Microfluidics and Nanofluidics

, Volume 8, Issue 5, pp 677–685 | Cite as

Microfluidic network-based combinatorial dilution device for high throughput screening and optimization

  • Kangsun Lee
  • Choong Kim
  • Geunhui Jung
  • Tae Song Kim
  • Ji Yoon Kang
  • Kwang W. Oh
Research Paper

Abstract

We present a combinatorial dilution device using a three-layer microfluidic network that can produce systematic variations of buffer and additive solutions in a combinatorial fashion for high throughput screening and optimization. A proof-of-concept device providing seven combinations (ABC/D, AB/D, BC/D, AC/D, A/D, B/D, and C/D) of three additive samples (A, B, and C) into a buffer solution (D) has been demonstrated. Such combinations are often used in simplex-centroid mixture DOE (design of experiments), useful techniques to minimize the experimental efforts at maximal information output with systematic variations of large-scale components. Based on mathematical and electrical modeling and computational fluid dynamic simulation, the device has been designed, fabricated, and characterized.

Keywords

Combinatorial device Microfluidic network High throughput screening Design of experiments 

Supplementary material

10404_2009_500_MOESM1_ESM.doc (452 kb)
Supplementary material 1 (DOC 451 kb)

References

  1. Assay Guidance Manual Version 5.0. (2008) Eli Lilly and Company and NIH Chemical Genomics Center. http://www.ncgc.nih.gov/guidance/manual_toc.html
  2. Breslauer DN, Lee PJ et al (2006) Microfluidics-based systems biology. Mol Biosyst 2(2):97–112CrossRefGoogle Scholar
  3. Campbell K, Groisman A (2007) Generation of complex concentration profiles in microchannels in a logarithmically small number of steps. Lab Chip 7(2):264–272CrossRefGoogle Scholar
  4. Cooksey GA, Sip CG et al (2009) A multi-purpose microfluidic perfusion system with combinatorial choice of inputs, mixtures, gradient patterns, and flow rates. Lab Chip 9(3):417–426CrossRefGoogle Scholar
  5. Dertinger SKW, Chiu DT et al (2001) Generation of gradients having complex shapes using microfluidic networks. Anal Chem 73(6):1240–1246CrossRefGoogle Scholar
  6. Dittrich PS, Manz A (2006) Lab-on-a-chip: microfluidics in drug discovery. Nat Rev Drug Discov 5(3):210–218CrossRefGoogle Scholar
  7. Garcia-Egido E, Spikmans V et al (2003) Synthesis and analysis of combinatorial libraries performed in an automated micro reactor system. Lab Chip 3(2):73–76CrossRefGoogle Scholar
  8. Greve F, Seemann L et al (2007) A hybrid microsystem for parallel perfusion experiments on living cells. J Micromech Microeng 17(8):1721–1730CrossRefGoogle Scholar
  9. Hattori K, Sugiura S et al (2009) Generation of arbitrary monotonic concentration profiles by a serial dilution microfluidic network composed of microchannels with a high fluidic-resistance ratio. Lab Chip. doi:10.1039/b816995k
  10. Holden MA, Kumar S et al (2003) Generating fixed concentration arrays in a microfluidic device. Sens Actuators B 92(1–2):199–207CrossRefGoogle Scholar
  11. Irimia D, Geba DA et al (2006) Universal microfluidic gradient generator. Anal Chem 78(10):3472–3477CrossRefGoogle Scholar
  12. Islam RS, Tisi D et al (2007) Framework for the rapid optimization of soluble protein expression in Escherichia coli combining microscale experiments and statistical experimental design. Biotechnol Prog 23(4):785–793Google Scholar
  13. Ismagilov RF, Ng JMK et al (2001) Microfluidic arrays of fluid–fluid diffusional contacts as detection elements and combinatorial tools. Anal Chem 73(21):5207–5213CrossRefGoogle Scholar
  14. Jacobson SC, McKnight TE et al (1999) Microfluidic devices for electrokinetically driven parallel and serial mixing. Anal Chem 71(20):4455–4459CrossRefGoogle Scholar
  15. Jeon NL, Dertinger SKW et al (2000) Generation of solution and surface gradients using microfluidic systems. Langmuir 16(22):8311–8316CrossRefGoogle Scholar
