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Register a new userOn two quantum approaches to adaptive mutations in bacteria
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Vasily Ogryzko
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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.
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A didactic introduction, dated by 1999, to the ideas of the papers arXiv:q-bio/0701050 and arXiv:0704.0034
This thesis is aimed at studying mutations, understood as trajectories in the DNA configuration space. An evolutive model of mutations in terms of Levy flights is proposed. The parameters of the model are estimated by means of data from the Long-Term Evolution Experiment (LTEE) with {it E. Coli} bacteria. The results of simulations on competition of clones, mean fitness, etc are compared with experimental data. We discuss the qualitative analogy found between the bacterial mutator phenotype and the cancerous cells. The role of radiation as source of mutations is analyzed. We focus on the case of Radons decay in the lungs in breathing.
This is a supplement to the paper arXiv:q-bio/0701050, containing the text of correspondence sent to Nature in 1990.
Levy flights in the space of mutations model time evolution of bacterial DNA. Parameters in the model are adjusted in order to fit observations coming from the Long Time Evolution Experiment with E. Coli.
The COVID-19 pandemic has lead to a worldwide effort to characterize its evolution through the mapping of mutations in the genome of the coronavirus SARS-CoV-2. Ideally, one would like to quickly identify new mutations that could confer adaptive advantages (e.g. higher infectivity or immune evasion) by leveraging the large number of genomes. One way of identifying adaptive mutations is by looking at convergent mutations, mutations in the same genomic position that occur independently. However, the large number of currently available genomes precludes the efficient use of phylogeny-based techniques. Here, we establish a fast and scalable Topological Data Analysis approach for the early warning and surveillance of emerging adaptive mutations based on persistent homology. It identifies convergent events merely by their topological footprint and thus overcomes limitations of current phylogenetic inference techniques. This allows for an unbiased and rapid analysis of large viral datasets. We introduce a new topological measure for convergent evolution and apply it to the GISAID dataset as of February 2021, comprising 303,651 high-quality SARS-CoV-2 isolates collected since the beginning of the pandemic. We find that topologically salient mutations on the receptor-binding domain appear in several variants of concern and are linked with an increase in infectivity and immune escape, and for many adaptive mutations the topological signal precedes an increase in prevalence. We show that our method effectively identifies emerging adaptive mutations at an early stage. By localizing topological signals in the dataset, we extract geo-temporal information about the early occurrence of emerging adaptive mutations. The identification of these mutations can help to develop an alert system to monitor mutations of concern and guide experimentalists to focus the study of specific circulating variants.
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