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dc.contributor.authorPurcell, Oliver
dc.contributor.authorJain, Bonny
dc.contributor.authorKarr, Jonathan R.
dc.contributor.authorCovert, Markus W.
dc.contributor.authorLu, Timothy K.
dc.date.accessioned2016-01-20T18:50:50Z
dc.date.available2016-01-20T18:50:50Z
dc.date.issued2013-06
dc.date.submitted2013-01
dc.identifier.issn10541500
dc.identifier.issn1089-7682
dc.identifier.urihttp://hdl.handle.net/1721.1/100960
dc.description.abstractDespite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.en_US
dc.description.sponsorshipMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Advanced Undergraduate Research Program)en_US
dc.description.sponsorshipUnited States. Defense Advanced Research Projects Agencyen_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (New Innovator Award 1DP2OD008435)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (1124247)en_US
dc.language.isoen_US
dc.publisherAmerican Institute of Physics (AIP)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1063/1.4811182en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceMIT web domainen_US
dc.titleTowards a whole-cell modeling approach for synthetic biologyen_US
dc.typeArticleen_US
dc.identifier.citationPurcell, Oliver, Bonny Jain, Jonathan R. Karr, Markus W. Covert, and Timothy K. Lu. “Towards a Whole-Cell Modeling Approach for Synthetic Biology.” Chaos: An Interdisciplinary Journal of Nonlinear Science 23, no. 2 (2013): 025112.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronicsen_US
dc.contributor.mitauthorPurcell, Oliveren_US
dc.contributor.mitauthorJain, Bonnyen_US
dc.contributor.mitauthorLu, Timothy K.en_US
dc.relation.journalChaos: An Interdisciplinary Journal of Nonlinear Scienceen_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
dspace.orderedauthorsPurcell, Oliver; Jain, Bonny; Karr, Jonathan R.; Covert, Markus W.; Lu, Timothy K.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-9999-6690
dc.identifier.orcidhttps://orcid.org/0000-0002-2031-8871
mit.licensePUBLISHER_POLICYen_US


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