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dc.contributor.advisorRon Weiss.en_US
dc.contributor.authorElias, Blake Marshalen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-12-18T19:48:57Z
dc.date.available2018-12-18T19:48:57Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119762
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 129-132).en_US
dc.description.abstractSynthetic biology is the artificial design and engineering of biological systems and living organisms, drawing from disciplines such as biological and electrical engineering, computer science, biology and chemistry. Experimental work in synthetic biology is enabled by molecular biology techniques such as DNA assembly and molecular cloning. Researchers currently perform these techniques manually in wet-labs, which is expensive, time-consuming, requires extensive training and is unreliable. This thesis demonstrates a working robotic automation system that lowers the monetary- and time-cost of performing synthetic biology experiments, reduces the volume of Golden Gate DNA Assembly reactions by an order of magnitude using pin tools for small-volume liquid transfer, and which has successfully built 130 new genetic constructs using 192 genetic parts submitted by 11 researchers for both bacterial and mammalian systems. This thesis also presents a model which uses data on past assembly success to predict the outcome of reactions that the model had not seen. This model generates two sub-libraries of validated parts: one containing 79 parts (41% of those submitted) with a predicted assembly success rate of 90% or greater, the other with 28 parts (15% of those submitted) and 99% or greater predicted success, when using parts exclusively within each of the respective libraries.en_US
dc.description.statementofresponsibilityby Blake Marshal Elias.en_US
dc.format.extent132 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleHigh throughput pin-tool based automated DNA assemblyen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1078783280en_US


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