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From Stochastic Thermodynamics to Thermodynamic Inference

Banff International Research Station for Mathematical Innovation and Discovery

Stochastic thermodynamics provides a universal framework for analyzing nano- and micro-sized non-equilibrium systems. Prominent examples are single molecules, molecular machines, colloidal particles in time-dependent laser traps and biochemical networks. Thermodynamic notions like work, heat and entropy can be identified on the level of individual fluctuating trajectories. They obey universal relations like the fluctuation theorem. Thermodynamic inference as a general strategy uses consistency constraints derived from stochastic thermodynamics to infer otherwise hidden properties of non-equilibrium systems. As a paradigm for thermodynamic inference, the thermodynamic uncertainty relation provides a lower bound on the entropy production through measurements of the dispersion of any current in the system. Likewise, it quantifies the cost of temporal precision for biomolecular processes and provides a model-free bound on the thermodynamic efficiency of molecular motors. For a review: U. Seifert, Annu. Rev. Condens. Matter Phys. 10, 171-192, 2019

Mathematics; Biology and other natural sciences; Information and communication, circuits

video/mp4

Author affiliation: University of Stuttgart

2021-01-24

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/77165

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/