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dc.contributor.authorBruno, Simone
dc.contributor.authorAl-Radhawi, M. Ali
dc.contributor.authorSontag, Eduardo D.
dc.contributor.authorDel Vecchio, Domitilla
dc.date.accessioned2021-11-09T16:22:39Z
dc.date.available2021-11-09T16:22:39Z
dc.date.issued2019-07
dc.identifier.urihttps://hdl.handle.net/1721.1/137959
dc.description.abstract© 2019 American Automatic Control Council. Cellular reprogramming is traditionally accomplished through an open loop (OL) control approach, wherein key transcription factors (TFs) are injected in cells to steer the state of the pluripotency (PL) gene regulatory network (GRN), as encoded by TFs concentrations, to the pluripotent state. Due to the OL nature of this approach, the concentration of TFs cannot be accurately controlled. Recently, a closed loop (CL) feedback control strategy was proposed to overcome this problem with promising theoretical results. However, previous analyses of the controller were based on deterministic models. It is well known that cellular systems are characterized by substantial stochasticity, especially when molecules are in low copy number as it is the case in reprogramming problems wherein the gene copy number is usually one or two. Hence, in this paper, we analyze the Chemical Master Equation (CME) for the reaction model of the PL GRN with and without the feedback controller. We computationally and analytically investigate the performance of the controller in biologically relevant parameter regimes where stochastic effects dictate system dynamics. Our results indicate that the feedback control approach still ensures reprogramming even when both the PL GRN and the controller are stochastic.en_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionof10.23919/acc.2019.8814355en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleStochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory networken_US
dc.typeArticleen_US
dc.identifier.citationBruno, Simone, Al-Radhawi, M. Ali, Sontag, Eduardo D. and Del Vecchio, Domitilla. 2019. "Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network." Proceedings of the American Control Conference, 2019-July.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.relation.journalProceedings of the American Control Conferenceen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-07-08T14:55:37Z
dspace.date.submission2020-07-08T14:55:40Z
mit.journal.volume2019-Julyen_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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