Université Côte d'Azur, INRIA, CNRS, I3S, Nice, France.
Université Côte d'Azur, CNRS, Inserm, iBV, Nice, France.
PLoS Comput Biol. 2018 Dec 3;14(12):e1006627. doi: 10.1371/journal.pcbi.1006627. eCollection 2018 Dec.
The confined and crowded environment of developing brains imposes spatial constraints on neuronal cells that have evolved individual and collective strategies to optimize their growth. These include organizing neurons into populations extending their axons to common target territories. How individual axons interact with each other within such populations to optimize innervation is currently unclear and difficult to analyze experimentally in vivo. Here, we developed a stochastic model of 3D axon growth that takes into account spatial environmental constraints, physical interactions between neighboring axons, and branch formation. This general, predictive and robust model, when fed with parameters estimated on real neurons from the Drosophila brain, enabled the study of the mechanistic principles underlying the growth of axonal populations. First, it provided a novel explanation for the diversity of growth and branching patterns observed in vivo within populations of genetically identical neurons. Second, it uncovered that axon branching could be a strategy optimizing the overall growth of axons competing with others in contexts of high axonal density. The flexibility of this framework will make it possible to investigate the rules underlying axon growth and regeneration in the context of various neuronal populations.
发育中大脑的封闭和拥挤环境对神经元施加了空间限制，这些神经元已经进化出个体和集体策略来优化它们的生长。这些策略包括将神经元组织成群，使其轴突延伸到共同的目标区域。在这种群体中，单个轴突如何相互作用以优化神经支配目前尚不清楚，并且在体内进行实验分析也很困难。在这里，我们开发了一个 3D 轴突生长的随机模型，该模型考虑了空间环境限制、相邻轴突之间的物理相互作用和分支形成。当将从果蝇大脑中的真实神经元估计的参数输入到这个通用、可预测且稳健的模型中时，它可以研究轴突群体生长的基础机制原则。首先，它为在体内观察到的遗传上相同的神经元群体中观察到的不同的生长和分支模式提供了一种新的解释。其次，它揭示了分支可以是一种策略，即在高轴突密度的情况下，与其他轴突竞争时，可以优化轴突的整体生长。该框架的灵活性将使我们能够在各种神经元群体的背景下研究轴突生长和再生的规则。
