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Surfing the p53 network

Author

Listed:
  • Bert Vogelstein

    (The Howard Hughes Medical Institute and Johns Hopkins Oncology Center)

  • David Lane

    (Ninewells Hospital, University of Dundee)

  • Arnold J. Levine

    (Laboratory of Cancer Biology, Genetics, and Molecular Biophysics, Rockefeller University)

Abstract

The p53 tumour-suppressor gene integrates numerous signals that control cell life and death. As when a highly connected node in the Internet breaks down, the disruption of p53 has severe consequences.

Suggested Citation

  • Bert Vogelstein & David Lane & Arnold J. Levine, 2000. "Surfing the p53 network," Nature, Nature, vol. 408(6810), pages 307-310, November.
  • Handle: RePEc:nat:nature:v:408:y:2000:i:6810:d:10.1038_35042675
    DOI: 10.1038/35042675
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    Citations

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    Cited by:

    1. Bálint Mészáros & István Simon & Zsuzsanna Dosztányi, 2009. "Prediction of Protein Binding Regions in Disordered Proteins," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-18, May.
    2. Jae Kyoung Kim & Trachette L Jackson, 2013. "Mechanisms That Enhance Sustainability of p53 Pulses," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
    3. Ikeda, N., 2007. "Network formed by traces of random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 701-713.
    4. Jeffrey V Wong & Bochong Li & Lingchong You, 2012. "Tension and Robustness in Multitasking Cellular Networks," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-12, April.
    5. Ronja Foraita & Juliane Friemel & Kathrin Günther & Thomas Behrens & Jörn Bullerdiek & Rolf Nimzyk & Wolfgang Ahrens & Vanessa Didelez, 2020. "Causal discovery of gene regulation with incomplete data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1747-1775, October.
    6. Wassim Abou-Jaoudé & Madalena Chaves & Jean-Luc Gouzé, 2011. "A Theoretical Exploration of Birhythmicity in the p53-Mdm2 Network," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-12, February.
    7. Phillips, J.C., 2016. "Autoantibody recognition mechanisms of p53 epitopes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 162-170.
    8. Shruti Koulgi & Archana Achalere & Uddhavesh Sonavane & Rajendra Joshi, 2015. "Investigating DNA Binding and Conformational Variation in Temperature Sensitive p53 Cancer Mutants Using QM-MM Simulations," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-22, November.
    9. Sirkku Karinen & Tuomas Heikkinen & Heli Nevanlinna & Sampsa Hautaniemi, 2011. "Data Integration Workflow for Search of Disease Driving Genes and Genetic Variants," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-8, April.
    10. Piotrowska, Monika J. & Bartłomiejczyk, Agnieszka & Bodnar, Marek, 2018. "Mathematical analysis of a generalised p53-Mdm2 protein gene expression model," Applied Mathematics and Computation, Elsevier, vol. 328(C), pages 26-44.
    11. Yong-Jun Shin & Ali H Sayed & Xiling Shen, 2012. "Adaptive Models for Gene Networks," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-6, February.

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