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Spatiotemporal Dynamics of Eukaryotic Gradient Sensing

  • K. K. Subramanian
  • Atul Narang
Part of the Topics in Biomedical Engineering International Book Series book series (ITBE)

Abstract

The crawling movement of eukaryotic cells in response to a chemical gradient is a complex process involving the orchestration of several subcellular activities. Although a complete description of the mechanisms underlying cell movement remains elusive, the very first step of gradient sensing, enabling the cell to perceive the imposed gradient, is becoming more transparent. The increased understanding of this step has been driven by the discovery that within 5–10 seconds of applying a weak chemoattractant gradient, membrane phosphoinositides, such as PIP3, localize at the front end of the cell, where they activate a process of intense actin polymerization and trigger the extension of a protrusion. This train of events implies that the key to gradient sensing is a mechanistic understanding of the phosphoinositide localization. Since the phosphoinositide distribution is highly localized compared to the shallow chemoattractant gradient, it has been suggested that the cell merely amplifies the chemoattractant gradient. However, this cannot be true since the phosphoinositide localization can display a bewildering array of spatial distributions that bear no resemblance to the external chemoattractant profile. For instance, a single phosphoinositide localization is produced in the face of multiple chemoattractant sources. More surprisingly, the localization forms at a random location even if the chemoattractant concentration is uniform. Here we show that all these seemingly complex dynamics are consistent with the so-called activator-inhibitor class of models. To this end, we formulate and simulate an activator-inhibitor model of gradient sensing based on the phosphoinositide signaling pathways.

Keywords

Transition Layer Phosphatidic Acid Actin Polymerization Spontaneous Polarization Inositol Phosphate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Inc. 2006

Authors and Affiliations

  • K. K. Subramanian
    • 1
  • Atul Narang
    • 1
  1. 1.Department of Chemical EngineeringUniversity of FloridaGainesville

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