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Quantitative Interpretation of a Genetic Model of Carcinogenesis Using Computer Simulations

Overview of attention for article published in PLOS ONE, March 2011
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Title
Quantitative Interpretation of a Genetic Model of Carcinogenesis Using Computer Simulations
Published in
PLOS ONE, March 2011
DOI 10.1371/journal.pone.0016859
Pubmed ID
Authors

Donghai Dai, Brandon Beck, Xiaofang Wang, Cory Howk, Yi Li

Abstract

The genetic model of tumorigenesis by Vogelstein et al. (V theory) and the molecular definition of cancer hallmarks by Hanahan and Weinberg (W theory) represent two of the most comprehensive and systemic understandings of cancer. Here, we develop a mathematical model that quantitatively interprets these seminal cancer theories, starting from a set of equations describing the short life cycle of an individual cell in uterine epithelium during tissue regeneration. The process of malignant transformation of an individual cell is followed and the tissue (or tumor) is described as a composite of individual cells in order to quantitatively account for intra-tumor heterogeneity. Our model describes normal tissue regeneration, malignant transformation, cancer incidence including dormant/transient tumors, and tumor evolution. Further, a novel mechanism for the initiation of metastasis resulting from substantial cell death is proposed. Finally, model simulations suggest two different mechanisms of metastatic inefficiency for aggressive and less aggressive cancer cells. Our work suggests that cellular de-differentiation is one major oncogenic pathway, a hypothesis based on a numerical description of a cell's differentiation status that can effectively and mathematically interpret some major concepts in V/W theories such as progressive transformation of normal cells, tumor evolution, and cancer hallmarks. Our model is a mathematical interpretation of cancer phenotypes that complements the well developed V/W theories based upon description of causal biological and molecular events. It is possible that further developments incorporating patient- and tissue-specific variables may build an even more comprehensive model to explain clinical observations and provide some novel insights for understanding cancer.

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The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 6%
Uruguay 1 3%
Germany 1 3%
Unknown 28 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 22%
Professor > Associate Professor 6 19%
Student > Ph. D. Student 5 16%
Student > Master 3 9%
Professor 2 6%
Other 3 9%
Unknown 6 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 44%
Medicine and Dentistry 3 9%
Computer Science 3 9%
Mathematics 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 1 3%
Unknown 7 22%