Low-Cost Dilution Engine for Sample Preparation in Digital Microfluidic Biochips

Roy, Sudip; Bhattacharya, Bhargab B.; Ghoshal, S. K.; Chakrabarty, Krishnendu

doi:10.1109/ised.2012.70

Cited by 11 publications

(12 citation statements)

References 7 publications

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“…A majority of them [Bhattacharjee et al 2012Chiang et al 2013;Dinh et al 2014;Griffith et al 2006;Huang et al 2012;Mitra et al 2014Mitra et al , 2012Ren et al 2003;Roy et al 2010Roy et al , 2011aRoy et al , 2014aRoy et al , 2014bRoy et al , 2014cThies et al 2008] deal with with the task of diluting a fluid to a certain CF by mixing it with a buffer. Note that the dilution process needs only two input fluids to prepare a target solution, and up to two mixers are sufficient to complete the task [Roy et al 2010;Thies et al 2008].…”

Section: Prior Work: Dilution and Mixing Algorithmsmentioning

confidence: 99%

“…that the mixing tree is a full binary tree, that is, every node has exactly two children (representing a 1 : 1 mix/split step), or no children (representing an input reactant, that is, a variable) [Aho et al 1974;Roy et al 2014b]. The root starts at level d. The partitioning of the tree into a left and a right subtree at a level , is obtained in such a way that the sum of the component ratios (or the coefficients of variables) in each child node becomes 2 −1 .…”

Section: Analysis Of Rmamentioning

confidence: 99%

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Layout-Aware Mixture Preparation of Biochemical Fluids on Application-Specific Digital Microfluidic Biochips

Roy

Chakrabarti

Kumar

et al. 2015

ACM Trans. Des. Autom. Electron. Syst.

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The recent proliferation of digital microfluidic (DMF) biochips has enabled rapid on-chip implementation of many biochemical laboratory assays or protocols. Sample preprocessing, which includes dilution and mixing of reagents, plays an important role in the preparation of assays. The automation of sample preparation on a digital microfluidic platform often mandates the execution of a mixing algorithm, which determines a sequence of droplet mix-split steps (usually represented as a mixing graph). However, the overall cost and performance of on-chip mixture preparation not only depends on the mixing graph but also on the resource allocation and scheduling strategy, for instance, the placement of boundary reservoirs or dispensers, mixer modules, storage units, and physical design of droplet-routing pathways. In this article, we first present a new mixing algorithm based on a number-partitioning technique that determines a layout-aware mixing tree corresponding to a given target ratio of a number of fluids. The mixing graph produced by the proposed method can be implemented on a chip with a fewer number of crossovers among droplet-routing paths as well as with a reduced reservoir-to-mixer transportation distance. Second, we propose a routing-aware resource-allocation scheme that can be used to improve the performance of a given mixing algorithm on a chip layout. The design methodology is evaluated on various test cases to demonstrate its effectiveness in mixture preparation with the help of two representative mixing algorithms. Simulation results show that on average, the proposed scheme can reduce the number of crossovers among droplet-routing paths by 89.7% when used in conjunction with the new mixing algorithm, and by 75.4% when an earlier algorithm [Thies et al. 2008] is used. . 2015. Layout-Aware Mixture preparation of biochemical fluids on application-specific digital microfluidic biochips.

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Section: Prior Work: Dilution and Mixing Algorithmsmentioning

confidence: 99%

Section: Analysis Of Rmamentioning

confidence: 99%

Layout-Aware Mixture Preparation of Biochemical Fluids on Application-Specific Digital Microfluidic Biochips

Roy

Chakrabarti

Kumar

et al. 2015

ACM Trans. Des. Autom. Electron. Syst.

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“…In order to produce a dilution gradient of size 2 k+1 + 1, we need to supply 2 k droplets for each boundary CF (Lemma 2). Here, we demonstrate how a low-cost dilution engine [26] can be integrated on-chip for this purpose. We will illustrate the technique using the following example.…”

Section: Fig 4: Generation Of a Target Cf With Multiple Demandmentioning

confidence: 99%

“…5b. If boundary CFs other than 0% and 100% are needed, a dilution engine [26] can be used for generating them. We provide two dilution engines for generating the boundary CFs, which can be run in parallel to reduce sample preparation time.…”

Section: Architecturementioning

confidence: 99%

On Producing Linear Dilution Gradient of a Sample with a Digital Microfluidic Biochip

Bhattacharjee

Banerjee

et al. 2013

2013 International Symposium on Electronic System Design

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Digital microfluidic (DMF) biochips are now being extensively used to automate several biochemical laboratory protocols such as clinical analysis, point-of-care diagnostics, and polymerase chain reaction (PCR). In many biological assays, e.g., in bacterial susceptibility tests, samples and reagents are required in multiple concentration (or dilution) factors, satisfying certain "gradient" patterns such as linear, exponential, or parabolic. Dilution gradients are usually prepared with continuous-flow microfluidic devices; however, they suffer from inflexibility, nonprogrammability, and from large requirement of costly stock solutions. DMF biochips, on the other hand, are shown to produce, more efficiently, a set of random dilution factors. However, all existing algorithms fail to optimize cost or performance when a certain gradient pattern is required. In this work, we present an algorithm to generate any arbitrary linear gradient, on-chip, with minimum wastage, while satisfying a required accuracy in concentration factors. We present new theoretical results on the number of mix-split operations and waste computation, and prove an upper bound on storage requirement. The corresponding layout design of the biochip is also proposed. Simulation results on different linear gradients show a significant improvement in sample cost over three earlier algorithms used for the generation of multiple concentrations.

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“…Traditionally, solution preparation was implemented using continuous-flow microfluidic chips [6][7][8]. Recently, several automated mixing/dilution schemes for solution preparation on a digital microfludic platform have also been reported [9][10][11][12][13][14][15]. Almost all previous work related to dilution with DMFs demonstrated various methods for obtaining a target droplet with the desired concentration when a sample and a neutral buffer solution are supplied.…”

Section: Introductionmentioning

confidence: 99%

Sample preparation with multiple dilutions on digital microfluidic biochips

Bhattacharjee

Banerjee

Bhattacharya

2014

IET Computers & Digital Techniques

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Digital microfluidic (DMF) biochips offer a versatile platform for implementing several laboratory based biochemical protocols. These tiny chips can electrically control the dynamics of nanoliter volume of discrete fluid droplets on an electrode array by application of actuation patterns. One important step in biochemical sample preparation is dilution, where the objective is to prepare a fluid with a desired concentration factor. The protocols implemented on DMF biochips may require several different concentration values of a sample. In this study, the authors propose a scheme to produce such target droplets from a supply of an input sample and a buffer solution. Simulation results show a significant amount of savings in the number of mix-split steps and waste droplets in comparison to other methods for generating multiple concentration factors.

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Low-Cost Dilution Engine for Sample Preparation in Digital Microfluidic Biochips

Cited by 11 publications

References 7 publications

Layout-Aware Mixture Preparation of Biochemical Fluids on Application-Specific Digital Microfluidic Biochips

Layout-Aware Mixture Preparation of Biochemical Fluids on Application-Specific Digital Microfluidic Biochips

On Producing Linear Dilution Gradient of a Sample with a Digital Microfluidic Biochip

Sample preparation with multiple dilutions on digital microfluidic biochips

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