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dc.contributor.authorDorfan, Yuval
dc.contributor.authorEspah Borujeni, Amin
dc.contributor.authorPark, YongJin
dc.contributor.authorSaxena, Uma
dc.contributor.authorGondon, Ben
dc.contributor.authorVoigt, Christopher A.
dc.contributor.authorYeung, Enoch
dc.date.accessioned2020-09-08T17:14:07Z
dc.date.available2020-09-08T17:14:07Z
dc.date.issued2019-10
dc.identifier.isbn9781509006175
dc.identifier.urihttps://hdl.handle.net/1721.1/127200
dc.description.abstractGenetic circuits aredesigned to implement certain logic in living cells, keeping burden on the host cell minimal. However, manipulating the genome often will have a significant impact for various reasons (usage of the cell machinery to express new genes, toxicity of genes, interactions with native genes, etc.). In this work we utilize Koopman operator theory to construct data-driven models of transcriptomic-level dynamics from noisy and temporally sparse RNAseq measurements. We show how Koopman models can be used to quantify impact on genetic circuits. We consider an experimental example, using high-Throughput RNAseq measurements collected from wild-Type E. coli, single gate components transformed in E. coli, and a NAND circuit composed from individual gates in E. coli, to explore how Koopman subspace functions encode increasing circuit interference on E. coli chassis dynamics. The algorithm provides a novel method for quantifying the impact of synthetic biological circuits on host-chassis dynamics.en_US
dc.description.sponsorshipUnited States. Defense Advanced Research Projects Agency (Grants FA8750-17-C-0229, HR001117C0092, HR001117C0094, DEAC0576RL01830)en_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/biocas.2019.8919140en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleA data-driven method for quantifying the impact of a genetic circuit on its hosten_US
dc.typeArticleen_US
dc.identifier.citationHasnain, Aqib et al. “A data-driven method for quantifying the impact of a genetic circuit on its host.” Paper presented at the BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings, Nara, Japan, 17-19 October 2019, IEEE © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.relation.journalBioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedingsen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-03-18T14:10:08Z
dspace.date.submission2020-03-18T14:10:15Z
mit.journal.volume2019en_US
mit.licenseOPEN_ACCESS_POLICY


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