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Storage-Aware Algorithms for Dilution and Mixture Preparation with Flow-Based Lab-on-Chip

  • S. Bhattacharjee
  • , Robert Wille
  • , J.-D. Huang
  • , Bhargab B. Bhattacharya

Research output: Contribution to journalArticlepeer-review

Abstract

Lab-on-Chip (LoC) technology has emerged as one of the major driving forces behind the recent surge in biochemical protocol automation. Dilution and mixture preparation with fluids in a desired ratio, constitute basic steps in sample preparation for which several LoC based architectures and algorithms are known. The optimization of cost and time for such protocols requires proper sequencing of fluidic mix-and-split steps, and storage-units for holding intermediate-fluids to be reused in later steps. However, practical design constraints often limit the amount of on-chip storage in microfluidic LoC architectures and thus can badly affect the performance of the algorithms. Consequently, results generated by previous work may not be useful (in the case they require more storage-units than available) or more expensive than necessary (in the case when storage-units are available but not used, e.g., to further reduce the number of mix/split operations or reactant-cost). In this paper, we propose new algorithms for dilution and mixing with continuous-flow based LoCs that explicitly take care of storage constraints while optimizing reactant-cost and time of sample preparation. We present a symbolic formulation of the problem that captures the degree of freedom in algorithmic steps satisfying the specified storage constraints. Solvers based on Boolean satisfiability are used to achieve the optimization goals. Experimental results show the efficiency and effectiveness of the solution as well as a variety of applications where the proposed methods would prove beneficial.
Original languageEnglish
Article number8675329
Pages (from-to)816-829
Number of pages14
JournalIEEE Transactions on Computer Aided Design of Integrated Circuits and Systems (TCAD)
Volume39
Issue number4
DOIs
Publication statusPublished - Apr 2020

Fields of science

  • 102 Computer Sciences
  • 202 Electrical Engineering, Electronics, Information Engineering

JKU Focus areas

  • Digital Transformation

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