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Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods.

Calderhead, B; Girolami, M; (2011) Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods. Interface Focus , 1 (6) 821 - 835. 10.1098/rsfs.2011.0051.

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Abstract

Mechanistic models based on systems of nonlinear differential equations can help provide a quantitative understanding of complex physical or biological phenomena. The use of such models to describe nonlinear interactions in molecular biology has a long history; however, it is only recently that advances in computing have allowed these models to be set within a statistical framework, further increasing their usefulness and binding modelling and experimental approaches more tightly together. A probabilistic approach to modelling allows us to quantify uncertainty in both the model parameters and the model predictions, as well as in the model hypotheses themselves. In this paper, the Bayesian approach to statistical inference is adopted and we examine the significant challenges that arise when performing inference over nonlinear ordinary differential equation models describing cell signalling pathways and enzymatic circadian control; in particular, we address the difficulties arising owing to strong nonlinear correlation structures, high dimensionality and non-identifiability of parameters. We demonstrate how recently introduced differential geometric Markov chain Monte Carlo methodology alleviates many of these issues by making proposals based on local sensitivity information, which ultimately allows us to perform effective statistical analysis. Along the way, we highlight the deep link between the sensitivity analysis of such dynamic system models and the underlying Riemannian geometry of the induced posterior probability distributions.

Type:Article
Title:Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods.
Location:England
DOI:10.1098/rsfs.2011.0051
Language:English
Additional information:PMCID: PMC3262297
Keywords:Bayesian analysis, Markov chain Monte Carlo, Riemann manifold sampling methods, nonlinear dynamic systems, parameter estimation, statistical inference
UCL classification:UCL > School of BEAMS > Faculty of Maths and Physical Sciences > CoMPLEX - Maths and Physics in the Life Sciences and Experimental Biology
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science

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