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Timothy K. Lu. Rubens, Jacob Rosenblum Massachusetts Institute of Technology. Department of Biology. Massachusetts Institute of Technology. Department of Biology 2016-12-05T19:10:52Z 2016-12-05T19:10:52Z 2016 2016 http://hdl.handle.net/1721.1/105566 963241818 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 2016. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 169-182). Natural organisms evolved gene networks that measure continuous environmental information and adjust gene expression to maximize fitness. Engineered cells will need to be capable of similar signal processing and computation in order to operate efficaciously in complex environments, like the human body. In this thesis I describe the development of synthetic biological circuits that enable such capabilities. In the first chapter, analog gene networks are engineered to measure the concentration of molecules and to perform mathematical operations such as addition and division. Building on this work, analog gene networks are next engineered to compensate for input-sensor circuit crosstalk. Finally, in the third chapter, analog-to-digital converters are introduced to convert signals from analog gene circuits into discrete regimes of gene expression. This mixed-signal approach merges the benefits of analog signal processing and of digital signal integration to enable robust continuous signal processing. I posit that the computational architecture demonstrated herein will enable novel applications for the field of synthetic biology. by Jacob Rosenblum Rubens. Ph.D. 182 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Biology. Synthetic biological circuits for continuous signal processing Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="979b5bd9-9f1f-4a62-8c8f-c949e9b16db6"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Synthetic biological circuits for continuous signal processing</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2016</PublicationDate> <Authors> <Author> <DisplayName>Rubens, Jacob Rosenblum</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Biology.</Keyword> <Abstract>Natural organisms evolved gene networks that measure continuous environmental information and adjust gene expression to maximize fitness. Engineered cells will need to be capable of similar signal processing and computation in order to operate efficaciously in complex environments, like the human body. In this thesis I describe the development of synthetic biological circuits that enable such capabilities. In the first chapter, analog gene networks are engineered to measure the concentration of molecules and to perform mathematical operations such as addition and division. Building on this work, analog gene networks are next engineered to compensate for input-sensor circuit crosstalk. Finally, in the third chapter, analog-to-digital converters are introduced to convert signals from analog gene circuits into discrete regimes of gene expression. This mixed-signal approach merges the benefits of analog signal processing and of digital signal integration to enable robust continuous signal processing. I posit that the computational architecture demonstrated herein will enable novel applications for the field of synthetic biology.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>