SIApopr

SIApopr simulates stochastic branching processes in R, using a C++ backend for computational efficiency, to model clonal evolution and the accumulation of driver and passenger mutations under the infinite-allele assumption.

Key Features:

• Implementation: Provided as an R package with C++ components to accelerate simulations.
• Stochastic branching processes: Supports both time-homogeneous and time-inhomogeneous branching-process models.
• Mutation modeling: Simulates driver and passenger mutations under the infinite-allele assumption.
• Algorithm: Employs an expanded version of the Gillespie Stochastic Simulation Algorithm.
• Scalability: Enables rapid simulation of large numbers of cell types across diverse scenarios.
• Model flexibility: Allows modification of existing models or creation of new models tailored to specific research needs.

Scientific Applications:

• Tumor progression: Exploration of hypotheses about temporal and clonal dynamics during tumor development.
• Mutation dynamics: Study of the accumulation and impact of driver and passenger mutations under an infinite-allele framework.
• Evolutionary branching trees: Analysis of branching patterns and clonal diversification in tumor clonal evolution.

Methodology:

Uses an expanded Gillespie Stochastic Simulation Algorithm to simulate time-homogeneous and time-inhomogeneous stochastic branching processes under the infinite-allele assumption, implemented in R with a C++ backend to enable simulations of large numbers of cell types.