40 (2005).

    CAS  PubMed  Google Scholar 

  71. Wheeler, A. R. et al. Microfluidic device for single-cell analysis. Anal. Chem. 75, 3581–3586 (2003).

    CAS  PubMed  Google Scholar 

  72. Thompson, D. M. et al. Dynamic gene expression profiling using a microfabricated living cell array. Anal. Chem. 76, 4098–4103 (2004).

    CAS  PubMed  Google Scholar 

  73. Lu, H. et al. Microfluidic shear devices for quantitative analysis of cell adhesion. Anal. Chem. 76, 5257–5264 (2004).

    CAS  PubMed  Google Scholar 

  74. King, K. R. et al. A high-throughput microfluidic real-time gene expression living cell array. Lab Chip 7, 77–85 (2007).

    CAS  PubMed  Google Scholar 

  75. Volfson, D., Cookson, S., Hasty, J. & Tsimring, L. S. Biomechanical ordering of dense cell populations. Proc. Natl Acad. Sci. USA 105, 15346–15351 (2008).

    PubMed  PubMed Central  Google Scholar 

  76. Cho, H. et al. Self-organization in high-density bacterial colonies: efficient crowd control. PLoS Biol. 5, e302 (2007).

    PubMed  PubMed Central  Google Scholar 

  77. Hao, N. et al. Regulation of cell signaling dynamics by the protein kinase-scaffold Ste5. Mol. Cell 30, 649–656 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Mao, H., Yang, T. & Cremer, P. S. A microfluidic device with a linear temperature gradient for parallel and combinatorial measurements. J. Am. Chem. Soc. 124, 4432–4435 (2002).

    CAS  PubMed  Google Scholar 

  79. Holden, M. A., Kumar, S., Castellana, E. T., Beskok, A. & Cremer, P. S. Generating fixed concentration arrays in a microfluidic device. Sens. Actuators B Chem. 92, 199–207 (2003).

    CAS  Google Scholar 

  80. Zhu, X. et al. Arrays of horizontally-oriented mini-reservoirs generate steady microfluidic flows for continuous perfusion cell culture and gradient generation. Analyst 129, 1026–1031 (2004).

    CAS  PubMed  Google Scholar 

  81. Walker, G. M., Ozers, M. S. & Beebe, D. J. Cell infection within a microfluidic device using virus gradients. Sens. Actuators B Chem. 98, 347–355 (2004).

    CAS  Google Scholar 

  82. Jiang, X. et al. A general method for patterning gradients of biomolecules on surfaces using microfluidic networks. Anal. Chem. 77, 2338–2347 (2005).

    CAS  PubMed  Google Scholar 

  83. Irimia, D., Geba, D. A. & Toner, M. Universal microfluidic gradient generator. Anal. Chem. 78, 3472–3477 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Mao, H., Cremer, P. S. & Manson, M. D. A sensitive, versatile microfluidic assay for bacterial chemotaxis. Proc. Natl Acad. Sci. USA 100, 5449–5454 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Diao, J. et al. A three-channel microfluidic device for generating static linear gradients and its application to the quantitative analysis of bacterial chemotaxis. Lab Chip 6, 381–388 (2006).

    CAS  PubMed  Google Scholar 

  86. Lin, F. & Butcher, E. C. T cell chemotaxis in a simple microfluidic device. Lab Chip 6, 1462–1469 (2006).

    CAS  PubMed  Google Scholar 

  87. Chung, B. G. et al. Human neural stem cell growth and differentiation in a gradient-generating microfluidic device. Lab Chip 5, 401–406 (2005).

    CAS  PubMed  Google Scholar 

  88. Paliwal, S. et al. MAPK-mediated bimodal gene expression and adaptive gradient sensing in yeast. Nature 446, 46–51 (2007).

    CAS  PubMed  Google Scholar 

  89. Lin, F. et al. Generation of dynamic temporal and spatial concentration gradients using microfluidic devices. Lab Chip 4, 164–167 (2004).

    CAS  PubMed  Google Scholar 

  90. Irimia, D. et al. Microfluidic system for measuring neutrophil migratory responses to fast switches of chemical gradients. Lab Chip 6, 191–198 (2006).

    CAS  PubMed  Google Scholar 

  91. Ingolia, N. T. & Weissman, J. S. Systems biology — reverse engineering the cell. Nature 454, 1059–1062 (2008).

    CAS  PubMed  Google Scholar 

  92. Tourovskaia, A., Figueroa-Masot, X. & Folch, A. Differentiation-on-a-chip: a microfluidic platform for long-term cell culture studies. Lab Chip 5, 14–19 (2005).

