标题： 熵约束噪声导致轴突生长的超扩散动力学 显示英文标题

标题： Entropy-Constrained Noise Yields Superdiffusive Dynamics in Axonal Growth

摘要： 我们提出了一种粗粒度的随机模型，用于描述轴突在周期性平行微图案阵列上的延伸，该模型整合了三个关键的生物物理机制：（i）分子抱括机制，它将肌动蛋白逆向流动与基底粘附耦合起来；（ii）基于活性生物聚合物的机制，产生牵引力波动；（iii）生长锥与微图案基底之间的机械相互作用。 利用从实验观察中推导出的约束条件以及香农-杰恩斯最大熵原理，我们推导出了进入朗之万方程的有色加速度噪声的独特概率分布。 由此产生的平稳过程表现出具有负指数的幂律时间相关性，这可以解释观测到的轴突超扩散动力学。 对于生物学相关的参数，该模型预测这个指数为-1/2，与在图案化基底上培养的大脑皮层神经元的测量结果在定量上非常吻合。 显示英文摘要

摘要： We present a coarse-grained stochastic model for axonal extension on periodic arrays of parallel micropatterns that integrates three key biophysical mechanisms: (i) the molecular clutch that couples actin retrograde flow to substrate adhesions, (ii) an active biopolymer-based mechanism generating traction-force fluctuations, and (iii) the mechanical interaction of the growth cone with the micropatterned substrate. Using the Shannon-Jaynes maximum entropy principle with constraints derived from experimental observations, we derive a unique probability distribution for the colored acceleration noise that enters the Langevin equation. The resulting stationary process exhibits power-law temporal correlations with negative exponent, which accounts for the observed superdiffusive dynamics of axons. For biologically relevant parameters the model predicts this exponent to be -1/2, in close quantitative agreement with measurements of cortical neurons cultured on patterned substrates.