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

• Published in: BMC bioinformatics 2013-08, Vol.14 Suppl 10 (Suppl 10), p.S3-S3, Article S3
• Main Authors: Higham, Catherine F, Husmeier, Dirk
• Format: Article
• Language: English
• Subjects: Arabidopsis - genetics; Arabidopsis - metabolism; Arabidopsis Proteins - antagonists & inhibitors; Arabidopsis Proteins - genetics; Arabidopsis Proteins - metabolism; Arabidopsis thaliana; Bayes Theorem; Bayesian analysis; Circadian Clocks - genetics; Circadian rhythm; DNA-Binding Proteins - antagonists & inhibitors; DNA-Binding Proteins - genetics; Gene Expression Regulation, Plant; Parameter estimation; Promoter Regions, Genetic; Protein Binding - genetics; Protein Processing, Post-Translational - genetics; Repressor Proteins - genetics; Repressor Proteins - metabolism; Studies; Transcription Factors - antagonists & inhibitors; Transcription Factors - genetics; Transcription Factors - metabolism
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-14-S10-S3

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.