Nested sampling for parameter inference in systems biology: application to an exemplar circadian model

BMC Systems Biology

Ozgur E Akman²  Stuart Aitken¹ 

[1] MRC Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh EH4 2XU, UK;Centre for Systems, Dynamics and Control, College of Engineering, Mathematics & Physical Sciences, University of Exeter, Exeter EX4 4QF, UK

关键词: Circadian rhythms;    Nested sampling;    Parameter inference;    Model selection;   

Others  :  1142467
DOI  :  10.1186/1752-0509-7-72

 received in 2012-10-23, accepted in 2013-07-29,  发布年份 2013

【 摘 要 】

Background

Model selection and parameter inference are complex problems that have yet to be fully addressed in systems biology. In contrast with parameter optimisation, parameter inference computes both the parameter means and their standard deviations (or full posterior distributions), thus yielding important information on the extent to which the data and the model topology constrain the inferred parameter values.

Results

We report on the application of nested sampling, a statistical approach to computing the Bayesian evidence Z, to the inference of parameters, and the estimation of log Z in an established model of circadian rhythms. A ten-fold difference in the coefficient of variation between degradation and transcription parameters is demonstrated. We further show that the uncertainty remaining in the parameter values is reduced by the analysis of increasing numbers of circadian cycles of data, up to 4 cycles, but is unaffected by sampling the data more frequently. Novel algorithms for calculating the likelihood of a model, and a characterisation of the performance of the nested sampling algorithm are also reported. The methods we develop considerably improve the computational efficiency of the likelihood calculation, and of the exploratory step within nested sampling.

Conclusions

We have demonstrated in an exemplar circadian model that the estimates of posterior parameter densities (as summarised by parameter means and standard deviations) are influenced predominately by the length of the time series, becoming more narrowly constrained as the number of circadian cycles considered increases. We have also shown the utility of the coefficient of variation for discriminating between highly-constrained and less-well constrained parameters.

【 授权许可】

   
2013 Aitken and Akman; licensee BioMed Central Ltd.

【 图 表 】

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