Literature DB >> 17061055

A mathematical framework for modeling axon guidance.

Johannes K Krottje1, Arjen van Ooyen.   

Abstract

In this paper, a simulation tool for modeling axon guidance is presented. A mathematical framework in which a wide range of models can been implemented has been developed together with efficient numerical algorithms. In our framework, models can be defined that consist of concentration fields of guidance molecules in combination with finite-dimensional state vectors. These vectors can characterize migrating growth cones, target neurons that release guidance molecules, or other cells that act as sources of membrane-bound or diffusible guidance molecules. The underlying mathematical framework is presented as well as the numerical methods to solve them. The potential applications of our simulation tool are illustrated with a number of examples, including a model of topographic mapping.

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Year:  2006        PMID: 17061055      PMCID: PMC2806218          DOI: 10.1007/s11538-006-9142-4

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  20 in total

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Authors:  D G Wilkinson
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Review 2.  Signal transduction underlying growth cone guidance by diffusible factors.

Authors:  H J Song; M M Poo
Journal:  Curr Opin Neurobiol       Date:  1999-06       Impact factor: 6.627

3.  Models of axon guidance and bundling during development.

Authors:  H G Hentschel; A van Ooyen
Journal:  Proc Biol Sci       Date:  1999-11-07       Impact factor: 5.349

4.  Electrical activity modulates growth cone guidance by diffusible factors.

Authors:  G Ming; J Henley; M Tessier-Lavigne; H Song; M Poo
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5.  Adaptation in the chemotactic guidance of nerve growth cones.

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Journal:  Nature       Date:  2002-05-01       Impact factor: 49.962

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8.  Topographic mapping in the retinotectal projection by means of complementary ligand and receptor gradients: a computer simulation study.

Authors:  H Honda
Journal:  J Theor Biol       Date:  1998-05-21       Impact factor: 2.691

9.  Change in chemoattractant responsiveness of developing axons at an intermediate target.

Authors:  R Shirasaki; R Katsumata; F Murakami
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10.  Age-related changes underlie switch in netrin-1 responsiveness as growth cones advance along visual pathway.

Authors:  D Shewan; A Dwivedy; R Anderson; C E Holt
Journal:  Nat Neurosci       Date:  2002-10       Impact factor: 24.884

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  17 in total

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6.  Developmental time windows for axon growth influence neuronal network topology.

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Journal:  Biol Cybern       Date:  2015-01-30       Impact factor: 2.086

7.  A hybrid computational model to predict chemotactic guidance of growth cones.

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Journal:  Sci Rep       Date:  2015-06-18       Impact factor: 4.379

8.  Glial scar size, inhibitor concentration, and growth of regenerating axons after spinal cord transection.

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Journal:  Neural Regen Res       Date:  2012-07-15       Impact factor: 5.135

9.  A framework for modeling the growth and development of neurons and networks.

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10.  A developmental approach to predicting neuronal connectivity from small biological datasets: a gradient-based neuron growth model.

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Journal:  PLoS One       Date:  2014-02-21       Impact factor: 3.240

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