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Patterns of Proliferative Activity in the Colonic Crypt Determine Crypt Stability and Rates of Somatic Evolution

Overview of attention for article published in PLoS Computational Biology, June 2013
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Title
Patterns of Proliferative Activity in the Colonic Crypt Determine Crypt Stability and Rates of Somatic Evolution
Published in
PLoS Computational Biology, June 2013
DOI 10.1371/journal.pcbi.1003082
Pubmed ID
Authors

Rui Zhao, Franziska Michor

Abstract

Epithelial cells in the colon are arranged in cylindrical structures called crypts in which cellular proliferation and migration are tightly regulated. We hypothesized that the proliferation patterns of cells may determine the stability of crypts as well as the rates of somatic evolution towards colorectal tumorigenesis. Here, we propose a linear process model of colonic epithelial cells that explicitly takes into account the proliferation kinetics of cells as a function of cell position within the crypt. Our results indicate that proliferation kinetics has significant influence on the speed of cell movement, kinetics of mutation propagation, and sensitivity of the system to selective effects of mutated cells. We found that, of all proliferation curves tested, those with mitotic activities concentrated near the stem cell, including the actual proliferation kinetics determined in in vivo labeling experiments, have a greater ability of delaying the rate of mutation accumulation in colonic stem cells compared to hypothetical proliferation curves with mitotic activities focused near the top of the crypt column. Our model can be used to investigate the dynamics of proliferation and mutation accumulation in spatially arranged tissues.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 3%
Unknown 37 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 34%
Student > Ph. D. Student 10 26%
Student > Bachelor 6 16%
Student > Master 3 8%
Student > Doctoral Student 1 3%
Other 1 3%
Unknown 4 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 32%
Agricultural and Biological Sciences 12 32%
Medicine and Dentistry 5 13%
Mathematics 3 8%
Computer Science 1 3%
Other 2 5%
Unknown 3 8%