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Modeling and Inferring Cleavage Patterns in Proliferating Epithelia

Overview of attention for article published in PLoS Computational Biology, June 2009
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Title
Modeling and Inferring Cleavage Patterns in Proliferating Epithelia
Published in
PLoS Computational Biology, June 2009
DOI 10.1371/journal.pcbi.1000412
Pubmed ID
Authors

Ankit B. Patel, William T. Gibson, Matthew C. Gibson, Radhika Nagpal

Abstract

The regulation of cleavage plane orientation is one of the key mechanisms driving epithelial morphogenesis. Still, many aspects of the relationship between local cleavage patterns and tissue-level properties remain poorly understood. Here we develop a topological model that simulates the dynamics of a 2D proliferating epithelium from generation to generation, enabling the exploration of a wide variety of biologically plausible cleavage patterns. We investigate a spectrum of models that incorporate the spatial impact of neighboring cells and the temporal influence of parent cells on the choice of cleavage plane. Our findings show that cleavage patterns generate "signature" equilibrium distributions of polygonal cell shapes. These signatures enable the inference of local cleavage parameters such as neighbor impact, maternal influence, and division symmetry from global observations of the distribution of cell shape. Applying these insights to the proliferating epithelia of five diverse organisms, we find that strong division symmetry and moderate neighbor/maternal influence are required to reproduce the predominance of hexagonal cells and low variability in cell shape seen empirically. Furthermore, we present two distinct cleavage pattern models, one stochastic and one deterministic, that can reproduce the empirical distribution of cell shapes. Although the proliferating epithelia of the five diverse organisms show a highly conserved cell shape distribution, there are multiple plausible cleavage patterns that can generate this distribution, and experimental evidence suggests that indeed plants and fruitflies use distinct division mechanisms.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 4%
United States 2 2%
Germany 1 1%
Unknown 76 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 38%
Researcher 22 27%
Professor 5 6%
Student > Bachelor 4 5%
Professor > Associate Professor 4 5%
Other 11 13%
Unknown 5 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 48%
Biochemistry, Genetics and Molecular Biology 14 17%
Physics and Astronomy 7 9%
Mathematics 5 6%
Computer Science 4 5%
Other 6 7%
Unknown 7 9%