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    A dynamical framework for complex fractional killing

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    Author
    Ballweg, Richard
    Paek, Andrew L.
    Zhang, Tongli
    Affiliation
    Univ Arizona, Dept Mol & Cellular Biol
    Issue Date
    2017-08-14
    
    Metadata
    Show full item record
    Publisher
    NATURE PUBLISHING GROUP
    Citation
    A dynamical framework for complex fractional killing 2017, 7 (1) Scientific Reports
    Journal
    Scientific Reports
    Rights
    © The Author(s) 2017. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    When chemotherapy drugs are applied to tumor cells with the same or similar genotypes, some cells are killed, while others survive. This fractional killing contributes to drug resistance in cancer. Through an incoherent feedforward loop, chemotherapy drugs not only activate p53 to induce cell death, but also promote the expression of apoptosis inhibitors which inhibit cell death. Consequently, cells in which p53 is activated early undergo apoptosis while cells in which p53 is activated late survive. The incoherent feedforward loop and the essential role of p53 activation timing makes fractional killing a complex dynamical challenge, which is hard to understand with intuition alone. To better understand this process, we have constructed a representative model by integrating the control of apoptosis with the relevant signaling pathways. After the model was trained to recapture the observed properties of fractional killing, it was analyzed with nonlinear dynamical tools. The analysis suggested a simple dynamical framework for fractional killing, which predicts that cell fate can be altered in three possible ways: alteration of bifurcation geometry, alteration of cell trajectories, or both. These predicted categories can explain existing strategies known to combat fractional killing and facilitate the design of novel strategies.
    ISSN
    2045-2322
    DOI
    10.1038/s41598-017-07422-2
    Version
    Final published version
    Additional Links
    http://www.nature.com/articles/s41598-017-07422-2
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41598-017-07422-2
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