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Integrating experimental data to calibrate quantitative cancer models

Heiko Enderling
doi: https://doi.org/10.1101/032102
Heiko Enderling
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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).

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 17, 2015.
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Integrating experimental data to calibrate quantitative cancer models
Heiko Enderling
bioRxiv 032102; doi: https://doi.org/10.1101/032102
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Integrating experimental data to calibrate quantitative cancer models
Heiko Enderling
bioRxiv 032102; doi: https://doi.org/10.1101/032102

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