Modeling propagation of weakly advantageous mutations in cancer cells

There is recent evidence that a considerable proportion of somatic mutations in cancer cells’ DNA may exert a weakly advantageous effect on tumor growth. In this paper, we develop models of cancer evolution with somatic mutations that introduce a weakly advantageous force to the evolution of cancer cells. The elaborated models incorporate random events of cellular births, deaths and occurrences of somatic mutations in cancer cells’ DNA. The models belong to two categories: deterministic and stochastic. The former are based on systems of differential equations that balance the average number of cells and mutations during evolution. To verify the results of our deterministic modeling, we use a stochastic Gillespie algorithm. We show that our models predict the explosive growth of the cancer cells population, consistent with recent experimental observations.

Authors

Publication Details

Journal
International Journal of Applied Mathematics and Computer Science
Published
2025-01-01
DOI
https://doi.org/10.61822/amcs-2025-0034
Citations
1
Primary Topic
Evolution and Genetic Dynamics
Type
article
Field-Weighted Citation Impact
1.82
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article

Modeling propagation of weakly advantageous mutations in cancer cells

Agnieszka Szczęsna, Mateusz Kania, Andrzej Polański, Jarosław Gil et al.
1 citations
International Journal of Applied Mathematics and Computer Science
Evolution and Genetic Dynamics
1.82
article

Modeling propagation of weakly advantageous mutations in cancer cells

Agnieszka Szczęsna, Mateusz Kania, Andrzej Polański, Jarosław Gil, Wojciech Łabaj
article en
1 citations

Abstract

There is recent evidence that a considerable proportion of somatic mutations in cancer cells’ DNA may exert a weakly advantageous effect on tumor growth. In this paper, we develop models of cancer evolution with somatic mutations that introduce a weakly advantageous force to the evolution of cancer cells. The elaborated models incorporate random events of cellular births, deaths and occurrences of somatic mutations in cancer cells’ DNA. The models belong to two categories: deterministic and stochastic. The former are based on systems of differential equations that balance the average number of cells and mutations during evolution. To verify the results of our deterministic modeling, we use a stochastic Gillespie algorithm. We show that our models predict the explosive growth of the cancer cells population, consistent with recent experimental observations.

International Journal of Applied Mathematics and Computer ScienceVol. 35(3)
Openalex Percentile: Top 12%
Evolution and Genetic Dynamics
1.82
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Modeling propagation of weakly advantageous mutations in cancer cells — Agnieszka Szczęsna, Mateusz Kania, et al. · International Journal of Applied Mathematics and Computer Science (2025) | TGRS Research Map | TGRS