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Updated: Aug 10, 2025

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

Published on: June 24, 2019

Next-generation development and application of codon model in evolution.

Manoj Kumar Gupta¹, Ramakrishna Vadde¹

¹Department of Biotechnology & Bioinformatics, Yogi Vemana University, Kadapa, Andhra Pradesh, India.

Frontiers in Genetics

|February 13, 2023

Summary

This summary is machine-generated.

Codon substitution models offer powerful insights into evolutionary history and selective pressures. This review explores developing more efficient models for genome-scale evolutionary analysis.

Keywords:

codon substitution models empirical model evolution mechanistic model phylogenetic reconstruction semi-emperical models

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Residue-specific Incorporation of Noncanonical Amino Acids into Model Proteins Using an Escherichia coli Cell-free Transcription-translation System

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Published on: August 1, 2016

Area of Science:

Evolutionary biology
Bioinformatics
Genomics

Background:

Nucleotide, amino acid, and codon substitution models are used to study evolutionary history.
Codon substitution models are the most powerful but computationally demanding.
Current models face limitations in genome-scale analyses due to computational cost.

Purpose of the Study:

To review the development of codon substitution models.
To explore methods for creating more robust and less computationally demanding models.
To enhance genome-scale evolutionary analyses.

Main Methods:

Review of existing literature on codon substitution models.
Analysis of the computational demands and robustness of different models.
Exploration of strategies for model optimization.

Main Results:

Codon models accurately detect selection regimes, codon usage bias, and phylogenetic relationships.
Existing models are powerful but computationally intensive for large-scale studies.
Developing less demanding models is crucial for broader application.

Conclusions:

Codon substitution models are vital for understanding molecular evolution.
Future research should focus on optimizing models for computational efficiency.
Improved models will advance fields like conservation, breeding, and medicine.