{"title":"Multi-target Many-Reactant Sample Preparation for Reactant Minimization on Microfluidic Biochips","authors":"Yung-Chun Lei, Tien-Kuo Lin, Juinn-Dar Huang","doi":"10.1109/ISVLSI.2018.00124","DOIUrl":null,"url":null,"abstract":"Sample preparation is one of essential steps in biochemical applications. It produces solutions with target concentrations through mixing various reactants in a specific way. In this paper, we propose a reactant cost minimization technique, M2SPA, for multi-target many-reactant sample preparation on microfluidic biochips through maximally sharing identical intermediate solutions among different targets. M2SPA first represents target concentrations as a recipe cube, searches all feasible candidates for intermediate solution sharing among targets, and then selects the one with the best cost saving for action. Experimental results show that the proposed algorithm can reduce up to 15.7% of reactant cost as compared to a state-of-the-art method.","PeriodicalId":114330,"journal":{"name":"2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISVLSI.2018.00124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Sample preparation is one of essential steps in biochemical applications. It produces solutions with target concentrations through mixing various reactants in a specific way. In this paper, we propose a reactant cost minimization technique, M2SPA, for multi-target many-reactant sample preparation on microfluidic biochips through maximally sharing identical intermediate solutions among different targets. M2SPA first represents target concentrations as a recipe cube, searches all feasible candidates for intermediate solution sharing among targets, and then selects the one with the best cost saving for action. Experimental results show that the proposed algorithm can reduce up to 15.7% of reactant cost as compared to a state-of-the-art method.