Abstract
For quantitative cancer models to be meaningful and interpretable the number of unknown parameters must be kept minimal. Experimental data can be utilized to calibrate model dynamics rates or rate constants. Proper integration of experimental data, however, depends on the chosen theoretical framework. Using live imaging of cell proliferation as an example, we show how to derive cell cycle distributions in agent-based models and averaged proliferation rates in differential equation models. We focus on a tumor hierarchy of cancer stem and progenitor non-stem cancer cells.
Footnotes
Research partially supported by the NIH/NCI Integrative Cancer Biology Program 5U54 CA113007.
H. Enderling is with the H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612 USA (phone: 813-745-3562; fax: 813-745-6497; e-mail: heiko.enderling{at}moffitt.org).