Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11969
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dc.contributor.authorPasemann, Gregor-
dc.contributor.authorFlemming, Sven-
dc.contributor.authorAlonso, Sergio-
dc.contributor.authorBeta, Carsten-
dc.contributor.authorStannat, Wilhelm-
dc.date.accessioned2021-05-31T13:12:26Z-
dc.date.available2021-05-31T13:12:26Z-
dc.date.issued2021-05-03-
dc.identifier.issn0938-8974-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/13175-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-11969-
dc.description.abstractA theory for diffusivity estimation for spatially extended activator–inhibitor dynamics modeling the evolution of intracellular signaling networks is developed in the mathematical framework of stochastic reaction–diffusion systems. In order to account for model uncertainties, we extend the results for parameter estimation for semilinear stochastic partial differential equations, as developed in Pasemann and Stannat (Electron J Stat 14(1):547–579, 2020), to the problem of joint estimation of diffusivity and parametrized reaction terms. Our theoretical findings are applied to the estimation of effective diffusivity of signaling components contributing to intracellular dynamics of the actin cytoskeleton in the model organism Dictyostelium discoideum.en
dc.description.sponsorshipDFG, 318763901, SFB 1294: Datenassimilation: Die nahtlose Verschmelzung von Daten und Modellenen
dc.description.sponsorshipTU Berlin, Open-Access-Mittel – 2021en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc510 Mathematikde
dc.subject.otherparametric drift estimationen
dc.subject.otherstochastic reaction–diffusion systemsen
dc.subject.othermaximum likelihood estimationen
dc.subject.otheractin cytoskeleton dynamicsen
dc.titleDiffusivity estimation for activator–inhibitor models: Theory and application to intracellular dynamics of the actin cytoskeletonen
dc.typeArticleen
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn1432-1467-
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1007/s00332-021-09714-4en
dcterms.bibliographicCitation.journaltitleJournal of Nonlinear Scienceen
dcterms.bibliographicCitation.originalpublisherplaceHeidelbergen
dcterms.bibliographicCitation.volume31en
dcterms.bibliographicCitation.originalpublishernameSpringer Natureen
dcterms.bibliographicCitation.articlenumber59en
Appears in Collections:FG Mathematische Stochastik / Stochastische Prozesse in den Neurowissenschaften » Publications

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