2025/12/26 by Andrea-Maria Grecu, Allen G. Rodrigo, Teng Li · 1 voice
Biochemistry, Genetics and Molecular Biology · Mathematics · #Cancer Genomics and Diagnostics #Evolution and Genetic Dynamics #Markov Chains and Monte Carlo Methods
paper · doi:10.64898/2025.12.26.696594
openalex publication_date 2025/12/26 · openalex created_date 2025/12/27 · openalex updated_date 2026/07/14
Abstract Long-lived trees accumulate somatic mutations over centuries, forming genetic mosaics in which branches carry distinct genotypes shaped by meristem development. Elongation dynamics regulate stem cell lineage maintenance, while branching events redistribute mutations, producing genetic patterns that often diverge from the physical tree topology. Tomimoto and Satake (2023) formalized these processes through mechanistic simulations; however, their framework was designed for forward prediction rather than parameter inference. Estimating mutation rates and developmental parameters from observed data remains computationally intractable for likelihood-based methods. We present MutSimABC, an Approximate Bayesian Computation (ABC) framework that extends the Tomimoto & Satake model to enable simulation-based parameter inference. MutSimABC jointly estimates the mutation rate ( μ ), elongation parameter ( StD ), and branching bias ( σ ) by comparing observed and simulated mutation distributions without requiring explicit likelihood functions. Validation across 169 simulated datasets with known parameters achieved complete recovery of mutation rate and branching bias, and 99.4% recovery for elongation parameters, within 95% highest posterior density (HPD) intervals. Applied to genomic sequencing data from Eucalyptus melliodora , MutSimABC estimated somatic mutation rates ranging from 2.3 × 10 −10 to 1 × 10 −10 per site per year and inferred partially stochastic meristem dynamics. This framework enables joint inference of mutation and developmental parameters, advancing the mechanistic analysis of somatic evolution in plants, with flexibility that extends to any long-lived organism.