Colored petri nets to model gene mutation and amino acids classification

Area of Science:

• Molecular Biology
• Computational Biology
• Bioinformatics

Background:

• The genetic code translates DNA codons into amino acid sequences for protein synthesis.
• Modeling genetic processes aids in understanding molecular biology.
• Colored Petri Nets (CPN) are effective for modeling discrete event dynamic systems (DEDS) in biological contexts.

Purpose of the Study:

• To develop a CPN model for classifying amino acids based on the genetic code.
• To create an improved CPN model for amino acid classification.
• To design a CPN model for classifying gene mutation types by analyzing DNA bases and amino acid codons.

Main Methods:

• Utilized the genetic code table to inform the CPN model.
• Developed a CPN model for amino acid classification.
• Extended the model to classify gene mutations by comparing DNA sequences and resulting amino acid chains.
• Applied the CPN models to analyze DNA bases and codons.

Main Results:

• Successfully modeled amino acid classification using CPN.
• Presented an enhanced CPN model for improved amino acid classification.
• Developed a novel CPN model to classify gene mutations.
• Demonstrated the model's utility in predicting the impact of mutations on protein structure and function.

Conclusions:

• The proposed CPN models provide an effective computational approach for analyzing genetic information.
• The gene mutation classification model can predict functional and structural changes in proteins.
• CPN modeling offers a valuable tool for molecular biology research and genetic analysis.