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Title: Approximate Bayesian computation for parameter inference and model selection in systems biology
Author: Toni, Tina
ISNI:       0000 0004 2686 6240
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2010
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In this thesis we present a novel algorithm for parameter estimation and model selection of dynamical systems. The algorithm belongs to the class of approximate Bayesian computation (ABC) methods, which can evaluate posterior distributions without having to calculate likelihoods. It is based on a sequential Monte Carlo framework, which gives our method a computational advantage over other existing ABC methods. The algorithm is applied to a wide variety of biological systems such as prokaryotic and eukaryotic signalling and stress response pathways, gene regulatory networks, and infectious diseases. We illustrate its applicability to deterministic and stochastic models, and draw inferences from different data frameworks. Posterior parameter distributions are analysed in order to gain further insight into parameter sensitivity and sloppiness. The comprehensive analysis provided in this thesis illustrates the flexibility of our new ABC SMC approach. The algorithm has proven useful for efficient parameter inference, systematic model selection and inference-based modelling, and is a novel and useful addition to the systems biology toolbox.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available