Abstract
BackgroundMathematical modeling has achieved a broad interest in the field of biology. These models represent the associations among the metabolism of the biological phenomenon with some mathematical equations such that the observed time course profile of the biological data fits the model. However, the estimation of the unknown parameters of the model is a challenging task. Many algorithms have been developed for parameter estimation, but none of them is entirely capable of finding the best solution. The purpose of this paper is to develop a method for precise estimation of parameters of a biological model.MethodsIn this paper, a novel particle swarm optimization algorithm based on a decomposition technique is developed. Then, its root mean square error is compared with simple particle swarm optimization, Iterative Unscented Kalman Filter and Simulated Annealing algorithms for two different simulation scenarios and a real data set related to the metabolism of CAD system.ResultsOur proposed algorithm results in 54.39% and 26.72% average reduction in root mean square error when applied to the simulation and experimental data, respectively.ConclusionThe results show that the metaheuristic approaches such as the proposed method are very wise choices for finding the solution of nonlinear problems with many unknown parameters.
Highlights
Mathematical modeling has achieved a broad interest in the field of biology
We have demonstrated that the novel PSO algorithm is accomplished to estimate the true parameters and has less RMSE compare with the SA and IUKF and the ordinary PSO algorithm
In this paper, a PSO based algorithm is suggested to estimate the unknown parameters of the nonlinear biological models
Summary
Mathematical modeling has achieved a broad interest in the field of biology These models represent the associations among the metabolism of the biological phenomenon with some mathematical equations such that the observed time course profile of the biological data fits the model. The parameter estimation of a biological model is a crucial step of a system description These models are an approximation of the real phenomenon and some of their parameters do not have physical interpretation and their presence is to compensate the reductions and approximations in the model. The modelling of biochemical pathway is possible with the concurrent measurement of biochemicals This is the result of latest developments in data acquisition technologies which provide us with abundance of time profiles of metabolites or proteins that can be used for mathematical modeling of biochemical networks [1]. The second step is to choose a promising approach to find a global solution for the unknown parameters of the model
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