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Status: Bibliographieeintrag

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Verfasst von:Gabel, Michael [VerfasserIn]   i
 Hohl, Tobias [VerfasserIn]   i
 Imle, Andrea [VerfasserIn]   i
 Fackler, Oliver Till [VerfasserIn]   i
 Graw, Frederik [VerfasserIn]   i
Titel:FAMoS
Titelzusatz:a flexible and dynamic algorithm for model selection to analyse complex systems dynamics
Verf.angabe:Michael Gabel, Tobias Hohl, Andrea Imle, Oliver T. Fackler, Frederik Graw
E-Jahr:2019
Jahr:August 16, 2019
Umfang:23 S.
Fussnoten:Gesehen am 10.12.2019
Titel Quelle:Enthalten in: Public Library of SciencePLoS Computational Biology
Ort Quelle:San Francisco, Calif. : Public Library of Science, 2005
Jahr Quelle:2019
Band/Heft Quelle:15(2019,8) Artikel-Nummer e1007230, 23 Seiten
ISSN Quelle:1553-7358
Abstract:Most biological systems are difficult to analyse due to a multitude of interacting components and the concomitant lack of information about the essential dynamics. Finding appropriate models that provide a systematic description of such biological systems and that help to identify their relevant factors and processes can be challenging given the sheer number of possibilities. Model selection algorithms that evaluate the performance of a multitude of different models against experimental data provide a useful tool to identify appropriate model structures. However, many algorithms addressing the analysis of complex dynamical systems, as they are often used in biology, compare a preselected number of models or rely on exhaustive searches of the total model space which might be unfeasible dependent on the number of possibilities. Therefore, we developed an algorithm that is able to perform model selection on complex systems and searches large model spaces in a dynamical way. Our algorithm includes local and newly developed non-local search methods that can prevent the algorithm from ending up in local minima of the model space by accounting for structurally similar processes. We tested and validated the algorithm based on simulated data and showed its flexibility for handling different model structures. We also used the algorithm to analyse experimental data on the cell proliferation dynamics of CD4+ and CD8+ T cells that were cultured under different conditions. Our analyses indicated dynamical changes within the proliferation potential of cells that was reduced within tissue-like 3D ex vivo cultures compared to suspension. Due to the flexibility in handling various model structures, the algorithm is applicable to a large variety of different biological problems and represents a useful tool for the data-oriented evaluation of complex model spaces.
DOI:doi:10.1371/journal.pcbi.1007230
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1371/journal.pcbi.1007230
 Verlag: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007230
 DOI: https://doi.org/10.1371/journal.pcbi.1007230
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:T cells
 Algorithms
 Cell cycle and cell division
 Cell death
 Cell proliferation
 Collagens
 Cytotoxic T cells
 Dynamical systems
K10plus-PPN:1684980208
Verknüpfungen:→ Zeitschrift

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