Energy and information flows in autonomous systems

TL;DR

This paper reviews recent advances in understanding energy and information flows in autonomous two-component molecular machines using thermodynamic and Markovian frameworks, highlighting their operation modes and bounds on energy efficiency.

Contribution

It introduces a unified approach to analyze thermodynamics and information flow in bipartite systems, applying it to molecular sensors and coupled machines.

Findings

Local energy and information flow analysis clarifies subsystem interactions.

Tighter energy bounds are derived for molecular sensors.

Operational modes range from power transduction to information engines.

Abstract

Multi-component molecular machines are ubiquitous in biology. We review recent progress on describing their thermodynamic properties using autonomous bipartite Markovian dynamics. The first and second laws can be split into local versions applicable to each subsystem of a two-component system, illustrating that one can not only resolve energy flows between the subsystems but also information flows quantifying how each subsystem's dynamics influence the joint system's entropy balance. Applying the framework to molecular-scale sensors allows one to derive tighter bounds on their energy requirement. Two-component strongly coupled machines can be studied from a unifying perspective quantifying to what extent they operate conventionally by transducing power or like an information engine by generating information flow to rectify thermal fluctuations into output power.