ArticleMolecular biology and evolution2022
Gattaca: Base-Pair Resolution Mutation Tracking for Somatic Evolution Studies using Agent-based Models.
Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Epithelial competition determines gene therapy potential to suppress Fanconi Anemia oral cancer risk.PLoS computational biology · 2025Article
- Epithelial competition determines gene therapy potential to suppress Fanconi Anemia oral cancer risk.bioRxiv : the preprint server for biology · 2025Article
- The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous drug resistant disease.Nature communications · 2024Article
- Agent-based methods facilitate integrative science in cancer.Trends in cell biology · 2023Review
- Homeostasis limits keratinocyte evolution.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Developing tools for analyzing and viewing multiplexed images.Patterns (New York, N.Y.) · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Research over the past two decades has made substantial inroads into our understanding of somatic mutations. Recently, these studies have focused on understanding their presence in homeostatic tissue. In parallel, agent-based mechanistic models have emerged as an important tool for understanding somatic mutation in tissue; yet no common methodology currently exists to provide base-pair resolution data for these models. Here, we present Gattaca as the first method for introducing and tracking somatic mutations at the base-pair resolution within agent-based models that typically lack nuclei. With nuclei that incorporate human reference genomes, mutational context, and sequence coverage/error information, Gattaca is able to realistically evolve sequence data, facilitating comparisons between in silico cell tissue modeling with experimental human somatic mutation data. This user-friendly method, incorporated into each in silico cell, allows us to fully capture somatic mutation spectra and evolution.
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