Publication: Modeling and genetic algorithm optimization of early events in signal transduction via dynamics of G-protein-coupled receptors: Internalization consideration
Issued Date
2009-01-15
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ISSN
00963003
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2-s2.0-58749091804
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Mahidol University
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SCOPUS
Bibliographic Citation
Applied Mathematics and Computation. Vol.207, No.2 (2009), 528-544
Suggested Citation
Jeerapond Leelawattanachai, Charin Modchang, Wannapong Triampo, Darapond Triampo, Yongwimon Lenbury Modeling and genetic algorithm optimization of early events in signal transduction via dynamics of G-protein-coupled receptors: Internalization consideration. Applied Mathematics and Computation. Vol.207, No.2 (2009), 528-544. doi:10.1016/j.amc.2008.11.008 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/27785
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Title
Modeling and genetic algorithm optimization of early events in signal transduction via dynamics of G-protein-coupled receptors: Internalization consideration
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Abstract
Signal transduction is the process of signal conversion that cells use to communicate among themselves and their environments. In this process, a cell converts one kind of signal or stimulus into another. This cellular communication brings about many cellular activities in response to the signals. Therefore, in-depth knowledge and understanding of this process, especially concerning the roles of G-proteins and cell receptors, which are the important components of the signal transduction process, could greatly benefit medical science, particularly in terms of medical diagnosis and treatment. In this research we study early events in signal transduction including receptor/ligand binding and G-protein activation using an ordinary differential equation model. Motivated by experimental data and the mathematical model proposed by Chen et al. [C.Y. Chen et al., Modelling of signalling via G-protein coupled receptors: pathway-dependent agonist potency and efficacy, Bull. Math. Biol. 65 (2003) 933-958] to explain the agonist potency and efficacy of drugs regulated by signaling dynamics via G-proteins and receptors, we extended their model to take into account internalization, recycling, degradation and synthesis of the receptors in this process to obtain a more realistic model. By analyzing the extended model, we have found that the numerical results agree well with experimental observation. Qualitatively, this modified model is shown to be more realistic than the previous one in some respects when certain experimental findings are considered. © 2008 Elsevier Inc. All rights reserved.