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Dynamic rendering of the heterogeneous cell response to anticancer treatments.

Authors :
Francesca Falcetta
Monica Lupi
Valentina Colombo
Paolo Ubezio
Source :
PLoS Computational Biology, Vol 9, Iss 10, p e1003293 (2013)
Publication Year :
2013
Publisher :
Public Library of Science (PLoS), 2013.

Abstract

The antiproliferative response to anticancer treatment is the result of concurrent responses in all cell cycle phases, extending over several cell generations, whose complexity is not captured by current methods. In the proposed experimental/computational approach, the contemporary use of time-lapse live cell microscopy and flow cytometric data supported the computer rendering of the proliferative process through the cell cycle and subsequent generations during/after treatment. The effects of treatments were modelled with modules describing the functional activity of the main pathways causing arrest, repair and cell death in each phase. A framework modelling environment was created, enabling us to apply different types of modules in each phase and test models at the complexity level justified by the available data. We challenged the method with time-course measures taken in parallel with flow cytometry and time-lapse live cell microscopy in X-ray-treated human ovarian cancer cells, spanning a wide range of doses. The most suitable model of the treatment, including the dose-response of each effect, was progressively built, combining modules with a rational strategy and fitting simultaneously all data of different doses and platforms. The final model gave for the first time the complete rendering in silico of the cycling process following X-ray exposure, providing separate and quantitative measures of the dose-dependence of G1, S and G2M checkpoint activities in subsequent generations, reconciling known effects of ionizing radiations and new insights in a unique scenario.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X and 15537358
Volume :
9
Issue :
10
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.46a8065e03ef4c0fba78f30d004bfa46
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1003293