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Title: The effect of noise on dynamics and the influence of biochemical systems
Author: Erguler, Kamil
ISNI:       0000 0004 2693 8662
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2010
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Understanding a complex system requires integration and collective analysis of data from many levels of organisation. Predictive modelling of biochemical systems is particularly challenging because of the nature of data being plagued by noise operating at each and every level. Inevitably we have to decide whether we can reliably infer the structure and dynamics of biochemical systems from present data. Here we approach this problem from many fronts by analysing the interplay between 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 in surprisingly simple terms; we furthermore demonstrate the advantages of a statistical point of view also for more complex systems. We then investigate strategies for the integrated analysis of models characterised by different organisational levels, and trace the propagation of noise through such systems. We use this approach to uncover, for the first time, the dynamics of metabolic adaptation of 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 a large fraction of current mathematical models of biochemical systems. By using this framework to quantify the effect of parametric variation on system dynamics, we provide practical guidelines as to when and why certain parameters are easily estimated while others are much harder to infer. We highlight the limitations on parameter inference due to model structure and qualitative dynamical behaviour, and identify candidate elements of control in biochemical pathways most likely of being subjected to regulation.
Supervisor: Stumpf, Michael P. H. ; Barahona, Mauricio Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral