A Mathematical Model of Regenerative Axon Growing along Glial Scar after Spinal Cord Injury

Computational and Mathematical Methods in Medicine, 2016 · DOI: http://dx.doi.org/10.1155/2016/3030454 · Published: March 28, 2016

Spinal Cord Injury Neurology Bioinformatics

Simple Explanation

This paper introduces a mathematical model to simulate how damaged nerve fibers (axons) regrow around glial scars after spinal cord injuries. Glial scars are barriers that hinder nerve regeneration. The model is based on experiments where Schwann cells were transplanted to bridge the glial scar, and it uses the Lattice Boltzmann Method (LBM) for three-dimensional numerical simulation. The simulation results suggest that the level of inhibitory factors on the glial scar and the scar's size significantly affect axon regeneration. This information can help researchers design more effective experiments to promote nerve repair.

Study Duration

Not specified

Participants

Mouse model of spinal cord transaction

Evidence Level

Not specified

Key Findings

1
The level of inhibitory factors on the surface of glial scar significantly impacts axon elongation.
2
When inhibitory factor levels are constant, the longitudinal size of the glial scar influences the average rate of axon growth more than the transverse size.
3
Regenerating axons can navigate across glial scars with the support of Schwann cells and NTFs concentration gradients when the release rate of inhibitory factors is less than 3%.

Research Summary

This study presents a mathematical model to investigate axon regeneration around glial scars after spinal cord injury, focusing on the impact of inhibitory factors and scar size. The model, based on Schwann cell transplantation experiments, uses the Lattice Boltzmann Method for numerical simulation and identifies key parameters affecting axon growth. The findings indicate that the level of inhibitory factors and the longitudinal size of the glial scar are critical determinants of successful axon regeneration, providing insights for designing future experiments.

Practical Implications

Experimental Design

The model can guide the design of efficient experiments by elucidating the ratio and distribution law of various impact factors.

Treatment Strategies

The study supports the development of treatment strategies focusing on reducing inhibitory factor levels and managing glial scar size to promote axon regeneration.

Data Integration

The model allows for the integration of data from different experiments and laboratories, enhancing predictive capabilities.

Study Limitations

1
The study did not account for sprouting mechanisms after neuronal injury.
2
The model did not consider the polymerization of the cytoskeletal protein within the growth cone of the regenerative axon.
3
The study did not include other internal factors affecting axon regeneration.

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