Abstract
Diseases are deeply implicated in the aberrant expression of microRNA (miRNA) genes. The RNA Wave 2000 dogma consists of four criteria, as first described in Chap. 2. Again, (1) miRNA genes induce transcriptional and posttranscriptional silencing through a networking architecture; (2) RNA information supplied by miRNA genes as mobile genetic elements expand to intra- and intercellularly, intra- and interorganically, and intra- and interspecies under the circulation of life to the terrestrial environment; (3) mobile miRNAs can self-proliferate; and (4) cells contain two types of information as a resident and genomic miRNA genes. Given these criteria, diseases are programmed by miRNA genetic information. Abnormal miRNA information induces system errors. In Darwinism, spontaneous mutations and recombination of genomic DNA can cause diseases. Transferable miRNA information in exosomes is passed from mother to child via breast milk, placenta, etc. Although genomic miRNA genes in the DNA genome obey Mendel’s laws, movable miRNA genes are absent from both Mendelian and Darwinian rules. Therefore, the acquired phenotype is inheritable, and the phenotype of offspring is easily reprogrammed. Beyond Darwinism and Mendelian, reprogrammed evolution as a new age is directed by the programming of the miRNA gene language. To apply the miRNA gene information algorithm to the properties of RNA Wave, the a priori miRNA gene information was converted into binary qubits as physicochemical characters, and mathematically, the electron spins of miRNAs were measured and computed in a matrix. Bit-to-bit coherence of miRNAs was recorded as the static (single) nexus score (SNS) or dynamic (double) nexus score (DNS). Since alterations in miRNA expression were both upregulation and downregulation, the binary qubits of coherence miRNA expression changes were further calculated as SNS + change (SNSC) and DNS + change (DNSC). Subsequently, DNSC has been correlated with human disease. Human disease phenotypes will be simulated by DNSC with miRNA language and artificial intelligence (AI) computing algorithm (MIRAI) in the future. The MIRAI can reduce overall healthcare cost containment.
I shall only say that the justification lies in the fact that human memory is necessarily limited.
Turing, A. M.
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Fujii, Y.R. (2023). Programmed Evolution by miRNA Memory. In: The MicroRNA 2000 Transformer. Springer, Singapore. https://doi.org/10.1007/978-981-99-3165-1_6
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