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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 which 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.

Original publication




Journal article


Molecular biology and evolution

Publication Date



H. Lee Moffitt Cancer Center & Research Institute, Integrated Mathematical Oncology, Tampa, 33612, USA.