Papers available: axon guidance

[Geoff Goodhill]

The following papers in TINS and J Neurobiol are now available from

http://www.giccs.georgetown.edu/labs/cns/axon.html

1. Retinotectal Maps: Molecules, Models, and Misplaced Data.
Geoffrey J. Goodhill & Linda J. Richards.
Trends in Neurosciences, 22, 529-534 (December 1999).

2. Theoretical analysis of gradient detection by growth cones.
Geoffrey J. Goodhill & Jeffrey S. Urbach.
Journal of Neurobiology, 41, 230-241 (November 1999).

Abstracts are below.

Geoff

Geoffrey J Goodhill, PhD
Assistant Professor, Department of Neuroscience &
Georgetown Institute for Cognitive and Computational Sciences
Georgetown University Medical Center
3970 Reservoir Road NW, Washington DC 20007
Tel: (202) 687 6889, Fax: (202) 687 0617
Email: geoff at giccs.georgetown.edu
Homepage: www.giccs.georgetown.edu/labs/cns

ABSTRACTS

1. The mechanisms underlying the formation of topographic maps in the
retinotectal system have long been debated. Recently, members of the
Eph and ephrin receptor-ligand family have been found to provide a
molecular substrate for one type of mechanism, that of chemospecific
gradient matching as proposed by Sperry. However, experiments over
several decades have demonstrated that there is more to map formation
than gradient matching. This article briefly reviews the old and new
findings, argues that these two types of data must be properly
integrated in order to understand map formation fully, and suggests
some experimental and theoretical ways to begin this process.

2. 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 paper applies
the seminal Berg & 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. (1) It is more likely
that growth cones use a spatial rather than temporal sensing
strategy. (2) 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.  (3) The
minimum detectable gradient steepness for growth cones is roughly in
the range 1% - 10%.  (4) This value varies depending on whether a
bound or freely diffusing ligand is being sensed, and on whether the
sensing occurs in three dimensions or two dimensions. The model also
makes predictions concerning the role of filopodia in gradient
detection.