General theory for localizing the where and when of entropy production meets single-molecule experiments

Abstract

The laws of thermodynamics apply to biophysical systems on the nanoscale as described by the framework of stochastic thermodynamics. This theory provides universal, exact relations for quantities like work, which have been verified in experiments where a fully resolved description allows direct access to such quantities. Complementary studies consider partially hidden, coarse-grained descriptions, in which the mean entropy production typically is not directly accessible but can be bounded in terms of observable quantities. Going beyond the mean, we introduce a fluctuating entropy production that applies to individual trajectories in a coarse-grained description under time-dependent driving. Thus, this concept is applicable to the broad and experimentally significant class of driven systems in which not all relevant states can be resolved. We provide a paradigmatic example by studying an experimentally verified protein unfolding process. As a consequence, the entire distribution of the coarse-grained entropy production rather than merely its mean retains spatial and temporal information about the microscopic process. In particular, we obtain a bound on the distribution of the physical entropy production of individual unfolding events.

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Cited by in corpus (8)

• Perspective: Time irreversibility in systems observed at coarse resolution
• Entropy estimation for partially accessible Markov networks based on imperfect observations: Role of finite resolution and finite statistics
• Conditional fluctuation theorems and entropy production for monitored quantum systems under imperfect detection
• Thermodynamic bounds and error correction for faulty coarse graining
• From observed transitions to hidden paths in Markov networks
• Compensating random transition-detection blackouts in Markov networks
• Experimentally accessible measurement of irreversibility in stochastic systems by categorizing single-molecule displacements
• A pedestrian's approach to large deviations in semi-Markov processes with an application to entropy production