Exploring the influence of mutation rates with stochastic perturbations on genome evolution

Abstract

We present a stochastic version of the classical Luria-Delbruck mutation rate model using Ito calculus. Our model is used to explore implications of population growth with randomly varying mutation rates. By comparing the It&ocirc version of the Luria-Delbruck mutation rate model to the deterministic we find that stochastic effects can have profound implications for genome evolution. In particular, as the growth rate decreases below the population's carrying capacity, or another given threshold, we show that stochastic effects can dominate the evolution of a population in a feedback-based manner. Moreover, we find that the influence of stochastic effects increase as the population reaches its carrying capacity if not accounted for. Our model also suggests strategies for a population to overcome increasing mutational load and evolve 'robustness' to genetic drift. We discuss our methodology in light of the SARS-CoV-2 virus and implications for its molecular evolution. Finally, we conclude that Ito calculus incorporates stochastic effects in mathematical models in a way that is very natural to biological systems.

Mutation-induced structural and dynamic perturbations at the IL-17A–IL-17RA interface: Therapeutic insights from multi-replicate molecular dynamics simulations
Afsheen Razzaq, Madiha Sardar, Mamona Mushtaq +3
PLOS One6 Nov 2026Peer-reviewedCC BY 4.0Proteomics & metabolomics Bioinformatics & computational biologyPLOS
Exploring the role of local sequence context on patterns of germline mutation
A. T. Beck, S. Zoellner, J. Z. Li
bioRxiv20 Oct 2026PreprintCC BYFull textEvolutionary biologybioRxiv
A diffusion model of viral evolution predicts mutation fitness and evolutionary trajectories
J. Wu, X. Ding, A. Wu
bioRxiv16 Sep 2026PreprintAll rights reservedFull textBioinformatics & computational biologybioRxiv
Reproductive mode and the evolution of mutation rate
Joëlle Lafond, Srivarshini Saravanakumar, Maurine Neiman +3
EcoEvoRxiv5 Sep 2026PreprintCC BY 4.0Full textGenetics & genomicsEcoEvoRxiv
Dynamical consequences of context-dependent mutation: visualizing antibody sequence evolution during somatic hypermutation
Vasilisa A. Kovaleva, Hannah V. Meyer, David M. McCandlish
bioRxiv26 Oct 2026PreprintCC BY-NC-NDFull textEvolutionary biologybioRxivPMC

Cite

BibTeX RIS

BibTeX

@misc{bohlin2026exploring,
  title = {{Exploring the influence of mutation rates with stochastic perturbations on genome evolution}},
  author = {J. Bohlin and J. H.- O. Pettersson},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.21.760709},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.21.760709v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - Exploring the influence of mutation rates with stochastic perturbations on genome evolution
AU  - J. Bohlin
AU  - J. H.- O. Pettersson
PY  - 2026
DA  - 2026/10/21
PB  - bioRxiv
DO  - 10.64898/2026.10.21.760709
UR  - https://www.biorxiv.org/content/10.64898/2026.10.21.760709v1
AB  - We present a stochastic version of the classical Luria-Delbruck mutation rate model using Ito calculus. Our model is used to explore implications of population growth with randomly varying mutation rates. By comparing the It&ocirc version of the Luria-Delbruck mutation rate model to the deterministic we find that stochastic effects can have profound implications for genome evolution. In particular, as the growth rate decreases below the population's carrying capacity, or another given threshold, we show that stochastic effects can dominate the evolution of a population in a feedback-based manner. Moreover, we find that the influence of stochastic effects increase as the population reaches its carrying capacity if not accounted for. Our model also suggests strategies for a population to overcome increasing mutational load and evolve 'robustness' to genetic drift. We discuss our methodology in light of the SARS-CoV-2 virus and implications for its molecular evolution. Finally, we conclude that Ito calculus incorporates stochastic effects in mathematical models in a way that is very natural to biological systems.
ER  -

Abstract

Related

Cite