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Angiogenesis: An Adaptive Dynamic Biological Patterning Problem

Overview of attention for article published in PLoS Computational Biology, March 2013
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Title
Angiogenesis: An Adaptive Dynamic Biological Patterning Problem
Published in
PLoS Computational Biology, March 2013
DOI 10.1371/journal.pcbi.1002983
Pubmed ID
Authors

Timothy W. Secomb, Jonathan P. Alberding, Richard Hsu, Mark W. Dewhirst, Axel R. Pries

Abstract

Formation of functionally adequate vascular networks by angiogenesis presents a problem in biological patterning. Generated without predetermined spatial patterns, networks must develop hierarchical tree-like structures for efficient convective transport over large distances, combined with dense space-filling meshes for short diffusion distances to every point in the tissue. Moreover, networks must be capable of restructuring in response to changing functional demands without interruption of blood flow. Here, theoretical simulations based on experimental data are used to demonstrate that this patterning problem can be solved through over-abundant stochastic generation of vessels in response to a growth factor generated in hypoxic tissue regions, in parallel with refinement by structural adaptation and pruning. Essential biological mechanisms for generation of adequate and efficient vascular patterns are identified and impairments in vascular properties resulting from defects in these mechanisms are predicted. The results provide a framework for understanding vascular network formation in normal or pathological conditions and for predicting effects of therapies targeting angiogenesis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Netherlands 2 1%
United Kingdom 2 1%
Portugal 1 <1%
Poland 1 <1%
Unknown 141 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 51 34%
Researcher 26 17%
Student > Master 11 7%
Student > Doctoral Student 8 5%
Student > Bachelor 8 5%
Other 22 15%
Unknown 25 17%
Readers by discipline Count As %
Engineering 36 24%
Agricultural and Biological Sciences 28 19%
Medicine and Dentistry 11 7%
Physics and Astronomy 11 7%
Mathematics 10 7%
Other 23 15%
Unknown 32 21%