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Mathematical Biosciences and Engineering

2005, Volume 2, Issue 4: 789-810. doi: 10.3934/mbe.2005.2.789
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A Mathematical Model for Fibroblast Growth Factor Competition Based on Enzyme

  • 1.
    Department of Mathematics, Iowa State University, Carver Hall, Ames, IA 50011
  • 2.
    Iowa State University, Department of Mathematics, 482 Carver Hall Ames, IA 50011
  • 3.
    Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Biology Building Ames, IA 50011
  • Received: 01 August 2005 Accepted: 29 June 2018 Published: 01 October 2005

  • MSC : 92C45.

  • 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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  • 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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  • © 2005 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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