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research-article

Reactant minimization during sample preparation on digital microfluidic biochips using skewed mixing trees

Authors:

Juinn-Dar Huang,

Ting-Wei ChiangAuthors Info & Claims

ICCAD '12: Proceedings of the International Conference on Computer-Aided Design

Pages 377 - 383

https://doi.org/10.1145/2429384.2429464

Published: 05 November 2012 Publication History

Abstract

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.

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Cited By

Saha BMajumder M(2024)A Decision Tree Mechanism for Microfluidic Sample Preparation for Digital BiochipsJournal of Circuits, Systems and Computers10.1142/S0218126625501269Online publication date: 18-Oct-2024
https://doi.org/10.1142/S0218126625501269
Dong CChen XChen Z(2024)Reactant and Waste Minimization during Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips using Splitting TreesJournal of Electronic Testing: Theory and Applications10.1007/s10836-024-06103-z40:1(87-99)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10836-024-06103-z
YOSHIDA IYAMASHITA S(2023)Making General Dilution Graphs Robust to Unbalanced-Split Errors on Digital Microfluidic BiochipsIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.2021EAP1174E106.A:2(97-105)Online publication date: 1-Feb-2023
https://doi.org/10.1587/transfun.2021EAP1174
Show More Cited By

Index Terms

Reactant minimization during sample preparation on digital microfluidic biochips using skewed mixing trees
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)

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Reactant and Waste Minimization during Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips using Splitting Trees
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Reactant Minimization in Sample Preparation on Digital Microfluidic Biochips
Sample preparation plays an essential role in most biochemical reactions. Raw reactants are diluted to solutions with desirable concentration values in this process. Since the reactants, like infant's blood, DNA evidence collected from crime scenes, or ...

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Published In

cover image ACM Conferences

ICCAD '12: Proceedings of the International Conference on Computer-Aided Design

November 2012

781 pages

ISBN:9781450315739

DOI:10.1145/2429384

General Chair:
Alan J. Hu
Univ. of British Columbia, Vancouver, BC, Canada

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGDA: ACM Special Interest Group on Design Automation
IEEE CEDA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2012

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ICCAD '12

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SIGDA

ICCAD '12: The International Conference on Computer-Aided Design

November 5 - 8, 2012

California, San Jose

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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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Cited By

Saha BMajumder M(2024)A Decision Tree Mechanism for Microfluidic Sample Preparation for Digital BiochipsJournal of Circuits, Systems and Computers10.1142/S0218126625501269Online publication date: 18-Oct-2024
https://doi.org/10.1142/S0218126625501269
Dong CChen XChen Z(2024)Reactant and Waste Minimization during Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips using Splitting TreesJournal of Electronic Testing: Theory and Applications10.1007/s10836-024-06103-z40:1(87-99)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10836-024-06103-z
YOSHIDA IYAMASHITA S(2023)Making General Dilution Graphs Robust to Unbalanced-Split Errors on Digital Microfluidic BiochipsIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.2021EAP1174E106.A:2(97-105)Online publication date: 1-Feb-2023
https://doi.org/10.1587/transfun.2021EAP1174
Gountia D(2023)Reliability Issues in State-of-the-Art Microfluidic Biochips: A SurveyIETE Technical Review10.1080/02564602.2022.215895240:5(694-709)Online publication date: 8-Jan-2023
https://doi.org/10.1080/02564602.2022.2158952
Poddar SFink GHaselmayr WWille R(2022)A Generic Sample Preparation Approach for Different Microfluidic Labs-on-ChipsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.313532341:11(4612-4625)Online publication date: Nov-2022
https://doi.org/10.1109/TCAD.2021.3135323
Bera N(2022)Studies in Algorithms and Architectures for Sample Preparation with Digital MicrofluidicsIntelligent Technologies: Concepts, Applications, and Future Directions10.1007/978-981-19-1021-0_8(177-199)Online publication date: 22-May-2022
https://doi.org/10.1007/978-981-19-1021-0_8
Sanyal MChakraborty S(2022)Enhanced Multigradient Dilution PreparationProceedings of the International Conference on Computational Intelligence and Sustainable Technologies10.1007/978-981-16-6893-7_46(519-529)Online publication date: 12-Feb-2022
https://doi.org/10.1007/978-981-16-6893-7_46
Poddar SFink GHaselmayr WWille R(2021)Generic Sample Preparation for Different Microfluidic Platforms2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE51398.2021.9474059(336-339)Online publication date: 1-Feb-2021
https://doi.org/10.23919/DATE51398.2021.9474059
Poddar SBhattacharjee SFang SHo TBhattacharya B(2021)Demand-Driven Multi-Target Sample Preparation on Resource-Constrained Digital Microfluidic BiochipsACM Transactions on Design Automation of Electronic Systems10.1145/347439227:1(1-21)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3474392
Shayan MBhattacharjee SWille RChakrabarty KKarri R(2021)How Secure Are Checkpoint-Based Defenses in Digital Microfluidic Biochips?IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.298835140:1(143-156)Online publication date: Jan-2021
https://doi.org/10.1109/TCAD.2020.2988351
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