Petri net modeling of high-order genetic systems using grammatical evolution

Area of Science:

• Computational biology
• Systems biology
• Genetics

Background:

• Understanding DNA sequence variations' impact on human health is crucial for complex disease management.
• Previous work established a Petri net approach for modeling biochemical networks linked to genetic disease susceptibility.

Purpose of the Study:

• To evaluate the Petri net approach's capability in identifying biochemical networks associated with higher-order nonlinear interactions of three DNA sequence variations.
• To assess the model-building approach's effectiveness for complex genetic interactions.

Main Methods:

• Utilized a hierarchical dynamic systems approach based on Petri nets.
• Employed grammatical evolution, an evolutionary computation method, for searching optimal Petri net models.
• Tested the approach against genetic models involving nonlinear interactions between three DNA sequence variations.

Main Results:

• The Petri net approach successfully identified biochemical networks consistent with disease susceptibility due to three DNA sequence variations.
• The model-building approach generated good, though not perfect, Petri net models for these higher-order interactions.
• The study highlights the approach's potential for complex genetic disease modeling.

Conclusions:

• The developed Petri net approach is capable of modeling complex genetic interactions influencing disease susceptibility.
• Further algorithmic improvements are needed for optimal performance in high-dimensional genetic interaction problems.
• This work contributes to advancing computational methods for understanding gene-environment interactions in human diseases.