Error-Correcting Sample Preparation with Cyberphysical Digital Microfluidic Lab-on-Chip

Abstract

Digital (droplet-based) microfluidic technology offers an attractive platform for implementing a wide variety of biochemical laboratory protocols, such as point-of-care diagnosis, DNA analysis, target detection, and drug discovery. A digital microfluidic biochip consists of a patterned array of electrodes on which tiny fluid droplets are manipulated by electrical actuation sequences to perform various fluidic operations, for example, dispense, transport, mix, or split. However, because of the inherent uncertainty of fluidic operations, the outcome of biochemical experiments performed on-chip can be erroneous even if the chip is tested a priori and deemed to be defect-free. In this article, we address an important error recoverability problem in the context of sample preparation. We assume a cyberphysical environment, in which the physical errors, when detected online at selected checkpoints with integrated sensors, can be corrected through recovery techniques. However, almost all prior work on error recoverability used checkpointing-based rollback approach, that is, re-execution of certain portions of the protocol starting from the previous checkpoint. Unfortunately, such techniques are expensive both in terms of assay completion time and reagent cost, and can never ensure full error-recovery in deterministic sense. We consider imprecise droplet mix-split operations and present a novel roll-forward approach where the erroneous droplets, thus produced, are used in the error-recovery process, instead of being discarded or remixed. All erroneous droplets participate in the dilution process and they mutually cancel or reduce the concentration-error when the target droplet is reached. We also present a rigorous analysis that reveals the role of volumetric-error on the concentration of a sample to be prepared, and we describe the layout of a lab-on-chip that can execute the proposed cyberphysical dilution algorithm. Our analysis reveals that fluidic errors caused by unbalanced droplet splitting can be classified as being either critical or non-critical , and only those of the former type require correction to achieve error-free sample dilution. Simulation experiments on various sample preparation test cases demonstrate the effectiveness of the proposed method.