Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density

BMC Bioinformatics

Research

Kate Smith-Miles¹  Tianhai Tian¹  Qianqian Wu¹ 

[1] School of Mathematical Sciences, Monash University, Melbourne, Australia;

关键词: Approximate Bayesian Computation;    Discrepancy Tolerance;    Sequential Monte Carlo;    Stochastic Simulation Algorithm;    Bayesian Inference Method;   

DOI  :  10.1186/1471-2105-15-S12-S3

来源: Springer

【 摘 要 】

BackgroundMathematical 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.ResultsTo 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.ConclusionsSimulation 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.

【 授权许可】

Unknown   
© Wu et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.