Biophysical model of axonal pathfinding

Aeschlimann, Mélanie; Tettoni, Laurent

doi:10.1016/s0925-2312(01)00539-2

Cited by 32 publications

(57 citation statements)

References 12 publications

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“…Further modeling work has focused on potential biochemical implementations of chemosensory behavior in cells and growth cones (Hentschel & van Ooyen, 1999;Aeschlimann & Tettoni, 2001;Levchenko & Iglesias, 2002;Levine, Kessler, & Rappel, 2004;Sakumura, Tsukada, Yamamoto, & Ishii, 2005;Skupsky, Losert, & Nossal, 2005;Narang, 2006;Skupsky, McCann, Nossal, & Losert, 2007;Onsum & Rao, 2007;Naoki, Sakumura, & Ishii, 2008). However, none of this work has addressed the question of the strategy by which a gradient sensor should combine information from a spatially distributed array of receptors to optimally determine gradient direction.…”

Section: Introductionmentioning

confidence: 99%

Bayes-Optimal Chemotaxis

et al. 2011

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Chemotaxis plays a crucial role in many biological processes, including nervous system development. However, fundamental physical constraints limit the ability of a small sensing device such as a cell or growth cone to detect an external chemical gradient. One of these is the stochastic nature of receptor binding, leading to a constantly fluctuating binding pattern across the cell's array of receptors. This is analogous to the uncertainty in sensory information often encountered by the brain at the systems level. Here we derive analytically the Bayes-optimal strategy for combining information from a spatial array of receptors in both one and two dimensions to determine gradient direction. We also show how information from more than one receptor species can be optimally integrated, derive the gradient shapes that are optimal for guiding cells or growth cones over the longest possible distances, and illustrate that polarized cell behavior might arise as an adaptation to slowly varying environments. Together our results provide closed-form predictions for variations in chemotactic performance over a wide range of gradient conditions.

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Section: Introductionmentioning

confidence: 99%

Bayes-Optimal Chemotaxis

et al. 2011

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“…A wide variety of approaches have been taken, ranging from the purely statistical (for reviews see Ascoli, 2002;van Pelt and Uylings, 1999) to more mechanistic models (Aeschlimann, 2000;Miller and Samuels, 1997;Li et al, 1994Li et al, , 1995van Veen and van Pelt, 1994). To establish an understanding of the fundamental biophysical processes involved, we have formulated a model of tubulindriven neurite elongation (McLean and .…”

Section: Introductionmentioning

confidence: 99%

Dynamics of outgrowth in a continuum model of neurite elongation

2006

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Neurite outgrowth (dendrites and axons) should be a stable, but easily regulated process to enable a neuron to make its appropriate network connections during development. We explore the dynamics of outgrowth in a mathematical continuum model of neurite elongation. The model describes the construction of the internal microtubule cytoskeleton, which results from the production and transport of tubulin dimers and their assembly into microtubules at the growing neurite tip. Tubulin is assumed to be largely synthesised in the cell body from where it is transported by active mechanisms and by diffusion along the neurite. It is argued that this construction process is a fundamental limiting factor in neurite elongation. In the model, elongation is highly stable when tubulin transport is dominated by either active transport or diffusion, but oscillations in length may occur when both active transport and diffusion contribute. Autoregulation of tubulin production can eliminate these oscillations. In all cases a stable steady-state length is reached, provided there is intrinsic decay of tubulin. Small changes in growth parameters, such as the tubulin production rate, can lead to large changes in length. Thus cytoskeleton construction can be both stable and easily regulated, as seems necessary for neurite outgrowth during nervous system development.

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“…Due to the fact that gradient sensing and the internal signal processing are inherently stocastic phenomena, several approaches synthetically describe the GC trajectory using some kind of persistent random walk model (see for example [5,16]). Further models are investigated in [1,10,23], where simple mechanisms are investigated, along with their mathematical properties, that transduce the external signal into an internal signal and then a macroscopic response. To set up our model, we think to the turning process as the sequence of simpler functional tasks.…”

Section: Mathematical Model Of Neuron Migrationmentioning

confidence: 99%

Little Tom Thumb among cells: seeking the cues of life

et al. 2009

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For a living being or a cell in a developing body, recognizing its peers and locating food sources or other targets and moving towards them is of paramount importance. In most cases this is achieved by detecting the presence of chemical substances in the environment and moving towards the areas of their higher concentration, a process known as chemotaxis. Despite its fundamental role for life, this phenomenon is not yet fully understood in all its details and mathematical models are proving very useful in guiding biological research. We address here two examples of chemotaxis occurring in the developing embryo: early formation of the vascular plexus and axon navigation in the wiring of the nervous system.

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Biophysical model of axonal pathfinding

Cited by 32 publications

References 12 publications

Bayes-Optimal Chemotaxis

Bayes-Optimal Chemotaxis

Dynamics of outgrowth in a continuum model of neurite elongation

Little Tom Thumb among cells: seeking the cues of life

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