Deterioration of Information Transcription Modeled with Maxwell’s Demon Reveals Fidelity Decay in Cellular Aging

The gradual decay of information during replication lies at the heart of biological ageing, but understanding this process at a fundamental level remains a significant challenge. Maggie Williams from University of Maryland, Baltimore County, Emery Doucet from University of Massachusetts, Boston, and Sebastian Deffner from University of Maryland, Baltimore County, investigate this deterioration by modelling information transcription as a simplified, yet powerful, information-theoretic process. Their work employs a conceptual Maxwell’s demon, a thought experiment in physics, to analyse how fidelity decreases when copying information, mirroring the errors that accumulate during DNA replication. By examining the statistics of work extraction and information transfer, the team provides new insights into the fundamental limits of accurate information copying and offers a holistic view of the physical principles governing cellular ageing.

Demon’s Dementia, Quantifying Memory Loss Impact

This research establishes a theoretical framework for understanding how memory loss impacts the efficiency of Maxwell’s demon, demonstrating that even small errors in information storage significantly reduce its ability to sort particles and create a temperature difference. The team developed a model where the demon’s memory has a limited retention time, leading to probabilistic rather than precise sorting. This equation allows scientists to calculate key thermodynamic properties, such as entropy production and efficiency, as a function of memory retention and error rate. The analysis reveals a critical threshold below which the demon’s performance degrades, effectively behaving like a passive system and no longer violating the second law of thermodynamics. The team explores the relationship between memory capacity, processing speed, and achievable temperature difference, providing insights into the limits of information-driven thermodynamics. This work rigorously applies mathematical principles to the effects of memory on Maxwell’s demon, quantitatively assessing the trade-off between information storage and thermodynamic performance, and demonstrating the link between cognitive limitations and the validity of the second law.

Stochasticity Drives Biological System Dynamics

This collection of resources explores the use of stochastic, or random, mathematical models to understand biological processes, particularly at the molecular level. This approach contrasts with deterministic models, which assume fixed outcomes, and focuses on systems that are not at equilibrium, a crucial characteristic of living systems. A central concept is that of information ratchets, systems that can extract useful work or perform computations by exploiting fluctuations and information, providing insight into how molecular machines and biological processes operate efficiently. The material emphasizes Markov chains, particularly time-inhomogeneous Markov chains where transition probabilities change over time.

The Fokker-Planck equation and master equation describe the time evolution of probability distributions for continuous and discrete stochastic processes, respectively. Gillespie’s algorithm is an exact method for simulating chemically reacting systems. Fluctuation theorems connect the probabilities of observing different amounts of work or heat flow. This material is geared towards understanding a wide range of biological phenomena, including gene expression, protein synthesis, molecular machines, chemical reactions in cells, and error correction in biological systems.

Thermodynamic Limits of Transcription Fidelity and Aging

This research presents a novel framework for understanding cellular aging as an information-theoretic process, drawing parallels between DNA transcription and the dynamics of Maxwell’s demon. By modeling information transcription as a stochastic process, the team analyzed the relationship between extractable work, transcription fidelity, and mutual information within a simplified system. The study establishes a connection between thermodynamic principles and the fidelity of DNA transcription, providing a new perspective on the origins of cellular aging. The authors acknowledge that the model simplifies complex biological realities and that future work could incorporate environmental variability. They rigorously demonstrated the convergence of the model to a steady state, confirming its stability over time. This work lays the foundation for connecting stochastic thermodynamics with microscopic biological processes, offering a promising avenue for future research into the fundamental mechanisms of aging and information processing within cells.