Quantitative statistical description of integrin clusters in adherent cells

E. S. Welf, B. A. Ogunnaike, U. P. Naik

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Regulation of protein-protein interactions because of their spatial organisation in cells often shapes cell signalling responses to external stimuli, yet most current cell signalling models do not include spatial segregation of proteins beyond coarse control volumes like the cytosol or nucleus. A significant hindrance to spatial modelling of cell signalling is a lack of data describing the spatial organisation of proteins in cells. One signalling system in which spatial organisation is critical is integrin signalling, where protein interactions are restricted to small, micron-sized protein complexes that form on clusters of transmembrane integrin proteins. Using confocal microscopy and image analysis to quantify the size, shape and location of integrin clusters, the authors observed that cells exhibit large variability in these integrin cluster properties. To describe these heterogeneous populations of clusters quantitatively, the authors identified appropriate probability models to characterise the size, shape and location of integrin clusters in a population of adherent cells. The authors determined that integrin cluster sizes are lognormally distributed, integrin cluster eccentricities are beta distributed, and the distances of integrin clusters from the cell centre are gamma distributed. The authors estimated the parameters corresponding to these probability models from empirical data describing integrin clusters in a population of cells, and the resulting probability models describe the heterogeneous populations of clusters. These population models provide the means to create an accurate mathematical description of spatially localised integrin signalling compartments for use in computational models of integrin signalling.

Original languageEnglish (US)
Pages (from-to)307-316
Number of pages10
JournalIET Systems Biology
Volume3
Issue number5
DOIs
StatePublished - Oct 2 2009

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ASJC Scopus subject areas

  • Biotechnology
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Cell Biology

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