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Author: search.filters.author.Modchang, CharinSubject: search.filters.subject.Optimization

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    Estimation of algae growth model parameters by a double layer genetic algorithm
    (2012-11-01)
    Nokkaew, Artorn
    ;
    Pimpunchat, Busayamas
    ;
    Modchang, Charin
    ;
    Amornsamankul, Somkid
    ;
    Triampo, Wannapong
    This paper presents a double layer genetic algorithm (DLGA) to improve performance of the information-constrained parameter estimations. When a simple genetic algorithm (SGA) fails, a DLGA is applied to the optimization problem in which the initial condition is missing. In this study, a DLGA is specifically designed. The two layers of the SGA serve different purposes. The two optimizations are applied separately but sequentially. The first layer determines the average value of a state variable as its derivative is zero. The knowledge from the first layer is utilized to guide search in the second layer. The second layer uses the obtained average to optimize model parameters. To construct a fitness function for the second layer, the relative derivative function of the average is combined into the fitness function of the ordinary least square problem as a value control. The result shows that the DLGA has better performance. When missing an initial condition, the DLGA provides more consistent numerical values for model parameters. Also, simulation produced by DLGA is more reasonable values than those produced by the SGA.
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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.