  16. Kang JH, Um E et al (2009) Fabrication of a poly(dimethylsiloxane) membrane with well-defined through-holes for three-dimensional microfluidic networks. J Micromech Microeng 19:045027 (6 pp)Google Scholar
  17. Kikutani Y, Horiuchi T et al (2002) Glass microchip with three-dimensional microchannel network for 2 × 2 parallel synthesis. Lab Chip 2(4):188–192CrossRefGoogle Scholar
  18. Kikutani Y, Ueno M et al (2005) Continuous-flow chemical processing in three-dimensional microchannel network for on-chip integration of multiple reactions in a combinatorial mode. QSAR Comb Sci 24(6):742–757CrossRefGoogle Scholar
  19. Kim C, Lee K et al (2008) A serial dilution microfluidic device using a ladder network generating logarithmic or linear concentrations. Lab Chip 8(3):473–479CrossRefGoogle Scholar
  20. Kirsten G, Maier WF (2004) Strategies for the discovery of new catalysts with combinatorial chemistry. Appl Surf Sci 223(1–3):87–101CrossRefGoogle Scholar
  21. Lee K, Kim C et al (2009) Generalized serial dilution module for monotonic and arbitrary microfluidic gradient generators. Lab Chip 9(5):709–717CrossRefGoogle Scholar
  22. Liu MC, Ho D et al (2008) Monolithic fabrication of three-dimensional microfluidic networks for constructing cell culture array with an integrated combinatorial mixer. Sens Actuators B 129(2):826–833CrossRefMathSciNetGoogle Scholar
  23. Maier WF, Stowe K et al (2007) Combinatorial and high-throughput materials science. Angew Chem Int Ed 46(32):6016–6067CrossRefGoogle Scholar
  24. Muteki K, MacGregor JF et al (2007) Mixture designs and models for the simultaneous selection of ingredients and their ratios. Chemom Intell Lab Syst 86(1):17–25CrossRefGoogle Scholar
  25. Narasimhan B, Mallapragada SK, Porter MD (2007) Combinatorial materials science. Wiley, New YorkGoogle Scholar
  26. Neils C, Tyree Z et al (2004) Combinatorial mixing of microfluidic streams. Lab Chip 4(4):342–350CrossRefGoogle Scholar
  27. Pereira SRM, Clerc F et al (2007) Optimisation methodologies and algorithms for research on catalysis employing high-throughput methods: comparison using the selox benchmark. Comb Chem High Throughput Screen 10(2):149–159CrossRefGoogle Scholar
  28. Schudel BR, Choi CJ, Cunningham BT, Kenis PJA (2009) Microfluidic chip for combinatorial mixing and screening of assays. Lab Chip 9:1676–1680Google Scholar
  29. Singh B, Dahiya M et al (2005) Optimizing drug delivery systems using systematic “design of experiments”. Part II: retrospect and prospects. Crit Rev Therap Drug Carr Syst 22(3):215–294CrossRefGoogle Scholar
  30. Smith CG, O’Donnell JT (2006) The process of new drug discovery and development. Informa Health Care, New YorkGoogle Scholar
  31. Timbrell JA (2000) Principles of biochemical toxicology. Taylor & Francis, LondonGoogle Scholar
  32. Tye H (2004) Application of statistical ‘design of experiments’ methods in drug discovery. Drug Discov Today 9(11):485–491CrossRefGoogle Scholar
  33. Walker GM, Monteiro-Riviere N et al (2007) A linear dilution microfluidic device for cytotoxicity assays. Lab Chip 7(2):226–232CrossRefGoogle Scholar
  34. Webster DC (2008) Combinatorial and high-throughput methods in macromolecular materials research and development. Macromol Chem Phys 209(3):237–246CrossRefGoogle Scholar
  35. Yang K, EI-Haik BS (2008) Design for six sigma: a roadmap for product development, McGraw-Hill Professional, New YorkGoogle Scholar
  36. Yu ZTF, Kamei KI et al (2009) Integrated microfluidic devices for combinatorial cell-based assays. Biomed Microdevices 11(3):547–555CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Kangsun Lee
    • 1
  • Choong Kim
    • 2
  • Geunhui Jung
    • 2
  • Tae Song Kim
    • 2
  • Ji Yoon Kang
    • 2
  • Kwang W. Oh
    • 1
  1. 1.SMALL (Nanobio Sensors and MicroActuators Learning Laboratory), Department of Electrical EngineeringUniversity at Buffalo, The State University of New York (SUNY at Buffalo)BuffaloUSA
  2. 2.Nano-Bioresearch CenterKorea Institute of Science and Technology (KIST)SeoulKorea

Personalised recommendations