Literature DB >> 10512980

Theoretical analysis of gradient detection by growth cones.

G J Goodhill1, J S Urbach.   

Abstract

Gradients of diffusible and substrate-bound molecules play an important role in guiding axons to appropriate targets in the developing nervous system. Although some of the molecules involved have recently been identified, little is known about the physical mechanisms by which growth cones sense gradients. This article applies the seminal Berg and Purcell (1977) model of gradient sensing to this problem. The model provides estimates for the statistical fluctuations in the measurement of concentration by a small sensing device. By assuming that gradient detection consists of the comparison of concentrations at two spatially or temporally separated points, the model therefore provides an estimate for the steepness of gradient that can be detected as a function of physiological parameters. The model makes the following specific predictions. (a) It is more likely that growth cones use a spatial rather than temporal sensing strategy. (b) Growth cone sensitivity increases with the concentration of ligand, the speed of ligand diffusion, the size of the growth cone, and the time over which it averages the gradient signal. (c) The minimum detectable gradient steepness for growth cones is roughly in the range 1-10%. (d) This value varies depending on whether a bound or freely diffusing ligand is being sensed, and on whether the sensing occurs in three or two dimensions. The model also makes predictions concerning the role of filopodia in gradient detection. Copyright 1999 John Wiley & Sons, Inc.

Mesh:

Year:  1999        PMID: 10512980     DOI: 10.1002/(sici)1097-4695(19991105)41:2<230::aid-neu6>3.0.co;2-9

Source DB:  PubMed          Journal:  J Neurobiol        ISSN: 0022-3034


  19 in total

1.  Designing in vivo concentration gradients with discrete controlled release: a computational model.

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2.  Limits to the precision of gradient sensing with spatial communication and temporal integration.

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Journal:  Proc Natl Acad Sci U S A       Date:  2016-01-20       Impact factor: 11.205

3.  Cell-cell communication during collective migration.

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Journal:  Proc Natl Acad Sci U S A       Date:  2016-01-22       Impact factor: 11.205

4.  Local diameter fully constrains dendritic size in basal but not apical trees of CA1 pyramidal neurons.

Authors:  Duncan E Donohue; Giorgio A Ascoli
Journal:  J Comput Neurosci       Date:  2005-10       Impact factor: 1.621

5.  A molecular model for axon guidance based on cross talk between rho GTPases.

Authors:  Yuichi Sakumura; Yuki Tsukada; Nobuhiko Yamamoto; Shin Ishii
Journal:  Biophys J       Date:  2005-05-27       Impact factor: 4.033

6.  Direct measurement of force generation by actin filament polymerization using an optical trap.

Authors:  Matthew J Footer; Jacob W J Kerssemakers; Julie A Theriot; Marileen Dogterom
Journal:  Proc Natl Acad Sci U S A       Date:  2007-02-02       Impact factor: 11.205

7.  Bayesian model predicts the response of axons to molecular gradients.

Authors:  Duncan Mortimer; Julia Feldner; Timothy Vaughan; Irina Vetter; Zac Pujic; William J Rosoff; Kevin Burrage; Peter Dayan; Linda J Richards; Geoffrey J Goodhill
Journal:  Proc Natl Acad Sci U S A       Date:  2009-06-18       Impact factor: 11.205

Review 8.  Using theoretical models to analyse neural development.

Authors:  Arjen van Ooyen
Journal:  Nat Rev Neurosci       Date:  2011-05-18       Impact factor: 34.870

9.  Spatially patterned gene expression for guided neurite extension.

Authors:  Tiffany Houchin-Ray; Alyssa Huang; Erin R West; Marina Zelivyanskaya; Lonnie D Shea
Journal:  J Neurosci Res       Date:  2009-03       Impact factor: 4.164

10.  Autocatalytic loop, amplification and diffusion: a mathematical and computational model of cell polarization in neural chemotaxis.

Authors:  Paola Causin; Giuseppe Facchetti
Journal:  PLoS Comput Biol       Date:  2009-08-28       Impact factor: 4.475

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