A modified particle swarm optimization algorithm for parameter estimation of a biological system

Background 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. However...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Theoretical biology and medical modelling Ročník 15; číslo 1; s. 17
Hlavní autoři: Mosayebi, Raziyeh, Bahrami, Fariba
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 05.11.2018
BioMed Central Ltd
Témata:
Algorithms
Animals
Approximation
Bioinformatics
Biological models (mathematics)
Biology
Biomedical and Life Sciences
Biomedicine
Cellular and Medical Topics
Computer simulation
Decomposition
E coli
Genetic algorithms
Health Sciences
Heuristic methods
Humans
Infectious Diseases
Iterative methods
Kalman filters
Mathematical models
Mean square errors
Medicine
Metabolism
Metabolites
Methods
Models, Biological
Models, Theoretical
Optimization algorithms
Parameter estimation
Parameter modification
Particle swarm optimization
Physiological
Root-mean-square errors
Simulated annealing
Statistics for Life Sciences
Systems Biology
Simulated Annealing
Biological System Modeling
Particle Swarm Optimization
Iterative UKF
ISSN:1742-4682, 1742-4682
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Background 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. 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. Methods In 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. Results Our 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. Conclusion The 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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1742-4682
1742-4682
DOI:10.1186/s12976-018-0089-6