ArticleBMC bioinformatics2014
Approximate Bayesian computation schemes for parameter inference of discrete stochastic models using simulated likelihood density.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions.PLoS computational biology · 2026Article
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- Mathematical modeling the order of driver gene mutations in colorectal cancer.PLoS computational biology · 2023Article
- BAYESIAN INFERENCE OF STOCHASTIC REACTION NETWORKS USING MULTIFIDELITY SEQUENTIAL TEMPERED MARKOV CHAIN MONTE CARLO.International journal for uncertainty quantification · 2020Article
- Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data.Journal of the Royal Society, Interface · 2019Article
- Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models.The journal of physical chemistry. B · 2019Article
- Reverse-engineering of gene networks for regulating early blood development from single-cell measurements.BMC medical genomics · 2017Article
- Stochastic modeling of biochemical systems with multistep reactions using state-dependent time delay.Scientific reports · 2016Article
- A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.BMC bioinformatics · 2014Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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