可压缩主动流网络中的模态选择
Mode selection in compressible active flow networks
摘要：
许多非平衡物理、生物或信息传输网络中的相干大尺度动力学是由小尺度局部能量输入驱动的。在这里，我们介绍和探讨了一个分析易处理的非线性模型的可压缩主动流网络。与热驱动系统相比，我们发现主动摩擦选择离散状态，其中有限数量的振荡模式在不同的固定振幅下激活。使用扰动理论，我们系统地预测噪声网络的稳态，并找到良好的协议与贝叶斯状态估计的基础上应用于模拟的时间序列数据的隐马尔可夫模型。我们的研究结果表明，积极的网络结构，从肌动球蛋白力网络细胞质流的宏观响应，可以占主导地位的模式数量显着减少，相反，能量均分在热平衡。该模型也非常适合于研究活性物质的拓扑声模和光谱带隙。
英文摘要：
Coherent, large scale dynamics in many nonequilibrium physical, biological, or information transport networks are driven by small-scale local energy input. Here, we introduce and explore an analytically tractable nonlinear model for compressible active flow networks. In contrast to thermally-driven systems, we find that active friction selects discrete states with a limited number of oscillation modes activated at distinct fixed amplitudes. Using perturbation theory, we systematically predict the stationary states of noisy networks and find good agreement with a Bayesian state estimation based on a hidden Markov model applied to simulated time series data. Our results suggest that the macroscopic response of active network structures, from actomyosin force networks to cytoplasmic flows, can be dominated by a significantly reduced number of modes, in contrast to energy equipartition in thermal equilibrium. The model is also well-suited to study topological sound modes and spectral band gaps in active matter.
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