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Author: search.filters.author.Modchang, CharinSubject: search.filters.subject.Genetic algorithm

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    Approximate solutions and parameter estimations of G-protein coupled receptor signal transduction model
    (2013-02-20)
    Modchang, Charin
    ;
    Pimpunchat, Busayamas
    ;
    Lenbury, Yongwimon
    ;
    Triampo, Wannapong
    We find approximate analytical solutions of a model for the signal transduction mediated by the G-protein coupled receptor proposed earlier by Modchang et al. The time evolution of the approximate solutions will be explored and compared with numerical solutions of the model. Moreover, an alternative procedure for estimating unknown parameters in the model has been proposed. This alternative parameter estimation method directly uses the exact equilibrium solutions for fitting with the experimental data. We found that this new parameter estimation method can estimate unknown parameter values faster than the method used in previous works. © 2013 Pushpa Publishing House.
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    Modeling and optimization of G-protein coupled receptor signal transduction
    (2011-06-01)
    Modchang, Charin
    ;
    Pimpunchat, Busayamas
    ;
    Triampo, Wannapong
    ;
    Triampo, Darapond
    ;
    Lenbury, Yongwimon
    Signal transduction is the process by which a cell converts one kind of signal or stimulus into another. In this process, G-protein coupled receptors (GPCRs) are considered a major class of membrane protein receptors. GPCRs play a critical role in signal transduction, and they are important pharmacological drug targets. Motivated by some specific experimental data, we construct a mathematical model to investigate a signaling system of interest. The model is composed of mass-action ordinary differential equations that describe ligand-receptor and receptor- G-protein interactions. Because the kinetic reaction rates in the signaling processes previously gathered in reliable in vivo and in vitro experiments are limited to a small number of known values, we apply a genetic algorithm (GA) to estimate the parameter values in our model. In order to carry out the parameter estimation, we use the Augmented Lagrangian Genetic Algorithm (ALGA) with help from the mathematical theorem of infinite norm. This method ensures a faster parameter estimation speed in the modeled system. In addition, mathematical analyses are also performed. Some good agreement between analytic, numerical and experimental data was found. The simulation results of the model are extensively discussed and compared with the experimental data. © 2011 Pushpa Publishing House.