Evolutionary trajectory estimation via replication simulation of coronavirus spike gene based on random mutation and...

Area of Science:

• Virology
• Computational Biology
• Evolutionary Biology

Background:

• Viruses evolve rapidly through mutation and selection, influencing adaptation and cross-species transmission.
• Understanding these evolutionary dynamics is crucial for predicting viral behavior and public health threats.

Purpose of the Study:

• To develop a simulation framework with a GUI to model viral evolution.
• To investigate the impact of random mutation and similarity-based selection on viral adaptation.
• To explore plausible viral evolutionary pathways and potential intermediates in zoonotic spillover.

Main Methods:

• Designed a simulation framework with a graphical user interface (GUI).
• Implemented random mutation and similarity-based selection mechanisms.
• Modeled viral sequence evolution toward a target sequence by recursive selection of top-N similar sequences.

Main Results:

• Simulations of SARS-CoV-2 evolution toward Omicron (BA.1) showed plateau-like similarity trajectories.
• Increased substitution rates accelerated the attainment of evolutionary plateaus.
• Generated intermediate spike sequences mirrored real-world viral lineages (e.g., B, B.1.1.529, BA.1).
• The model replicated divergent evolutionary outcomes for PEDV under different selection pressures.

Conclusions:

• The simplified model provides insights into viral evolutionary paths and potential zoonotic spillover intermediates.
• The framework successfully models key aspects of viral evolution, including lineage diversification.
• Future enhancements like recombination and population dynamics could improve predictive power.