Bayesian inference for differential equations
Theoretical Computer Science
A Model Checking Approach to the Parameter Estimation of Biochemical Pathways
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
A new probabilistic generative model of parameter inference in biochemical networks
Proceedings of the 2009 ACM symposium on Applied Computing
Estimating Hidden Influences in Metabolic and Gene Regulatory Networks
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Estimating Bayes factors via thermodynamic integration and population MCMC
Computational Statistics & Data Analysis
Target driven biochemical network reconstruction based on petri nets and simulated annealing
Proceedings of the 8th International Conference on Computational Methods in Systems Biology
A hybrid approach to piecewise modelling of biochemical systems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Motivation: There often are many alternative models of a biochemical system. Distinguishing models and finding the most suitable ones is an important challenge in Systems Biology, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Bayes factors are employed as a measure of evidential preference for one model over another. Marginal likelihood is a key component of Bayes factors, however computing the marginal likelihood is a difficult problem, as it involves integration of nonlinear functions in multidimensional space. There are a number of methods available to compute the marginal likelihood approximately. A detailed investigation of such methods is required to find ones that perform appropriately for biochemical modelling. Results: We assess four methods for estimation of the marginal likelihoods required for computing Bayes factors. The Prior Arithmetic Mean estimator, the Posterior Harmonic Mean estimator, the Annealed Importance Sampling and the Annealing-Melting Integration methods are investigated and compared on a typical case study in Systems Biology. This allows us to understand the stability of the analysis results and make reliable judgements in uncertain context. We investigate the variance of Bayes factor estimates, and highlight the stability of the Annealed Importance Sampling and the Annealing-Melting Integration methods for the purposes of comparing nonlinear models. Availability: Models used in this study are available in SBML format as the supplementary material to this article. Contact: vvv@dcs.gla.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.