Abstraction layers for scalable microfluidic biocomputing
Natural Computing: an international journal
Optimization of dilution and mixing of biochemical samples using digital microfluidic biochips
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Layout-Aware Solution Preparation for Biochemical Analysis on a Digital Microfluidic Biochip
VLSID '11 Proceedings of the 2011 24th International Conference on VLSI Design
Digital microfluidic biochips: a vision for functional diversity and more than Moore
Proceedings of the International Conference on Computer-Aided Design
Performance Characterization of a Reconfigurable Planar-Array Digital Microfluidic System
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A High-Performance Droplet Routing Algorithm for Digital Microfluidic Biochips
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Cross-Contamination Aware Design Methodology for Pin-Constrained Digital Microfluidic Biochips
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Placement and Routing for Cross-Referencing Digital Microfluidic Biochips
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A Network-Flow Based Pin-Count Aware Routing Algorithm for Broadcast-Addressing EWOD Chips
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Sample preparation for many-reactant bioassay on DMFBs using common dilution operation sharing
Proceedings of the International Conference on Computer-Aided Design
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Sample preparation is an indispensable process to biochemical reactions. Original reactants are usually diluted to the solutions with desirable concentrations. Since the reactants, like infant's blood, DNA evidence collected from a crime scene, or costly reagents, are extremely valuable, the usage of reactant must be minimized in the sample preparation process. In this paper, we propose the first reactant minimization approach, REMIA, during sample preparation on digital microfluidic biochips (DMFBs). Given a target concentration, REMIA constructs a skewed mixing tree to guide the sample preparation process for reactant minimization. Experimental results demonstrate that REMIA can save about 31%~52% of reactant usage on average compared with three existing sample preparation methods. Besides, REMIA can be extended to tackle the sample preparation problem with multiple target concentrations, and the extended version also successfully decreases the reactant usage further.