Algorithms for Producing Linear Dilution Gradient with Digital Microfluidics

TL;DR

This paper introduces an algorithm for generating arbitrary linear dilution gradients on digital microfluidic biochips, optimizing for minimal waste and cost while ensuring accuracy, with theoretical analysis and improved simulation results.

Contribution

It presents a novel algorithm for on-chip linear gradient generation that minimizes waste and cost, with theoretical bounds and layout design, outperforming previous methods.

Findings

Significant reduction in sample cost compared to earlier algorithms

Theoretical bounds on mix-split operations and waste

Effective layout design for biochips

Abstract

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, non-programmability, 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 the 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 the concentration factor. We present new theoretical results on the number of mix-split operations and waste computation, and prove an upper bound on the 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.