Parameter Estimation Using Improved Differential Evolution And Bacterial Foraging Algorithms To Model Tyrosine Production In Mus Musculus(Mouse)
DOI:
https://doi.org/10.11113/jt.v72.1778Keywords:
Parameter estimation, differential evolution algorithm, bacterial foraging algorithm, kalman filtering algorithm, modelling, metabolic engineering, bioinformatics, artificial intelligenceAbstract
The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimisation method implemented to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in Musmusculus (mouse) by using a dataset, the JAK/STAT(Janus Kinase Signal Transducer and Activator of Transcription) signal transduction pathway. Global optimisation is a method to identify the optimal kinetic parameter in ordinary differential equation. From the ordinary parameter of biomathematical field, there are many unknown parameters, and commonly, the parameter is in nonlinear form. Global optimisation method includes differential evolution algorithm, which will be used in this research. Kalman Filter and Bacterial Foraging algorithm helps in handling noise data and convergences faster respectively in the conventional Differential Evolution. The results from this experiment show estimated optimal kinetic parameters values, shorter computation time, and better accuracy of simulated results compared with other estimation algorithms.
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