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Network heterogeneity regulates steering in actin-based motility

Author

Listed:
  • Rajaa Boujemaa-Paterski

    (Université Grenoble-Alpes/CEA/CNRS/INRA)

  • Cristian Suarez

    (Université Grenoble-Alpes/CEA/CNRS/INRA)

  • Tobias Klar

    (Université Grenoble-Alpes/CEA/CNRS/INRA)

  • Jie Zhu

    (New York University)

  • Christophe Guérin

    (Université Grenoble-Alpes/CEA/CNRS/INRA)

  • Alex Mogilner

    (New York University)

  • Manuel Théry

    (Université Grenoble-Alpes/CEA/CNRS/INRA
    Institut Universitaire d’Hematologie, UMRS1160, INSERM/AP-HP/Université Paris Diderot)

  • Laurent Blanchoin

    (Université Grenoble-Alpes/CEA/CNRS/INRA
    Institut Universitaire d’Hematologie, UMRS1160, INSERM/AP-HP/Université Paris Diderot)

Abstract

The growth of branched actin networks powers cell-edge protrusions and motility. A heterogeneous density of actin, which yields to a tunable cellular response, characterizes these dynamic structures. We study how actin organization controls both the rate and the steering during lamellipodium growth. We use a high-resolution surface structuration assay combined with mathematical modeling to describe the growth of a reconstituted lamellipodium. We demonstrate that local monomer depletion at the site of assembly negatively impacts the network growth rate. At the same time, network architecture tunes the protrusion efficiency, and regulates the rate of growth. One consequence of this interdependence between monomer depletion and network architecture effects is the ability of heterogeneous network to impose steering during motility. Therefore, we have established that the general principle, by which the cell can modulate the rate and the direction of a protrusion, is by varying both density and architecture of its actin network.

Suggested Citation

  • Rajaa Boujemaa-Paterski & Cristian Suarez & Tobias Klar & Jie Zhu & Christophe Guérin & Alex Mogilner & Manuel Théry & Laurent Blanchoin, 2017. "Network heterogeneity regulates steering in actin-based motility," Nature Communications, Nature, vol. 8(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00455-1
    DOI: 10.1038/s41467-017-00455-1
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    Cited by:

    1. Chang, Zhenhai & Yin, Xianjun & Jia, Caiyan & Wang, Xiaoyang, 2018. "Mixture models with entropy regularization for community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 339-350.

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