SGER: Optimal Strategies for Moving Droplets in Digital Microfluidic Systems

University Of Washington, Seattle WA

Investigators

Abstract

Microfluidic systems are devices that can manipulate (e.g., handle, store, sort, and analyze) very small amounts of liquids (often much less than a microliter) with very high accuracy. Over the past decade, much progress has been achieved in miniaturizing components such as valves, pumps, and channels, and integrating them onto silicon, glass, or plastic chips. The anufacture of these systems often uses techniques derived from the integrated circuit and microprocessor industry. The goal is to create a complete lab on a chip, which could be employed in particular for novel biomedical and chemical tasks, including genomics and proteomics research, pathogen detection, and homeland security. The first generation of microfluidic devices has mostly used designs that are downscaled versions of conventional components, such as micro valves, micro pumps, and micro channels. However, recently a new generation of microfluidic systems has been introduced. These so-called digital microfluidic systemsexploit effects that are only available at very small scales. Electrowetting is such an effect: when a voltage is applied near a droplet that forms a bead on a hydrophobic surface then this droplet deforms in response to this voltage. By appropriate design, one can build systems that can move tiny droplets very rapidly and precisely across a surface. The big advantage of this approach is that the handling of liquid is performed by software and re-programmable at any time, depending on the task one wants to perform. This provides a level of flexibility that does not exist in traditional lab equipment or even first generation microfluidics. It is expected that these digital microfluidic systems could handle hundreds or thousands of droplets simultaneously, resulting in massively parallel performance of experiments. However, controlling such a large number the droplets is highly non-trivial: moving hundreds or thousands of droplets between reservoirs, analysis sites, reaction sites, and waste bins could be compared to a parking lot where some cars arrive, others want to leave, and yet others maybe want to find a better, shady spot. Our goal is to find the optimal motion plan for all droplets, resulting in a strategy that minimizes the time it takes to perform all experiments simultaneously. Theorists have shown that similar problems (such as the traveling salesman problem) are very difficult to solve optimally. Thus, our task in this project are to (a) develop a good theoretical understanding of the problem, (b) derive methods and computer software to automatically generate optimal solutions, and (c) if part b proves to be too hard, then find approximations that are close to optimal but easier to compute. The end result should be a system that takes as input a description of a digital microfluidic system plus all the start and goal states of all droplets, and generates as output a plan that moves all droplets from start to goal in (near) optimal time.