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Multiscale convergence of the inverse problem for chemotaxis in the Bayesian setting

Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-250216
  • Chemotaxis describes the movement of an organism, such as single or multi-cellular organisms and bacteria, in response to a chemical stimulus. Two widely used models to describe the phenomenon are the celebrated Keller–Segel equation and a chemotaxis kinetic equation. These two equations describe the organism's movement at the macro- and mesoscopic level, respectively, and are asymptotically equivalent in the parabolic regime. The way in which the organism responds to a chemical stimulus is embedded in the diffusion/advection coefficients ofChemotaxis describes the movement of an organism, such as single or multi-cellular organisms and bacteria, in response to a chemical stimulus. Two widely used models to describe the phenomenon are the celebrated Keller–Segel equation and a chemotaxis kinetic equation. These two equations describe the organism's movement at the macro- and mesoscopic level, respectively, and are asymptotically equivalent in the parabolic regime. The way in which the organism responds to a chemical stimulus is embedded in the diffusion/advection coefficients of the Keller–Segel equation or the turning kernel of the chemotaxis kinetic equation. Experiments are conducted to measure the time dynamics of the organisms' population level movement when reacting to certain stimulation. From this, one infers the chemotaxis response, which constitutes an inverse problem. In this paper, we discuss the relation between both the macro- and mesoscopic inverse problems, each of which is associated with two different forward models. The discussion is presented in the Bayesian framework, where the posterior distribution of the turning kernel of the organism population is sought. We prove the asymptotic equivalence of the two posterior distributions.zeige mehrzeige weniger

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Metadaten
Autor(en): Kathrin Hellmuth, Christian Klingenberg, Qin Li, Min Tang
URN:urn:nbn:de:bvb:20-opus-250216
Dokumentart:Artikel / Aufsatz in einer Zeitschrift
Institute der Universität:Fakultät für Mathematik und Informatik / Institut für Mathematik
Sprache der Veröffentlichung:Englisch
Titel des übergeordneten Werkes / der Zeitschrift (Englisch):Computation
ISSN:2079-3197
Erscheinungsjahr:2021
Band / Jahrgang:9
Heft / Ausgabe:11
Aufsatznummer:119
Originalveröffentlichung / Quelle:Computation (2021) 9:11, 119. https://doi.org/10.3390/computation9110119
DOI:https://doi.org/10.3390/computation9110119
Allgemeine fachliche Zuordnung (DDC-Klassifikation):5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Freie Schlagwort(e):Bayesian approach; Keller–Segel model; asymptotic analysis; inverse problems; kinetic chemotaxis equation; mathematical biology; multiscale modeling; velocity jump process
Datum der Freischaltung:08.12.2022
Datum der Erstveröffentlichung:11.11.2021
Lizenz (Deutsch):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International