Adaptive Protocol Synthesis and Error Recovery in Micro-Electrode-Dot-Array (MEDA) Microfluidic Biochips

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Droplet-based ("digital") microfluidic biochips (DMFBs) are revolutionizing high-throughput DNA sequencing and point-of-care clinical diagnosis. Using DMFBs, bioassay protocols are scaled down to droplet size and executed through software-based control of nanoliter droplets on a patterned electrode array. However, technology transition to industry has been challenging as today's DMFBs suffer from several key limitations: (1) constraints on droplet size; (2) difficulty of sensor integration for real-time detection and monitoring; and (3) reliability/yield concerns. To overcome these limitations, micro-electrode-dot-array (MEDA) biochips have been recently developed, incorporating real-time capacitive sensing on every microelectrode to detect the property and location of a droplet. Such 'sensing maps' open up the exciting opportunity of cyber-physical MEDA biochips that can dynamically respond to bioassay outcomes, perform real-time error recovery, and execute "if-then-else" protocols from biochemistry necessary to support the next generation of cyber-physical systems (CPS) with tightly integrated lab-on-chip sensing technology. Despite such tremendous promise, a significant barrier in the exploitation of MEDA for realistic biochemistry arises from the need to manually control biochemical protocols on the biochip. This research is thus motivated by the need to enable the execution of biomolecular assays on programmable and cyber-physical MEDA biochips. To take full advantage of the dynamic adaptation capabilities of MEDA, there is a need for a synthesis and run-time optimization framework that can be agile in its ability to respond to real-time sensor feedback. The proposed research therefore constitutes a comprehensive effort towards the realization of MEDA-based CPS, resulting in new applications that would, for instance, enable breakthroughs in cancer treatment or atmospheric aerosol measurements for pollution monitoring in smart cities.This is aimed at developing an integrated system solution for MEDA that includes advances in both hardware and software. Specific research products include the following innovations: (1) Modeling and robust controller design, which will involve offline model-based protocol synthesis and online learning-based protocol/model adaptation; (2) Adaptive and elastic synthesis techniques that comprehensively incorporate all the MEDA-specific droplet operations; (3) Optimization methods for multiple-reactant synthesis, which will involve on-chip sample preparation and optimization of the fluidic steps associated with dilution, mixing, and the generation of concentration gradients; (4) Fault tolerance through error recovery based on real-time sensing, droplet tracking, and adaptive MEDA-specific fluidic operations; and (5) MEDA biochip design, fabrication, and testbed setup, and the demonstration of real-time adaptation under software control for cell analysis in personalized cancer treatment. These breakthroughs will advance MEDA from an exploratory platform used to demonstrate droplet manipulation, to a mature platform that microbiologists and biochemists can use for implementing realistic protocols. The project also has an extensive education and outreach component, including curriculum development, expansion of hands-on research opportunities for undergraduate and graduate students, and international collaboration. For instance, MEDA-CPS will be used as an important example to showcase real-time adaptation in new undergraduate and graduate courses on modeling, design, and analysis of embedded control and cyber-physical systems. Tutorials at top conferences and benchmark dissemination activities will benefit the broader research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.