Nguyen Huyen, Dayan Peter, Pujic Zac, Cooper-White Justin, Goodhill Geoffrey J
Queensland Brain Institute, The University of Queensland, St. Lucia, Australia.
School of Mathematics and Physics, The University of Queensland, St. Lucia, Australia.
Elife. 2016 Feb 2;5:e12248. doi: 10.7554/eLife.12248.
Correct wiring is crucial for the proper functioning of the nervous system. Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molecular gradients bias their movement is unclear. Here, we introduce a mathematical model based on persistence, bias, and noise to describe this behaviour, constrained directly by measurements of the detailed statistics of growth cone movements in both attractive and repulsive gradients in a microfluidic device. This model provides a mathematical explanation for why average axon turning angles in gradients in vitro saturate very rapidly with time at relatively small values. This work introduces the most accurate predictive model of growth cone trajectories to date, and deepens our understanding of axon guidance events both in vitro and in vivo.
正确的布线对于神经系统的正常运作至关重要。分子梯度提供关键信号，引导生长锥（即发育中轴突的能动尖端）到达其目标。然而，在体外，生长锥追踪高度随机的轨迹，而且分子梯度如何影响其运动尚不清楚。在这里，我们引入了一个基于持久性、偏差和噪声的数学模型来描述这种行为，该模型直接受微流控装置中吸引性和排斥性梯度下生长锥运动详细统计测量的约束。该模型为体外梯度中轴突平均转向角为何随时间在相对较小的值时非常迅速地饱和提供了数学解释。这项工作引入了迄今为止最准确的生长锥轨迹预测模型，并加深了我们对体外和体内轴突导向事件的理解。
