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dc.contributor.authorRastogi, Ananya
dc.contributor.authorRobert, Philippe A
dc.contributor.authorHalle, Stephan
dc.contributor.authorMeyer-Hermann, Michael
dc.date.accessioned2021-04-06T11:29:54Z
dc.date.available2021-04-06T11:29:54Z
dc.date.issued2020-12-28
dc.identifier.citationPLoS Comput Biol. 2020 Dec 28;16(12):e1008428. doi: 10.1371/journal.pcbi.1008428.en_US
dc.identifier.pmid33370254
dc.identifier.doi10.1371/journal.pcbi.1008428
dc.identifier.urihttp://hdl.handle.net/10033/622821
dc.description.abstractIn vivo imaging of cytotoxic T lymphocyte (CTL) killing activity revealed that infected cells have a higher observed probability of dying after multiple contacts with CTLs. We developed a three-dimensional agent-based model to discriminate different hypotheses about how infected cells get killed based on quantitative 2-photon in vivo observations. We compared a constant CTL killing probability with mechanisms of signal integration in CTL or infected cells. The most likely scenario implied increased susceptibility of infected cells with increasing number of CTL contacts where the total number of contacts was a critical factor. However, when allowing in silico T cells to initiate new interactions with apoptotic target cells (zombie contacts), a contact history independent killing mechanism was also in agreement with experimental datasets. The comparison of observed datasets to simulation results, revealed limitations in interpreting 2-photon data, and provided readouts to distinguish CTL killing models.en_US
dc.language.isoenen_US
dc.publisherPLOSen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleEvaluation of CD8 T cell killing models with computer simulations of 2-photon imaging experiments.en_US
dc.typeArticleen_US
dc.identifier.eissn1553-7358
dc.contributor.departmentBRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany.en_US
dc.identifier.journalPLoS computational biologyen_US
dc.source.volume16
dc.source.issue12
dc.source.beginpagee1008428
dc.source.endpage
refterms.dateFOA2021-04-06T11:29:54Z
dc.source.journaltitlePLoS computational biology
dc.source.countryUnited States


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Attribution 4.0 International
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