Efficient generation of dilution gradients with digital microfluidic biochips

Published

Journal Article

© 1982-2012 IEEE. Digital microfluidic biochips (DMFBs) are now being extensively used to automate several biochemical laboratory protocols such as clinical analysis, point-of-care diagnostics, or DNA sequencing. In many biological assays, e.g., bacterial susceptibility tests and cellular response analysis, samples, or reagents are required in multiple concentration (or dilution) factors, satisfying certain gradient patterns such as linear, exponential, or parabolic. Dilution gradients are traditionally prepared using continuous-flow microfluidic devices. Unfortunately, most of them suffer from inflexibility and nonprogrammability, and they require large volumes of costly stock-solutions. DMFBs, on the other hand, are shown to produce, more efficiently, samples with multiple dilution factors. However, none of the existing DMFB-based algorithms utilize the properties of the gradient-profile while optimizing reactant-cost and sample-preparation time. In this paper, we explore the underlying combinatorial attributes of different gradients and harnessed them for efficient production of the desired concentration profile. For linear gradients, we present theoretical results concerning the number of mix-split operations and waste production, and prove an upper bound on on-chip storage requirement. A cost-effective method for generating a wide class of exponential gradients is also proposed. Finally, in order to handle a complex-shaped gradient, we posit a digital-geometric technique to approximate it with a sequence of linear gradients. Experimental results on various gradient-profiles are presented in support of the proposed method.

Full Text

Duke Authors

Cited Authors

  • Bhattacharjee, S; Banerjee, A; Ho, TY; Chakrabarty, K; Bhattacharya, BB

Published Date

  • May 1, 2019

Published In

Volume / Issue

  • 38 / 5

Start / End Page

  • 874 - 887

International Standard Serial Number (ISSN)

  • 0278-0070

Digital Object Identifier (DOI)

  • 10.1109/TCAD.2018.2834413

Citation Source

  • Scopus