A computational model simulating how DNA sequence repeats influence gene inversions and strand bias in bacterial genomes.
This project investigates how different DNA repeat configurations (direct, inter-inverted, intra-inverted) affect gene-strand bias through inversion events. The model demonstrates:
- Null Model Behavior: Without selection pressure, gene-strand bias stabilizes at 50-50 distribution
- Selection Impact: Imposing constraints through inversion disparity scores maintains existing strand bias
- Evolutionary Insights: Provides framework for understanding bacterial adaptation through chromosomal rearrangements
- Circular bacterial genome simulation with configurable parameters:
- Ori/Ter positions
- Repeat distributions (direct, inter/intra-inverted)
- Gene allocation between strands
- Two operational modes:
- Null Model: Unconstrained inversions
- Selection Model:
- Gene count imbalance thresholds
- Inversion Disparity Score (IDS) calculations
- Penalty limit constraints
- Analytical tools for:
- Strand bias quantification
- Inversion event tracking
- Fitness impact assessment
Key Configurable Parameters (via config.yaml):
inversion_disparity_limit: 10/25/no-limitrepeat_distribution: Random/Clusteredfitness_function: Normal/Exponential distribution
- Rapid convergence to 50-50 strand distribution
- Demonstrates baseline evolutionary pressure from unconstrained inversions
| Penalty Limit | Strand Bias Stability | Inversion Frequency |
|---|---|---|
| 10 | High | Low |
| 25 | Moderate | Medium |
| no-limit | Low | High |
Critical Finding: Higher penalty limits permit larger inversions while maintaining ancestral strand bias patterns
Planned model extensions:
- GC skewness integration
- Transcription-replication collision modeling
- Horizontal gene transfer simulations
MIT License - see LICENSE for details.