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Register a new userQuantum approach to adaptive mutations. Didactic introduction
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Vasily Ogryzko
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A didactic introduction, dated by 1999, to the ideas of the papers arXiv:q-bio/0701050 and arXiv:0704.0034
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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.
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.
Data from a long time evolution experiment with Escherichia Coli and from a large study on copy number variations in subjects with european ancestry are analyzed in order to argue that mutations can be described as Levy flights in the mutation space. These Levy flights have at least two components: random single-base substitutions and large DNA rearrangements. From the data, we get estimations for the time rates of both events and the size distribution function of large rearrangements.
A precise estimate of allele and haplotype polymorphism is of great interest for theoretical population genetics, but also practical issues, such as bone marrow registries. Allele polymorphism is driven mainly by point mutations, while haplotype polymorphism is also affected by recombination events. Even in the simple case of two loci in a haploid individual, there is currently no good estimate of the number of haplotypes as a function of the mutation and recombination rates. We here propose such an estimate and show that the common approximation that recombination can be treated as mutations is limited to recombination rates of the same order as the mutation rate. Beyond this regime, the total number of haplotypes is much lower than expected from the approximation above. Moreover, in contrast with mutations, the number of haplotypes does not grow linearly with the population size. We apply this new estimate to very large-scale human haplotype frequencies from human populations to show that the current estimated haplotype recombination rate in the HLA region is underestimated. This high recombination rate may be the source of HLA haplotype extreme polymorphism.
Both external environmental selection and internal lower-level evolution are essential for an integral picture of evolution. This paper proposes that the division of internal evolution into DNA/RNA pattern formation (genotype) and protein functional action (phenotype) resolves a universal conflict between fitness and evolvability. Specifically, this paper explains how this universal conflict drove the emergence of genotype-phenotype division, why this labor division is responsible for the extraordinary complexity of life, and how the specific ways of genotype-phenotype mapping in the labor division determine the paths and forms of evolution and development.
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