The thermodynamics of computational copying in biochemical systems

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.

Accepted version

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• Fundamental costs in the production and destruction of persistent polymer copies
• Kinetic Proofreading and the Limits of Thermodynamic Uncertainty
• Physical constraints in intracellular signaling: the cost of sending a bit
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• Unified framework for the entropy production and the stochastic interaction based on information geometry
• What we learn from the learning rate
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• Multi-dimensional biochemical information processing of dynamical patterns
• Thermodynamic inference in partially accessible Markov networks: A unifying perspective from transition-based waiting time distributions
• Dissipation at limited resolutions: Power law and detection of hidden dissipative scales
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• Exploiting bias in optimal finite-time copying protocols
• Universal Scaling Laws of Absorbing Phase Transitions in Artificial Deep Neural Networks
• The thermodynamics of quasi-deterministic digital computers
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• Brownian-motion approach to statistical mechanics: Langevin equations, fluctuations, and timescales
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• Information Thermodynamics for Deterministic Chemical Reaction Networks
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