从生物模式的学习表征中测量不可逆性
Measuring irreversibility from learned representations of biological patterns
AI总结：
本文利用深度学习从Rho-GTPase和复杂Ginzburg-Landau动力学模式中学习低维表征，并结合Ziv-Merhav估计器实现无模型的不可逆性测量，用于区分稳定与混沌动力学。
摘要：
热力学不可逆性是生命物质的一个关键性质。不可逆过程维持着生命系统所特有的时空复杂结构和功能。在高维生物动力学中，由于实验噪声以及耦合众多自由度的非线性相互作用，对不可逆性进行稳健且具有普适性的定量刻画仍然是一项具有挑战性的任务。在此，我们使用深度学习来识别一个典型蛋白质信号过程——Rho-GTPase系统——以及复杂Ginzburg-Landau动力学中相场模式的可处理低维表征。我们表明，因子化变分自编码器神经网络能够学习对噪声具有稳健性的信息性模式特征。由此得到的神经网络表征揭示了Rho-GTPase和复杂Ginzburg-Landau波动力学中介观细致平衡破缺和时间反演不对称性的特征。将基于压缩的Ziv-Merhav不可逆性估计器应用于这些表征，我们恢复了在时空频率和噪声水平上变化很大的复杂Ginzburg-Landau模式中的不可逆性趋势。来自表征的不可逆性估计同样重现了经历代谢抑制的Rho-GTPase模式形成系统中的细胞活动趋势。此外，我们发现我们的不可逆性估计可作为一种动力学序参量，用于区分这些非线性系统中的稳定动力学和混沌动力学。我们的框架利用深度学习进展，为复杂生命过程中的非平衡和非线性行为提供稳健的、无模型的测量。
英文摘要：
Thermodynamic irreversibility is a crucial property of living matter. Irreversible processes maintain spatiotemporally complex structures and functions characteristic of living systems. In high-dimensional biological dynamics, robust and general quantification of irreversibility remains a challenging task due to experimental noise and nonlinear interactions coupling many degrees of freedom. Here we use deep learning to identify tractable, low-dimensional representations of phase-field patterns in a canonical protein signaling process -- the Rho-GTPase system -- as well as complex Ginzburg-Landau dynamics. We show that factorizing variational autoencoder neural networks learn informative pattern features robustly to noise. Resulting neural-network representations reveal signatures of mesoscopic broken detailed balance and time-reversal asymmetry in Rho-GTPase and complex Ginzburg-Landau wave dynamics. Applying the compression-based Ziv-Merhav estimator of irreversibility to representations, we recover irreversibility trends across complex Ginzburg-Landau patterns varying widely in spatiotemporal frequency and noise level. Irreversibility estimates from representations similarly recapitulate cell-activity trends in a Rho-GTPase patterning system undergoing metabolic inhibition. Additionally, we find that our irreversibility estimates serve as a dynamical order parameter, distinguishing stable and chaotic dynamics in these nonlinear systems. Our framework leverages advances in deep learning to offer robust, model-free measurements of nonequilibrium and nonlinear behavior in complex living processes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
