IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003461.html
   My bibliography  Save this article

Mathematical Modeling of the Phoenix Rising Pathway

Author

Listed:
  • Chad Liu
  • Chuan-Yuan Li
  • Fan Yuan

Abstract

Apoptosis is a tightly controlled process in mammalian cells. It is important for embryogenesis, tissue homoeostasis, and cancer treatment. Apoptosis not only induces cell death, but also leads to the release of signals that promote rapid proliferation of surrounding cells through the Phoenix Rising (PR) pathway. To quantitatively understand the kinetics of interactions of different molecules in this pathway, we developed a mathematical model to simulate the effects of various changes in the PR pathway on the secretion of prostaglandin E2 (PGE2), a key factor for promoting cell proliferation. These changes include activation of caspase 3 (C3), caspase 7 (C7), and nuclear factor κB (NFκB). In addition, we simulated the effects of cyclooxygenase-2 (COX2) inhibition and C3 knockout on the level of secreted PGE2. The model predictions on PGE2 in MEF and 4T1 cells at 48 hours after 10-Gray radiation were quantitatively consistent with the experimental data in the literature. Compared to C7, the model predicted that C3 activation was more critical for PGE2 production. The model also predicted that PGE2 production could be significantly reduced when COX2 expression was blocked via either NFκB inactivation or treatment of cells with exogenous COX2 inhibitors, which led to a decrease in the rate of conversion from arachidonic acid to prostaglandin H2 in the PR pathway. In conclusion, the mathematical model developed in this study yielded new insights into the process of tissue regrowth stimulated by signals from apoptotic cells. In future studies, the model can be used for experimental data analysis and assisting development of novel strategies/drugs for improving cancer treatment or normal tissue regeneration.Author Summary: Apoptosis, or programmed cell death, is known to be important for embryogenesis, tissue homoeostasis, and cancer treatment. Furthermore, researchers have recently observed that apoptosis may promote wound healing and tissue regeneration, and accelerate undesired solid tumor regrowth after chemotherapy/radiation therapy. Mechanisms of apoptosis-induced tissue regrowth are related to a molecular network discovered recently in our lab. To quantitatively understand the kinetics of interactions of different molecules in this network, we developed a mathematical model and validated it by comparing the simulation results to experimental data reported in previous studies. To gain new insights into the process of tissue regrowth after inducing apoptosis, we used the model to simulate the effects of radiation on the production of a key growth stimulating factor, PGE2, in apoptotic cells. Additionally, we simulated how PGE2 production could be altered when cells were treated with different inhibitors. We expect that the new mathematical model can be used in future studies to facilitate design of better approaches to cancer treatment or normal tissue regeneration.

Suggested Citation

  • Chad Liu & Chuan-Yuan Li & Fan Yuan, 2014. "Mathematical Modeling of the Phoenix Rising Pathway," PLOS Computational Biology, Public Library of Science, vol. 10(2), pages 1-10, February.
  • Handle: RePEc:plo:pcbi00:1003461
    DOI: 10.1371/journal.pcbi.1003461
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003461
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003461&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003461?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sabrina L. Spencer & Suzanne Gaudet & John G. Albeck & John M. Burke & Peter K. Sorger, 2009. "Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis," Nature, Nature, vol. 459(7245), pages 428-432, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ehrmann, Michael & Talmi, Jonathan, 2020. "Starting from a blank page? Semantic similarity in central bank communication and market volatility," Journal of Monetary Economics, Elsevier, vol. 111(C), pages 48-62.
    2. Xing, Xiaoyun & Xiong, Wanting & Guo, Jinzhong & Wang, Yougui, 2021. "The role of debt in aggregate demand," Finance Research Letters, Elsevier, vol. 39(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kazunari Iwamoto & Yuki Shindo & Koichi Takahashi, 2016. "Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-18, November.
    2. Lucy Ham & Megan A. Coomer & Kaan Öcal & Ramon Grima & Michael P. H. Stumpf, 2024. "A stochastic vs deterministic perspective on the timing of cellular events," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Artémis Llamosi & Andres M Gonzalez-Vargas & Cristian Versari & Eugenio Cinquemani & Giancarlo Ferrari-Trecate & Pascal Hersen & Gregory Batt, 2016. "What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-18, February.
    4. Jan Hasenauer & Christine Hasenauer & Tim Hucho & Fabian J Theis, 2014. "ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics," PLOS Computational Biology, Public Library of Science, vol. 10(7), pages 1-17, July.
    5. Andreas Doncic & Umut Eser & Oguzhan Atay & Jan M Skotheim, 2013. "An Algorithm to Automate Yeast Segmentation and Tracking," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-11, March.
    6. Szymon Stoma & Alexandre Donzé & François Bertaux & Oded Maler & Gregory Batt, 2013. "STL-based Analysis of TRAIL-induced Apoptosis Challenges the Notion of Type I/Type II Cell Line Classification," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-14, May.
    7. Dirke Imig & Nadine Pollak & Frank Allgöwer & Markus Rehm, 2020. "Sample-based modeling reveals bidirectional interplay between cell cycle progression and extrinsic apoptosis," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-17, June.
    8. Christopher C Govern & Arup K Chakraborty, 2013. "Stochastic Responses May Allow Genetically Diverse Cell Populations to Optimize Performance with Simpler Signaling Networks," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.
    9. Miles Miller & Marc Hafner & Eduardo Sontag & Noah Davidsohn & Sairam Subramanian & Priscilla E M Purnick & Douglas Lauffenburger & Ron Weiss, 2012. "Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-18, July.
    10. Subhadip Raychaudhuri, 2010. "A Minimal Model of Signaling Network Elucidates Cell-to-Cell Stochastic Variability in Apoptosis," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-7, August.
    11. Leighton T Izu & Tamás Bányász & Ye Chen-Izu, 2015. "Optimizing Population Variability to Maximize Benefit," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-17, December.
    12. Suzanne Gaudet & Sabrina L Spencer & William W Chen & Peter K Sorger, 2012. "Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor-Mediated Apoptosis," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-15, April.
    13. Andreas Raue & Marcel Schilling & Julie Bachmann & Andrew Matteson & Max Schelke & Daniel Kaschek & Sabine Hug & Clemens Kreutz & Brian D Harms & Fabian J Theis & Ursula Klingmüller & Jens Timmer, 2013. "Lessons Learned from Quantitative Dynamical Modeling in Systems Biology," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-17, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1003461. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.