A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana

BMC Bioinformatics

Research

Catherine F Higham¹  Dirk Husmeier¹ 

[1] School of Mathematics and Statistics, College of Science and Engineering, University of Glasgow, G12 8QQ, Glasgow, Scotland, UK;

关键词: Posterior Distribution;    Markov Chain Monte Carlo;    Circadian Clock;    Markov Chain Monte Carlo Chain;    Markov Chain Monte Carlo Technique;   

DOI  :  10.1186/1471-2105-14-S10-S3

来源: Springer

【 摘 要 】

The circadian clock is an important molecular mechanism that enables many organisms to anticipate and adapt to environmental change. Pokhilko et al. recently built a deterministic ODE mathematical model of the plant circadian clock in order to understand the behaviour, mechanisms and properties of the system. The model comprises 30 molecular species (genes, mRNAs and proteins) and over 100 parameters. The parameters have been fitted heuristically to available gene expression time series data and the calibrated model has been shown to reproduce the behaviour of the clock components. Ongoing work is extending the clock model to cover downstream effects, in particular metabolism, necessitating further parameter estimation and model selection. This work investigates the challenges facing a full Bayesian treatment of parameter estimation. Using an efficient adaptive MCMC proposed by Haario et al. and working in a high performance computing setting, we quantify the posterior distribution around the proposed parameter values and explore the basin of attraction. We investigate if Bayesian inference is feasible in this high dimensional setting and thoroughly assess convergence and mixing with different statistical diagnostics, to prevent apparent convergence in some domains masking poor mixing in others.

【 授权许可】

CC BY   
© Higham and Husmeier; licensee BioMed Central Ltd. 2013