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A Feedback Quenched Oscillator Produces Turing Patterning with One Diffuser

Overview of attention for article published in PLoS Computational Biology, January 2012
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Title
A Feedback Quenched Oscillator Produces Turing Patterning with One Diffuser
Published in
PLoS Computational Biology, January 2012
DOI 10.1371/journal.pcbi.1002331
Pubmed ID
Authors

Justin Hsia, William J. Holtz, Daniel C. Huang, Murat Arcak, Michel M. Maharbiz

Abstract

Efforts to engineer synthetic gene networks that spontaneously produce patterning in multicellular ensembles have focused on Turing's original model and the "activator-inhibitor" models of Meinhardt and Gierer. Systems based on this model are notoriously difficult to engineer. We present the first demonstration that Turing pattern formation can arise in a new family of oscillator-driven gene network topologies, specifically when a second feedback loop is introduced which quenches oscillations and incorporates a diffusible molecule. We provide an analysis of the system that predicts the range of kinetic parameters over which patterning should emerge and demonstrate the system's viability using stochastic simulations of a field of cells using realistic parameters. The primary goal of this paper is to provide a circuit architecture which can be implemented with relative ease by practitioners and which could serve as a model system for pattern generation in synthetic multicellular systems. Given the wide range of oscillatory circuits in natural systems, our system supports the tantalizing possibility that Turing pattern formation in natural multicellular systems can arise from oscillator-driven mechanisms.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 7%
United Kingdom 4 4%
Spain 3 3%
Germany 2 2%
Brazil 1 <1%
Sweden 1 <1%
Norway 1 <1%
Chile 1 <1%
Portugal 1 <1%
Other 2 2%
Unknown 83 78%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 29%
Researcher 25 24%
Student > Master 13 12%
Student > Bachelor 8 8%
Student > Doctoral Student 6 6%
Other 16 15%
Unknown 7 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 38%
Engineering 17 16%
Physics and Astronomy 12 11%
Biochemistry, Genetics and Molecular Biology 10 9%
Computer Science 6 6%
Other 13 12%
Unknown 8 8%