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dc.contributor.authorEstrada, Javieren_US
dc.contributor.authorAndrew, Natalieen_US
dc.contributor.authorGibson, Danielen_US
dc.contributor.authorChang, Fredericken_US
dc.contributor.authorGnad, Florianen_US
dc.contributor.authorGunawardena, Jeremyen_US
dc.date.accessioned2016-08-09T14:53:23Z
dc.date.issued2016en_US
dc.identifier.citationEstrada, Javier, Natalie Andrew, Daniel Gibson, Frederick Chang, Florian Gnad, and Jeremy Gunawardena. 2016. “Cellular Interrogation: Exploiting Cell-to-Cell Variability to Discriminate Regulatory Mechanisms in Oscillatory Signalling.” PLoS Computational Biology 12 (7): e1004995. doi:10.1371/journal.pcbi.1004995. http://dx.doi.org/10.1371/journal.pcbi.1004995.en
dc.identifier.issn1553-734Xen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:27822281
dc.description.abstractThe molecular complexity within a cell may be seen as an evolutionary response to the external complexity of the cell’s environment. This suggests that the external environment may be harnessed to interrogate the cell’s internal molecular architecture. Cells, however, are not only nonlinear and non-stationary, but also exhibit heterogeneous responses within a clonal, isogenic population. In effect, each cell undertakes its own experiment. Here, we develop a method of cellular interrogation using programmable microfluidic devices which exploits the additional information present in cell-to-cell variation, without requiring model parameters to be fitted to data. We focussed on Ca2+ signalling in response to hormone stimulation, which exhibits oscillatory spiking in many cell types and chose eight models of Ca2+ signalling networks which exhibit similar behaviour in simulation. We developed a nonlinear frequency analysis for non-stationary responses, which could classify models into groups under parameter variation, but found that this question alone was unable to distinguish critical feedback loops. We further developed a nonlinear amplitude analysis and found that the combination of both questions ruled out six of the models as inconsistent with the experimentally-observed dynamics and heterogeneity. The two models that survived the double interrogation were mathematically different but schematically identical and yielded the same unexpected predictions that we confirmed experimentally. Further analysis showed that subtle mathematical details can markedly influence non-stationary responses under parameter variation, emphasising the difficulty of finding a “correct” model. By developing questions for the pathway being studied, and designing more versatile microfluidics, cellular interrogation holds promise as a systematic strategy that can complement direct intervention by genetics or pharmacology.en
dc.language.isoen_USen
dc.publisherPublic Library of Scienceen
dc.relation.isversionofdoi:10.1371/journal.pcbi.1004995en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930170/pdf/en
dash.licenseLAAen_US
dc.subjectBiology and Life Sciencesen
dc.subjectBiochemistryen
dc.subjectNeurochemistryen
dc.subjectNeurotransmittersen
dc.subjectBiogenic Aminesen
dc.subjectHistamineen
dc.subjectNeuroscienceen
dc.subjectPhysical Sciencesen
dc.subjectChemistryen
dc.subjectChemical Compoundsen
dc.subjectOrganic Compoundsen
dc.subjectOrganic Chemistryen
dc.subjectEngineering and Technologyen
dc.subjectFluidicsen
dc.subjectMicrofluidicsen
dc.subjectMathematical and Statistical Techniquesen
dc.subjectMathematical Modelsen
dc.subjectSignal Processingen
dc.subjectSimulation and Modelingen
dc.subjectBiological culturesen
dc.subjectCell linesen
dc.subjectHeLa cellsen
dc.subjectCell culturesen
dc.subjectCultured tumor cellsen
dc.subjectPhysicsen
dc.subjectClassical Mechanicsen
dc.subjectContinuum Mechanicsen
dc.subjectFluid Mechanicsen
dc.subjectFluid Dynamicsen
dc.subjectFluid Flowen
dc.subjectCell Biologyen
dc.subjectSignal Transductionen
dc.subjectCell Signalingen
dc.subjectCalcium Signalingen
dc.titleCellular Interrogation: Exploiting Cell-to-Cell Variability to Discriminate Regulatory Mechanisms in Oscillatory Signallingen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalPLoS Computational Biologyen
dash.depositing.authorChang, Fredericken_US
dc.date.available2016-08-09T14:53:23Z
dc.identifier.doi10.1371/journal.pcbi.1004995*
dash.contributor.affiliatedChang, Frederick S.
dash.contributor.affiliatedGunawardena, Jeremy


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