A quantum mechanical model of adaptive mutation

Michelle X. Yang

Ivan B. Djordjević

Fatemeh Ghasemi, Arash Tirandaz

The adaptive mutation phenomenon has been drawing the attention of biologists for several decades in evolutionist community. In this study, we propose a quantum mechanical model of adaptive mutation based on the implications of the theory of open quantum systems. We survey a new framework that explain how random point mutations can be stabilized and directed to be adapted with the stresses introduced by the environments according to the microscopic rules dictated by constraints of quantum mechanics. We consider a pair of entangled qubits consist of DNA and mRNA pair, each coupled to a distinct reservoir for analyzing the spreed of entanglement using time-dependent perturbation theory. The reservoirs are physical demonstrations of the cytoplasm and nucleoplasm and surrounding environments of mRNA and DNA, respectively. Our predictions confirm the role of the environmental-assisted quantum progression of adaptive mutations. Computing the concurrence as a measure that determines to what extent the bipartite DNA-mRNA can be correlated through entanglement, is given. Preventing the entanglement loss is crucial for controlling unfavorable point mutations under environmental influences. We explore which physical parameters may affect the preservation of entanglement between DNA and mRNA pair systems, despite the destructive role of interaction with the environments.

John Steven Torday

inquantio

• A study published on bioRxiv presents a completely new "Entangled Evolution" model that integrates quantum game theory (the CHSH game) and quantum walks. • Computer simulation results demonstrate that when quantum entanglement operates within an E. coli population, it achieves successful adaptive mutations with an approximately 85% probability, surpassing the limits of classical probability. • This is a revolutionary algorithmic approach that mathematically proves the possibility that biological evolution is not a simple random search, but rather a simultaneous multi-pathway exploration utilizing quantum mechanical superposition. [Quantum Biology Society] Adaptive mutation, a phenomenon where organisms drastically increase their beneficial mutation rates when faced with environmental stress, has long been a mystery difficult to fully explain through classical random mutation evolutionary theory. Recently, groundbreaking research mathematically demonstrating that subatomic quantum entanglement and quantum walk mechanisms may be involved in the evolutionary process of bacteria has been published, bringing a fresh shock to the field of evolutionary biology. The paper titled "Entangled Evolution Model: Application to Adaptive Mutation," released on the preprint repository bioRxiv, presents an entirely new approach to explaining biological evolution. Through this independent study, researcher Patrick Ross simulated the mutation dynamics of E. coli by fusing quantum game theory (the CHSH game) and quantum walk algorithms into the Lotka-Volterra framework, a classical ecological population dynamics model. ■ An 85% Mutation Success Rate Breaking Classical Limits The simulation results were remarkable. Assuming that quantum entanglement operates within the E. coli population, the bacteria exhibited patterns akin to non-locally coordinating the direction of their mutations. Through this, it was mathematically proven that the entangled bacterial population could achieve successful, environment-adapting mutations with a high probability reaching 85%, far exceeding the theoretical limit of classical probability (approximately 75%). ■ From Random Search to Simultaneous Multi-Pathway Exploration This research strongly suggests that the evolutionary adaptation of living organisms is not a blind, random accumulation of chemical errors, but rather a highly optimized process that actively utilizes quantum mechanical superposition mechanisms to simultaneously explore multiple pathways on genetic networks. This revolutionary algorithmic framework, which reinterprets the speed and efficiency of evolution by borrowing the computational framework of quantum information science, is expected to provide a crucial theoretical foundation for exploring the role of quantum effects within biological systems moving forward.