Holistic View on Cell Survival and DNA Damage: How Model-Based Data Analysis Supports Exploration of Dynamics in Biological Systems

In this work, a method is established to calibrate a model that describes the basic dynamics of DNA damage and repair. The model can be used to extend planning for radiotherapy and hyperthermia in order to include the biological effects. In contrast to “syntactic” models (e.g., describing molecular...

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Published in:Computational and mathematical methods in medicine 2020, Vol.2020 (2020), p.1-11
Main Authors: Scheidegger, Stephan, Füchslin, Rudolf M., Petri-Fink, Alke, Ulzega, Simone, Rohrer Bley, Carla, Nytko, Katarzyna J., Thumser-Henner, Pauline, Weyland, Mathias S., Lattuada, Marco
Format: Article
Language:English
Subjects:
Animals
Bayes Theorem
Cell Line, Tumor
Cell Survival - radiation effects
Comet Assay
Computational Biology
DNA Damage
DNA Repair
Dogs
Humans
Mathematical Concepts
Models, Biological
Monte Carlo Method
Neoplasms - metabolism
Neoplasms - pathology
Neoplasms - radiotherapy
Radiotherapy Planning, Computer-Assisted
Systems Biology
Tumor Stem Cell Assay
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Summary:In this work, a method is established to calibrate a model that describes the basic dynamics of DNA damage and repair. The model can be used to extend planning for radiotherapy and hyperthermia in order to include the biological effects. In contrast to “syntactic” models (e.g., describing molecular kinetics), the model used here describes radiobiological semantics, resulting in a more powerful model but also in a far more challenging calibration. Model calibration is attempted from clonogenic assay data (doses of 0–6 Gy) and from time-resolved comet assay data obtained within 6 h after irradiation with 6 Gy. It is demonstrated that either of those two sources of information alone is insufficient for successful model calibration, and that both sources of information combined in a holistic approach are necessary to find viable model parameters. Approximate Bayesian computation (ABC) with simulated annealing is used for parameter search, revealing two aspects that are beneficial to resolving the calibration problem: (1) assessing posterior parameter distributions instead of point-estimates and (2) combining calibration runs from different assays by joining posterior distributions instead of running a single calibration run with a combined, computationally very expensive objective function.
ISSN:1748-670X
1748-6718
DOI:10.1155/2020/5972594