OG-SPACE: Optimized Stochastic Simulation of Spatial Models of Cancer Evolution

TL;DR

OG-SPACE is a computational framework that efficiently simulates the spatial and temporal evolution of cancer cell populations, enabling realistic in-silico experiments and benchmarking of bioinformatics tools.

Contribution

It introduces an optimized Gillespie algorithm tailored for large-scale, spatial cancer evolution simulations across various interaction rules and lattice structures.

Findings

Handles large cell populations efficiently

Produces detailed outputs like phylogenies and mutational trees

Supports diverse birth-death and interaction models

Abstract

Algorithmic strategies for the spatio-temporal simulation of multi-cellular systems are crucial to generate synthetic datasets for bioinformatics tools benchmarking, as well as to investigate experimental hypotheses on real-world systems in a variety of in-silico scenarios. In particular, efficient algorithms are needed to overcome the harsh trade-off between scalability and expressivity, which typically limits our capability to produce realistic simulations, especially in the context of cancer evolution. We introduce the Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE), a computational framework for the simulation of the spatio-temporal evolution of cancer subpopulations and of the experimental procedures of both bulk andsingle-cell sequencing. OG-SPACE relies on an evolution of the Gillespie algorithm optimized to deal with large numbers of cells and is designed tohandle a variety of birth-death processes and interaction rules on arbitrary lattices. As output OG-SPACE returns: the visual snapshots of the spatial configuration of the system over time, the phylogeny of the (sampled) cells, the mutational tree, the variant allele frequency spectrum (for bulk experiments) and the cell genotypes (for single-cell experiments).OG-SPACE is freely available at:https://github.com/BIMIB-DISCo/OG-SPACE