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Mathematical Modelling of Glioma Stem Cell Fractions After Irradiation Treatments

  • Author / Creator
    Veljee, Wafa S
  • Glioblastoma Multiforme (GBM) is a grade IV brain tumour. It is the most common brain malignancy and is extremely aggressive. Ionizing radiation plays a vital role in the treatment of this tumour. Growth of the GBM is sustained by a subpopulation of the tumour cells often called the glioma stem cells (GSC). Kim et al. and Gao et al. presented in vitro and in silico data respectively where GSC population seemed unnaturally increased. We created four nested ODE models for GBM growth. Parameters were estimated from the available data using the least squares error method and the Akaike Information Criterion was used to choose a suitable model for tumour growth. The aspect of irradiation treatment was incorporated into the glioma growth model using the linear-quadratic model. My analysis on the treatment ODE model supports the findings of Gao et al. that the increased stem cell ratios can only be explained if the stem cell population divides more aggressively after radiotherapy.

  • Subjects / Keywords
  • Graduation date
    2014-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R36D5PK33
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Applied Mathematics
  • Supervisor / co-supervisor and their department(s)
    • Hillen, Thomas (Mathematics and Statistical Sciences)
  • Examining committee members and their departments
    • Hillen, Thomas (Mathematics and Statistical Sciences)
    • Safouhi, Hassan (Mathematics and Statistical Sciences)
    • Wang, Hao (Mathematics and Statistical Sciences)
    • De Vries, Gerda (Mathematics and Statistical Sciences)