A Mathematical Model for Fibroblast Growth Factor Competition Based on Enzyme
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Department of Mathematics, Iowa State University, Carver Hall, Ames, IA 50011
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Iowa State University, Department of Mathematics, 482 Carver Hall Ames, IA 50011
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Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Biology Building Ames, IA 50011
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Received:
01 August 2005
Accepted:
29 June 2018
Published:
01 October 2005
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MSC :
92C45.
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In this paper, we develop a mathematical model for the competition
of two species of fibroblast growth factor, FGF-1 and FGF-2, for the
same cell surface receptor. We provide pathways for this
interaction using experimental data obtained by Neufeld and
Gospodarowicz reported in 1986 [9]. These pathways
demonstrate how the interaction of two fibroblast growth factors
affects cell proliferation. Upon development of these pathways, we
use simulations in MATLAB and optimization to extrapolate the values
of a variety of biochemical parameters imbedded within the model.
Furthermore, it should be possible to use the model as the basis for
a testable hypothesis. We explore this predictive ability with
further simulations in MATLAB.
Citation: Justin P. Peters, Khalid Boushaba, Marit Nilsen-Hamilton. A Mathematical Model for Fibroblast Growth Factor Competition Based on Enzyme[J]. Mathematical Biosciences and Engineering, 2005, 2(4): 789-810. doi: 10.3934/mbe.2005.2.789
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Abstract
In this paper, we develop a mathematical model for the competition
of two species of fibroblast growth factor, FGF-1 and FGF-2, for the
same cell surface receptor. We provide pathways for this
interaction using experimental data obtained by Neufeld and
Gospodarowicz reported in 1986 [9]. These pathways
demonstrate how the interaction of two fibroblast growth factors
affects cell proliferation. Upon development of these pathways, we
use simulations in MATLAB and optimization to extrapolate the values
of a variety of biochemical parameters imbedded within the model.
Furthermore, it should be possible to use the model as the basis for
a testable hypothesis. We explore this predictive ability with
further simulations in MATLAB.
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