The thermodynamics of computational copying in biochemical systems

TL;DR

This paper investigates the thermodynamic efficiency of biochemical copying in cells, revealing fundamental limits, trade-offs, and potential for optimal devices, bridging biological processes and computational thermodynamics.

Contribution

It establishes a thermodynamic framework for biochemical readout networks, identifying bounds and trade-offs in their efficiency and accuracy, and proposes experimental implementations.

Findings

Biochemical readout networks generate correlations that set a lower bound on dissipation.

These networks cannot reach the thermodynamic bound, even with slow reactions or weak driving.

Biomolecular reactions could be used in thermodynamically optimal devices with external fuel manipulation.

Abstract

Living cells use readout molecules to record the state of receptor proteins, similar to measurements or copies in typical computational devices. But is this analogy rigorous? Can cells be optimally efficient, and if not, why? We show that, as in computation, a canonical biochemical readout network generates correlations; extracting no work from these correlations sets a lower bound on dissipation. For general input, the biochemical network cannot reach this bound, even with arbitrarily slow reactions or weak thermodynamic driving. It faces an accuracy-dissipation trade-off that is qualitatively distinct from and worse than implied by the bound, and more complex steady-state copy processes cannot perform better. Nonetheless, the cost remains close to the thermodynamic bound unless accuracy is extremely high. Additionally, we show that biomolecular reactions could be used in thermodynamically optimal devices under exogenous manipulation of chemical fuels, suggesting an experimental system for testing computational thermodynamics.