Related Experiment Video
Updated: Jul 16, 2025
08:12
High-throughput Screening for Protein-based Inheritance in S. cerevisiae
Published on: August 8, 2017
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The principle "like begets like" in algebra-matrix genetics and code biology
1Mechanical Engineering Research Institute of Russian Academy of Sciences, M. Kharitonievskiy pereulok, 4, 101990, Moscow, Russia.
Bio Systems
|September 10, 2023
Summary
This study reveals hidden probability rules in genomic DNA sequences using a novel hierarchy binary stochastics method. These findings link DNA
Area of Science:
- Genomics and Bioinformatics
- Molecular Biology
- Information Theory
Background:
- The Watson-Crick model of DNA highlights base-pair complementarity (purine-pyrimidine) for genetic information replication.
- The genetic code involves a richer system of binary oppositions beyond simple base pairing, suggesting deeper coding principles.
- Understanding these oppositions is key to deciphering the complex organization of genetic information.
Purpose of the Study:
- To analyze emergent properties of binary oppositions within the genetic code ensemble.
- To investigate the quasi-stochastic organization of binary sequences in genomic single-stranded DNA.
- To explore potential connections between DNA information sequences and inherited physiological phenomena.
Main Methods:
- Application of the author's hierarchy binary stochastics (HBS-method) to analyze genomic DNA sequences.
- Identification of probability rules and fractal-like probability trees within binary sequences.
- Algorithmic construction of probability matrices representing multidimensional hyperbolic numbers.
Main Results:
- Discovery of hidden probability rules and dichotomous fractal-like structures in genomic DNA binary sequences.
- Establishment of connections between inherited bodily dichotomies and probability dichotomies in DNA.
- Demonstration that DNA binary oppositions can be represented by multidimensional hyperbolic numbers.
Conclusions:
- The study proposes a novel framework for understanding genetic information through binary oppositions and multidimensional numbers.
- A unified algebra-numeric certification of genomes and genes based on these systems is suggested.
- Potential implications for understanding inherited physiological phenomena and deep neural networks are noted.
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