Numerical Modelling of the human menstrual cycle applied to ovulation prediction

Please always quote using this URN: urn:nbn:de:0297-zib-46168
  • In this Master Thesis, the modelling process and the required biological background to adequately describe the hormonal dynamics of the human menstrual cycle are presented. The aim of this Master thesis project is to establish a mathematical routine that enables prediction of potentially fertile time windows. A system of four parameter dependent ordinary differential equations coupled with two algebraic equations is built, accounting for the main kinetic drivers of the human menstrual cycle { basal body temperature, estradiol, progesterone, luteinizing and follicle stimulating hormone. The key parameters are identifed using a nonlinear least squares approach to match the model to individual data. Aiming to solve the optimality problem arising, the Newton and Gauss-Newton method are examined and their local convergence theory is outlined. By the nature of the application, a globalised Gauss-Newton method seems more convenient. An adaptive trust region approach to determine the step lengths is chosen, which together with an linearly-implicit Euler extrapolation for the given differential algebraic equation system makes parameter estimation possible. The simulation results are discussed particularly in view of limitations and problems arising through the modelling approach.

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Author:Jo Andrea Brüggemann
Document Type:Master's Thesis
Granting Institution:Universität Trier
Advisor:Susanna Röblitz
Year of first publication:2013