Identifying the sources of structural sensitivity in partially specified biological models

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Titel: Identifying the sources of structural sensitivity in partially specified biological models
Autor(en): Adamson, Matthew W.
Morozov, Andrew Yu.
ORCID des Autors: https://orcid.org/0000-0002-8994-2170
Zusammenfassung: Biological systems are characterised by a high degree of uncertainty and complexity, which implies that exact mathematical equations to describe biological processes cannot generally be justified. Moreover, models can exhibit sensitivity to the precise formulations of their component functions—a property known as structural sensitivity. Structural sensitivity can be revealed and quantified by considering partially specified models with uncertain functions, but this goes beyond well-established, parameter-based sensitivity analysis, and currently presents a mathematical challenge. Here we build upon previous work in this direction by addressing the crucial question of identifying the processes which act as the major sources of model uncertainty and those which are less influential. To achieve this goal, we introduce two related concepts: (1) the gradient of structural sensitivity, accounting for errors made in specifying unknown functions, and (2) the partial degree of sensitivity with respect to each function, a global measure of the uncertainty due to possible variation of the given function while the others are kept fixed. We propose an iterative framework of experiments and analysis to inform a heuristic reduction of structural sensitivity in a model. To demonstrate the framework introduced, we investigate the sources of structural sensitivity in a tritrophic food chain model.
Bibliografische Angaben: Adamson, M.W., Morozov, A.Y. Identifying the sources of structural sensitivity in partially specified biological models. Sci Rep 10, 16926 (2020).
URL: https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-202104124252
Schlagworte: Applied mathematics; Biological models; Ecological modelling
Erscheinungsdatum: 9-Okt-2020
Lizenzbezeichnung: Attribution 4.0 International
URL der Lizenz: http://creativecommons.org/licenses/by/4.0/
Publikationstyp: Einzelbeitrag in einer wissenschaftlichen Zeitschrift [article]
Enthalten in den Sammlungen:FB06 - Hochschulschriften
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Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons Creative Commons