Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density
Web of Science
AuthorWu, Q; Smith-Miles, K; Tian, T
Source TitleBMC Bioinformatics
University of Melbourne Author/sSmith-Miles, Kate
AffiliationSchool of Mathematics and Statistics
Document TypeJournal Article
CitationsWu, Q., Smith-Miles, K. & Tian, T. (2014). Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density. BMC BIOINFORMATICS, 15 (12), https://doi.org/10.1186/1471-2105-15-S12-S3.
Access StatusOpen Access
BACKGROUND: Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems. However, one of the major challenges in systems biology is how to infer unknown parameters in mathematical models based on the experimental data sets, in particular, when the data are sparse and the regulatory network is stochastic. RESULTS: To address this issue, this work proposed a new algorithm to estimate parameters in stochastic models using simulated likelihood density in the framework of approximate Bayesian computation. Two stochastic models were used to demonstrate the efficiency and effectiveness of the proposed method. In addition, we designed another algorithm based on a novel objective function to measure the accuracy of stochastic simulations. CONCLUSIONS: Simulation results suggest that the usage of simulated likelihood density improves the accuracy of estimates substantially. When the error is measured at each observation time point individually, the estimated parameters have better accuracy than those obtained by a published method in which the error is measured using simulations over the entire observation time period.
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