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Modeling and Measuring Signal Relay in Noisy Directed Migration of Cell Groups

Overview of attention for article published in PLoS Computational Biology, May 2013
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Title
Modeling and Measuring Signal Relay in Noisy Directed Migration of Cell Groups
Published in
PLoS Computational Biology, May 2013
DOI 10.1371/journal.pcbi.1003041
Pubmed ID
Authors

Can Guven, Erin Rericha, Edward Ott, Wolfgang Losert

Abstract

We develop a coarse-grained stochastic model for the influence of signal relay on the collective behavior of migrating Dictyostelium discoideum cells. In the experiment, cells display a range of collective migration patterns, including uncorrelated motion, formation of partially localized streams, and clumping, depending on the type of cell and the strength of the external, linear concentration gradient of the signaling molecule cyclic adenosine monophosphate (cAMP). From our model, we find that the pattern of migration can be quantitatively described by the competition of two processes, the secretion rate of cAMP by the cells and the degradation rate of cAMP in the gradient chamber. Model simulations are compared to experiments for a wide range of strengths of an external linear-gradient signal. With degradation, the model secreting cells form streams and efficiently transverse the gradient, but without degradation, we find that model secreting cells form clumps without streaming. This indicates that the observed effective collective migration in streams requires not only signal relay but also degradation of the signal. In addition, our model allows us to detect and quantify precursors of correlated motion, even when cells do not exhibit obvious streaming.

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Geographical breakdown

Country Count As %
United States 3 8%
Portugal 1 3%
Unknown 34 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 32%
Student > Master 6 16%
Student > Ph. D. Student 6 16%
Professor > Associate Professor 5 13%
Student > Doctoral Student 2 5%
Other 3 8%
Unknown 4 11%
Readers by discipline Count As %
Physics and Astronomy 15 39%
Agricultural and Biological Sciences 11 29%
Biochemistry, Genetics and Molecular Biology 3 8%
Environmental Science 1 3%
Computer Science 1 3%
Other 3 8%
Unknown 4 11%