Learning the non-equilibrium dynamics of Brownian movies

被引:25
作者
Gnesotto, Federico S. [1 ]
Gradziuk, Grzegorz [1 ]
Ronceray, Pierre [2 ]
Broedersz, Chase P. [1 ,3 ]
机构
[1] Ludwig Maximilians Univ MUnchen, Arnold Sommerfeld Ctr Theoret Phys, D-80333 Munich, Germany
[2] Princeton Univ, Ctr Phys Biol Funct, Princeton, NJ 08544 USA
[3] Vrije Univ Amsterdam, Dept Phys & Astron, NL-1081 HV Amsterdam, Netherlands
关键词
IN-VIVO; MOTION; MECHANICS; MICROSCOPY; REVEALS; SHAPE;
D O I
10.1038/s41467-020-18796-9
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Time-lapse microscopy imaging provides direct access to the dynamics of soft and living systems. At mesoscopic scales, such microscopy experiments reveal intrinsic thermal and non-equilibrium fluctuations. These fluctuations, together with measurement noise, pose a challenge for the dynamical analysis of these Brownian movies. Traditionally, methods to analyze such experimental data rely on tracking embedded or endogenous probes. However, it is in general unclear, especially in complex many-body systems, which degrees of freedom are the most informative about their non-equilibrium nature. Here, we introduce an alternative, tracking-free approach that overcomes these difficulties via an unsupervised analysis of the Brownian movie. We develop a dimensional reduction scheme selecting a basis of modes based on dissipation. Subsequently, we learn the non-equilibrium dynamics, thereby estimating the entropy production rate and time-resolved force maps. After benchmarking our method against a minimal model, we illustrate its broader applicability with an example inspired by active biopolymer gels. A tracking-free approach by Gnesotto et al. is developed to distinguish active and thermal fluctuations in microscopy data of non-equilibrium systems such as cell membranes. The method relies on a dimensional reduction scheme revealing a hierarchy of the most dissipative dynamical components.
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页数:9
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