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Updated: Feb 8, 2026

Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion
Published on: February 10, 2023
Physical factors in origin of genetic code.
12774 Sunnybridge Dr., Burnaby, Canada.
This study proposes a physical model for the origin of the genetic code (GC) within open thermodynamic systems (OTS). It explains codon formation and amino acid assignment through energy dynamics and evolutionary steps.
Area of Science:
- Thermodynamics
- Biophysics
- Origin of Life Research
Background:
- The origin of the genetic code (GC) remains a fundamental question in understanding life's beginnings.
- Existing models often lack a unified physical framework to explain the transition from simple molecules to a complex coding system.
- Open thermodynamic systems (OTS) provide a relevant context for studying early life processes driven by energy flow.
Purpose of the Study:
- To propose a consistent physical approach for the origin of the genetic code (GC).
- To explain the formation of the 64 codons and assignment to 20 amino acids using energy development in an open thermodynamic system (OTS).
- To outline the evolutionary steps of the GC based on physical principles and conserved quantities.
Main Methods:
- Modeling the impact of the physical environment as random energy-based factors within an OTS.
- Analyzing the formation of a triple-based energy structure via bifurcation points and quaternary nucleotide flow.
- Introducing a three-step molecular machine for codon-to-amino acid transformation based on dynamic energy balance.
Main Results:
- A two-part approach detailing the emergence of the 64 codons and the subsequent evolutionary steps.
- Demonstration of GC evolution through binary logic, energy space segregation, and phase splitting.
- Validation of the simulated GC properties against observable data using conserved physical quantities.
Conclusions:
- The physical approach provides a consistent framework for the origin and evolution of the genetic code.
- Energy dynamics in open thermodynamic systems are crucial for understanding the emergence of biological information.
- The proposed model aligns with evolutionary principles and offers testable predictions for future research.
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