SIApopr: a computational method to simulate evolutionary branching trees for analysis of tumor clonal evolution

Area of Science:

• Computational Biology
• Evolutionary Biology
• Genetics

Background:

• Stochastic branching processes are fundamental to modeling cell populations.
• Understanding clonal evolution and mutation dynamics is crucial in cancer research and evolutionary biology.
• Existing simulation tools may lack flexibility or computational efficiency for complex scenarios.

Purpose of the Study:

• To introduce SIApopr, an R package for simulating stochastic branching processes.
• To provide a flexible and efficient tool for modeling clonal evolution with driver and passenger mutations.
• To enable users to easily adapt or create custom simulation models.

Main Methods:

• The SIApopr package utilizes the Gillespie Stochastic Simulation Algorithm.
• It is implemented in C++ for enhanced computational speed.
• The package supports both time-homogeneous and inhomogeneous stochastic branching processes.

Main Results:

• SIApopr allows for the simulation of clonal evolution under the infinite-allele assumption.
• It accommodates a large number of cell types and diverse evolutionary scenarios.
• The software is designed for user-friendly modification and model creation.

Conclusions:

• SIApopr provides a powerful and flexible R package for simulating complex biological systems.
• Its efficient implementation facilitates the study of mutation dynamics and population evolution.
• The package empowers researchers to explore various evolutionary hypotheses through customizable models.