Introduction to mathematical oncology

You can find material on a graduate course for introduction to mathematical and computational oncology.

The slides of the course that cover an introduction to cancer biology and clinical oncology basic concepts and historical use of mathematical methods to: 1) better understand tumor growth and metastatic dynamics laws and 2) better predict tumor growth, metastatic state and the effect of therapeutic interventions.

A "hands-on" session devoted to modeling experimental data of tumor growth. It covers in part the contents of the publication Classical Mathematical Models for Description and Forecast of Preclinical Tumor Growth. Please cite this paper when using this material.

• The data file (.xlsx)
• Some basic data exploration and the correction (jupyter notebooks here and here, pdf files here and here)
• Investigations of basic tumor growth law through model fitting and the correction (jupyter notebooks here and here, pdf files here and here)

Introduction to modeling, simulation and data science in oncology

Hands-on sessions are jupyter notebooks in python.

• Nonlinear regression. Slides. Hands-on: html or jupyter notebook.
• Population modeling. Slides. Hands-on (Monolix).
• Statistical learning. Slides. Hands-on: html or jupyter notebook.

Population pharmacokinetics modeling using Monolix

You can find here the text of a practical hands-on session on parameter estimation using the excellent software Monolix. It deals with the modeling analysis (within the mixed-effects statistical framework) of pharmacokinetics data of warfarin. See also the warfarin case study video by the Lixoft team, which inspired part of this material. (NB: a Monolix license is required. Monolix is free for academic researchers/students).

Mathematical tools for pharmacometrics

Introduction to linear algebra and linear least-squares regression.

• Slides
• Hands-on in R

Introduction to calculus

• Slides
• Hands-on in R (jupyter notebook).
• Hands-on simulx
  • Slides
  • Material

Introduction to nonlinear regression.

• Slides
• R code for standard errors in linear regression
• Hands-on in R (jupyter notebook)

Evaluation (jupyter notebook).