Self-similarity and the maximum entropy principle in the genetic code.

Area of Science:

• Bioinformatics
• Information Theory
• Genetics

Background:

• The genetic code exhibits anomalies like non-contiguous serine codons and zero-redundancy amino acids.
• These features challenge conventional error-correction and structural interpretations.

Purpose of the Study:

• To investigate the genetic code's structure and information content.
• To reconcile puzzling anomalies by incorporating information theory and maximum entropy principles.

Main Methods:

• Analyzing the genetic code through information-theoretic dimensionality.
• Applying the principle of maximum entropy and algorithmic information complexity.
• Utilizing exponentiation mapping for data transformation.

Main Results:

• The genetic code demonstrates self-similarity across scales, consistent with non-integer dimensionality.
• Maximum entropy principle explains element scrambling and maximum algorithmic information complexity.
• New constraints derived from these principles likely explain codon group non-uniformity and lack of redundancy.

Conclusions:

• Viewing the genetic code through information-theoretic dimensionality and maximum entropy offers a novel explanation for its structure.
• These principles provide insights into the non-uniformity and error-handling strategies within the genetic code.