Tse T H Z, Chan B P, Chan C M, Lam J
Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
Ann Biomed Eng. 2007 Sep;35(9):1561-72. doi: 10.1007/s10439-007-9328-4. Epub 2007 May 23.
Neurotrophic factors such as nerve growth factor (NGF) provide essential cues to navigate growing axon toward their targets. Concentration and concentration gradient of NGF are key parameters affecting the growth rate and direction of neurites and axons. However, the maximum distance for guided nerve growth under stimulation of a single concentration gradient is limited and is thus unfavorable in nerve regeneration. Since the sensitivity of PC12 cells to NGF signals is restorable even after brief removal of the factors, exposure to multiple concentration gradients of the factor can achieve longer distances and greater rates of guided growth. In this study, a mathematical model simulating nerve growth in a virtually constructed nerve conduit incorporating multiple NGF concentration gradients is established. Using a genetic algorithm, optimized initial profiles of NGF able to achieve 4.5 cm of guided growth with a significantly improved growth rate has been obtained. The model also predicts an inverse relationship between the diffusion coefficient of the factor and the neurite growth rate. This model provides a useful tool for evaluating various conduit designs before fabrication and evaluation.
神经营养因子，如神经生长因子（NGF），为生长中的轴突向其靶标导航提供重要线索。NGF的浓度和浓度梯度是影响神经突和轴突生长速率及方向的关键参数。然而，在单一浓度梯度刺激下引导神经生长的最大距离是有限的，因此在神经再生中是不利的。由于即使在短暂去除这些因子后，PC12细胞对NGF信号的敏感性仍可恢复，因此暴露于该因子的多个浓度梯度可实现更长的距离和更高的引导生长速率。在本研究中，建立了一个数学模型，该模型模拟了在虚拟构建的包含多个NGF浓度梯度的神经导管中的神经生长。使用遗传算法，已获得能够实现4.5厘米引导生长且生长速率显著提高的NGF优化初始分布。该模型还预测了该因子的扩散系数与神经突生长速率之间的反比关系。该模型为在制造和评估之前评估各种导管设计提供了一个有用的工具。
