I compare two quantum-theoretical approaches to the phenomenon of adaptive mutations, termed here Q-cell and Q-genome. I use 'fluctuation trapping' model as a general framework. I introduce notions of R-error and D-error and argue that the 'fluctuation trapping' model has to employ a correlation between the R- and D- errors. Further, I compare how the two approaches can justify the R-D-error correlation, focusing on the advantages of the Q-cell approach. The positive role of environmentally induced decoherence (EID) on both steps of the adaptation process is emphasized. A starving bacterial cell is proposed to be in an einselected state. The intracellular dynamics in this state has a unitary character and I propose to interpret it as 'exponential growth in imaginary time', analogously to the commonly considered 'diffusion' interpretation of the Schroedinger equation. Addition of a substrate leads to Wick rotation and a switch from 'imaginary time' reproduction to a 'real time' reproduction regime. Due to the variations at the genomic level (such as base tautomery), the starving cell has to be represented as a superposition of different components, all 'reproducing in imaginary time'. Adidtion of a selective substrate, allowing only one of these components to amplify, will cause Wick rotation and amplification of this component, thus justifying the occurence of the R-D-error correlation. Further ramifications of the proposed ideas for evolutionary theory are discussed.
I compare two quantum-theoretical approaches to the phenomenon of adaptive mutations, termed here Q-cell and Q-genome. I use 'fluctuation trapping' model as a general framework. I introduce notions of R-error and D-error and argue that the 'fluctuation trapping' model has to employ a correlation between the R- and D- errors. Further, I compare how the two approaches can justify the R-D-error correlation, focusing on the advantages of the Q-cell approach. The positive role of environmentally induced decoherence (EID) on both steps of the adaptation process is emphasized. A starving bacterial cell is proposed to be in an einselected state. The intracellular dynamics in this state has a unitary character and I propose to interpret it as 'exponential growth in imaginary time', analogously to the commonly considered 'diffusion' interpretation of the Schroedinger equation. Addition of a substrate leads to Wick rotation and a switch from 'imaginary time' reproduction to a 'real time' reproduction regime. Due to the variations at the genomic level (such as base tautomery), the starving cell has to be represented as a superposition of different components, all 'reproducing in imaginary time'. Adidtion of a selective substrate, allowing only one of these components to amplify, will cause Wick rotation and amplification of this component, thus justifying the occurence of the R-D-error correlation. Further ramifications of the proposed ideas for evolutionary theory are discussed.
The Darwinian paradigm of biological evolution is based on the separability of the variation and selection processes. As a result, the population thinking had always been an integral part of the Darwinian approach. I propose an alternative scheme of biological adaptation. It is based on appreciation of limits of what we can observe considering an individual biological object. This leads to a possibility for the adaptation process to occur on the level of a single object, as a 'selection among virtual states of the organism'. I discuss the application of this idea to the phenomenon of adaptive mutations in bacteria.
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.
Abstract Adaptive mutation, the phenomenon where organisms appear to increase beneficial mutation rates in response to environmental stress, has challenged the traditional understanding of random mutations in evolutionary biology. Here, we present a novel computational model, the Entangled Evolution Model, which integrates quantum game theory and quantum walks to provide a new approach to understanding mutation dynamics. By applying the Clauser-Horne-Shimony-Holt (CHSH) quantum game within a modified Lotka-Volterra framework, we simulate how quantum entanglement can accelerate the rate of adaptive mutations in Escherichia coli populations. Our simulations reveal that entangled bacterial populations achieve successful adaptive mutations with an 85% probability, significantly surpassing the classical theoretical maximum. These results introduce a new perspective on computational approaches to evolutionary adaptation, offering an efficient algorithmic framework to explore the role of quantum effects in biological systems.
Application of quantum principles to living cells requires a new approximation of the full quantum mechanical description of intracellular dynamics. We discuss what principal elements any such good approximation should contain. As one such element, the notion of "Catalytic force" Cf is introduced. Cf is the effect of the molecular target of catalysis on the catalytic microenvironment that adjusts the microenvironment towards a state that facilitates the catalytic act. This phenomenon is experimentally testable and has an intriguing implication for biological organization and evolution, as it amounts to "optimization without natural selection of replicators". Unlike the statistical-mechanical approaches to self-organization, the Cf principle does not encounter the problem of "tradeoff between stability and complexity" at the level of individual cell. Physically, the Cf is considered as a harmonic-like force of reaction, which keeps the state of the cell close to the ground state, defined here as a state where enzymatic acts work most efficiently. Ground state is subject to unitary evolution, and serves as a starting point in a general strategy of quantum description of intracellular processes, termed here "Euclidean approach". The next step of this strategy is transition from the description of ground state to that one of growing state, and we suggest how it can be accomplished using arguments from the fluctuation-dissipation theorem. Finally, given that the most reliable and informative observable of an individual cell is the sequence of its genome, we propose that the non-classical correlations between individual molecular events at the single cell level could be easiest to detect using high throughput DNA sequencing.
• 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.
We explore a quantum mechanical framework for modeling mutation dynamics in viral genomes. Motivated by experimental observations, such as interference-like mutation patterns and an inverse relationship between genome size and per-site mutation rate, we represent the viral genome as a superposition state in a high-dimensional Hilbert space, and model mutations as quantum operators acting on this state. This is not a microscopic theory of replication but an effective, physically inspired model aimed at capturing non-classical features such as interference and entanglement in sequence evolution. We introduce structured mutation operators diagonal, random, and spatially correlated, and study their action on quantum sequence states using numerical simulations. Correlated operators with phase structure produce interference patterns in mutation probabilities, analogous to the electron double-slit experiment. Entangled mutation dynamics, modeled via cluster-state-like correlations, alter the scaling behavior of the per-site mutation rate with genome size. We show that while classical models predict a constant per-site mutation rate for small genomes, entangled models yield size-dependent rates that decrease as L −α , where α > 0 reflects the strength of correlation. This matches trends observed in RNA viruses with genomes below ∼ 30 kb. Finally, we propose two experimental tests: (1) detection of replication-speed-dependent interference fringes using modified viral polymerases, and (2) ensemble-level coherence signatures measurable by nuclear magnetic resonance spectroscopy. Our results offer a testable hypothesis that quantum correlations may influence mutation dynamics in viral genomes below 30 kb in size.