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Proc Natl Acad Sci U S A. 2010 Oct 5;107(40):17079-86. doi: 10.1073/pnas.1011271107. Epub 2010 Sep 23.

Cells navigate with a local-excitation, global-inhibition-biased excitable network.

Author information

1
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Abstract

Cells have an internal compass that enables them to move along shallow chemical gradients. As amoeboid cells migrate, signaling events such as Ras and PI3K activation occur spontaneously on pseudopodia. Uniform stimuli trigger a symmetric response, whereupon cells stop and round up; then localized patches of activity appear as cells spread. Finally cells adapt and resume random migration. In contrast, chemotactic gradients continuously direct signaling events to the front of the cell. Local-excitation, global-inhibition (LEGI) and reaction-diffusion models have captured some of these features of chemotaxing cells, but no system has explained the complex response kinetics, sensitivity to shallow gradients, or the role of recently observed propagating waves within the actin cytoskeleton. We report here that Ras and PI3K activation move in phase with the cytoskeleton events and, drawing on all of these observations, propose the LEGI-biased excitable network hypothesis. We formulate a model that simulates most of the behaviors of chemotactic cells: In the absence of stimulation, there are spontaneous spots of activity. Stimulus increments trigger an initial burst of patches followed by localized secondary events. After a few minutes, the system adapts, again displaying random activity. In gradients, the activity patches are directed continuously and selectively toward the chemoattractant, providing an extraordinary degree of amplification. Importantly, by perturbing model parameters, we generate distinct behaviors consistent with known classes of mutants. Our study brings together heretofore diverse observations on spontaneous cytoskeletal activity, signaling responses to temporal stimuli, and spatial gradient sensing into a unified scheme.

PMID:
20864631
PMCID:
PMC2951443
DOI:
10.1073/pnas.1011271107
[Indexed for MEDLINE]
Free PMC Article

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