    CAS  PubMed  Google Scholar 

  93. Olofsson, J. et al. A chemical waveform synthesizer. Proc. Natl Acad. Sci. USA 102, 8097–8102 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Lee, P. J., Gaige, T. A. & Hung, P. J. Dynamic cell culture: a microfluidic function generator for live cell microscopy. Lab Chip 9, 164–166 (2009).

    CAS  PubMed  Google Scholar 

  95. Zhang, X. & Roper, M. G. Microfluidic perfusion system for automated delivery of temporal gradients to islets of Langerhans. Anal. Chem. 81, 1162–1168 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Charvin, G., Cross, F. R. & Siggia, E. D. Forced periodic expression of G1 cyclins phase-locks the budding yeast cell cycle. Proc. Natl Acad. Sci. USA 106, 6632–6637 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Chen, D. et al. The chemistrode: a droplet-based microfluidic device for stimulation and recording with high temporal, spatial, and chemical resolution. Proc. Natl Acad. Sci. USA 105, 16843–16848 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. King, K. R., Wang, S., Jayaraman, A., Yarmush, M. L. & Toner, M. Microfluidic flow-encoded switching for parallel control of dynamic cellular microenvironments. Lab Chip 8, 107–116 (2008).

    CAS  PubMed  Google Scholar 

  99. Taylor, R. J. et al. Dynamic analysis of MAPK signaling using a high-throughput microfluidic single-cell imaging platform. Proc. Natl Acad. Sci. USA 106, 3758–3763 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Higgins, J. M., Eddington, D. T., Bhatia, S. N. & Mahadevan, L. Sickle cell vasoocclusion and rescue in a microfluidic device. Proc. Natl Acad. Sci. USA 104, 20496–20500 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Polinkovsky, M., Gutierrez, E., Levchenko, A. & Groisman, A. Fine temporal control of the medium gas content and acidity and on-chip generation of series of oxygen concentrations for cell cultures. Lab Chip 9, 1073–1084 (2009).

    CAS  PubMed  Google Scholar 

  102. Breslauer, D. N., Lee, P. J. & Lee, L. P. Microfluidics-based systems biology. Mol. Biosyst. 2, 97–112 (2006).

    CAS  PubMed  Google Scholar 

  103. Kim, H. J., Boedicker, J. Q., Choi, J. W. & Ismagilov, R. F. Defined spatial structure stabilizes a synthetic multispecies bacterial community. Proc. Natl Acad. Sci. USA 105, 18188–18193 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Keymer, J. E., Galajda, P., Muldoon, C., Park, S. & Austin, R. H. Bacterial metapopulations in nanofabricated landscapes. Proc. Natl Acad. Sci. USA 103, 17290–17295 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Keymer, J. E., Galajda, P., Lambert, G., Liao, D. & Austin, R. H. Computation of mutual fitness by competing bacteria. Proc. Natl Acad. Sci. USA 105, 20269–20273 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Miller, M. B. & Bassler, B. L. Quorum sensing in bacteria. Annu. Rev. Microbiol. 55, 165–199 (2001).

    CAS  PubMed  Google Scholar 

  107. Lucchetta, E. M., Lee, J. H., Fu, L. A., Patel, N. H. & Ismagilov, R. F. Dynamics of Drosophila embryonic patterning network perturbed in space and time using microfluidics. Nature 434, 1134–1138 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Cai, L., Dalal, C. K. & Elowitz, M. B. Frequency-modulated nuclear localization bursts coordinate gene regulation. Nature 455, 485–490 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. De Jong, H. Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9, 67–103 (2002).

    CAS  PubMed  Google Scholar 

  110. Glass, L. & Kauffman, S. A. The logical analysis of continuous, non-linear biochemical control networks. J. Theor. Biol. 39, 103–129 (1973).

    CAS  PubMed  Google Scholar 

  111. Savageau, M. A. Comparison of classical and autogenous systems of regulation in inducible operons. Nature 252, 546–549 (1974).

    CAS  PubMed  Google Scholar 

  112. Mather, W., Bennett, M. R., Hasty, J. & Tsimring, L. S. Delay-induced degrade-and-fire oscillations in small genetic circuits. Phys. Rev. Lett. 102, 068105 (2009).

  113. Tran, L. M., Rizk, M. L. & Liao, J. C. Ensemble modeling of metabolic networks. Biophys. J. 95, 5606–5617 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Kepler, T. B. & Elston, T. C. Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations. Biophys. J. 81, 3116–3136 (2001). References 109–111 and 114 discuss some of the best modelling techniques that are common to both systems and synthetic biology, especially those that model the dynamics and stochasticity of gene regulation.

