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.
Highlights
Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems
Algorithm 1 is in the framework of approximate Bayesian computation (ABC) sequential Monte Carlo (SMC) and uses transitional density based on the simulations over two consecutive observation time points
Using two chemical reaction systems as the test problems, we examined the accuracy and efficiency of proposed new algorithms
Summary
Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems. 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. We need to infer the model parameters according to experimental data. The likelihood surfaces of large models are complex The calibration of these unknown parameters within a model structure is one of the key issues in systems biology [2].
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