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dc.contributor.authorErguler, Kamilen
dc.creatorErguler, Kamilen
dc.publisherImperial College Londonen
dc.titleThe effect of noise on dynamics and the influence of biochemical systems [electronic resource]en
dc.typeinfo:eu-repo/semantics/doctoralThesisΣχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Βιολογικών Επιστημών / Department of Biological Sciences
dc.type.uhtypeDoctoral Thesisen
dc.description.notes<p>ID: 1182en
dc.description.notesCataloging Source: Uk eng Uk Uken
dc.description.notesDewey DC Class. No.: 572.8 15en
dc.description.notesBibliography Note: Includes bibliographical references.en
dc.description.notesSummary Note: Understanding a complex system requires integration and collective analysis of data from manylevels of organisation. Predictive modelling of biochemical systems is particularly challengingbecause of the nature of data being plagued by noise operating at each and every level. Inevitablywe have to decide whether we can reliably infer the structure and dynamics of biochemical systemsfrom present data. Here we approach this problem from many fronts by analysing the interplaybetween deterministic and stochastic dynamics in a broad collection of biochemical models. In a classical mathematical model we first illustrate how this interplay can be described insurprisingly simple termsen
dc.description.noteswe furthermore demonstrate the advantages of a statistical point of viewalso for more complex systems. We then investigate strategies for the integrated analysis of modelscharacterised by different organisational levels, and trace the propagation of noise through suchsystems. We use this approach to uncover, for the first time, the dynamics of metabolic adaptationof a plant pathogen throughout its life cycle and discuss the ecological implications. Finally, we investigate how reliably we can infer model parameters of biochemical models. We develop a novel sensitivity/inferability analysis framework that is generally applicable to alarge fraction of current mathematical models of biochemical systems. By using this framework toquantify the effect of parametric variation on system dynamics, we provide practical guidelines asto when and why certain parameters are easily estimated while others are much harder to infer. Wehighlight the limitations on parameter inference due to model structure and qualitative dynamicalbehaviour, and identify candidate elements of control in biochemical pathways most likely of beingsubjected to regulation.en
dc.description.notesSystem Details Note: Mode of access: World Wide Web.en
dc.description.notesSource: EThOS ethos_marc_111007.mrc</p>en

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