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Alon, U. An Introduction to Systems Biology (Chapman and Hall/CRC, Boca Raton, 2007).

    Google Scholar 

  116. Zamir, E. & Bastiaens, P. I. Reverse engineering intracellular biochemical networks. Nature Chem. Biol. 4, 643–647 (2008).

    CAS  Google Scholar 

  117. Hasty, J., Isaacs, F., Dolnik, M., McMillen, D. & Collins, J. J. Designer gene networks: towards fundamental cellular control. Chaos 11, 207–220 (2001).

    CAS  PubMed  Google Scholar 

  118. Rao, C. V. & Arkin, A. P. Stochastic chemical kinetics and the quasi-steady-state assumption: application to the Gillespie algorithm. J. Chem. Phys. 118, 4999–5010 (2003).

    CAS  Google Scholar 

  119. Gillespie, D. T. Exact stochastic simulation of coupled chemical-reactions. J. Phys. Chem. 81, 2340–2361 (1977). This paper describes the Gillespie algorithm, which is used to simulate systems of randomly interacting chemical species and is now ubiquitously used in the synthetic biology community.

    CAS  Google Scholar 

  120. Volfson, D. et al. Origins of extrinsic variability in eukaryotic gene expression. Nature 439, 861–864 (2006).

    CAS  PubMed  Google Scholar 

  121. MacDonald, N. Time lag in a model of a biochemical reaction sequence with end product inhibition. J. Theor. Biol. 67, 549–556 (1977).

    CAS  PubMed  Google Scholar 

  122. Mahaffy, J. M. & Pao, C. V. Models of genetic control by repression with time delays and spatial effects. J. Math. Biol. 20, 39–57 (1984).

    CAS  PubMed  Google Scholar 

  123. McAdams, H. H. & Shapiro, L. Circuit simulation of genetic networks. Science 269, 650–656 (1995).

    CAS  PubMed  Google Scholar 

  124. Bratsun, D., Volfson, D., Tsimring, L. S. & Hasty, J. Delay-induced stochastic oscillations in gene regulation. Proc. Natl Acad. Sci. USA 102, 14593–14598 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Bundschuh, R., Hayot, F. & Jayaprakash, C. Fluctuations and slow variables in genetic networks. Biophys. J. 84, 1606–1615 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. Bennett, M. R., Volfson, D., Tsimring, L. & Hasty, J. Transient dynamics of genetic regulatory networks. Biophys. J. 92, 3501–3512 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

    CAS  PubMed  Google Scholar 

  128. Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000). References 127 and 128 are two of the earliest triumphs of synthetic biology, the construction of a genetic toggle switch and a synthetic oscillator, respectively.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank O. Mondragon and S. Cookson for initial literature searches, and B. Baumgartner for thorough readings of the drafts. This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health (GMO79333).

Author information

Authors and Affiliations

  1. Department of Biochemistry and Cell Biology and Institute of Biosciences and Bioengineering, Rice University, 6100 Main Street, Houston, 77005-1892, Texas, USA

    Matthew R. Bennett

  2. Departments of Molecular Biology and Bioengineering and BioCircuits Institute, University of California, San Diego, La Jolla, 92093-0412, California, USA

    Jeff Hasty

Authors
  1. Matthew R. Bennett
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeff Hasty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthew R. Bennett.

Related links

Related links

DATABASES

FlyBase

even skipped

FURTHER INFORMATION

Mathew R. Bennett's homepage

Jeff Hasty's homepage

Glossary

Intrinsic noise

Random, stochastic fluctuations in gene expression caused by a small number of reactants interacting in a finite volume.

Extrinsic noise

Fluctuations in gene expression that are not caused by intrinsic noise.

Time-lapse fluorescence microscopy

The repeated imaging of fluorescent markers using microscopy over a period of time, thus allowing a movie of the dynamics of gene expression or signalling networks to be obtained.

Bacterial persistence

Similar to antibiotic resistance, bacterial persistence is the phenomenon by which a fraction of a genetically homogeneous bacterial colony will survive antibiotic treatment but retain antibiotic sensitivity following regrowth.

Polydimethylsiloxane

An optically clear organic polymer that is commonly used for soft lithography.

Stochastic

Probabilistic; governed by chance.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bennett, M., Hasty, J. Microfluidic devices for measuring gene network dynamics in single cells. Nat Rev Genet 10, 628–638 (2009). https://doi.org/10.1038/nrg2625

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg2625

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing