Mixed-state branching evolution for cell division models

Abstract We prove a scaling limit theorem for two-type Galton–Watson branching processes with interaction. The limit theorem gives rise to a class of mixed-state branching processes with interaction used to simulate evolution for cell division affected by parasites. Such processes can also be obtained by the pathwise-unique solution to a stochastic equation system. Moreover, we present sufficient conditions for extinction with probability 1 and the exponential ergodicity in the $L^1$ -Wasserstein distance of such processes in some cases.

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Publication Details

Journal
Journal of Applied Probability
Published
2025-09-29
DOI
https://doi.org/10.1017/jpr.2025.10028
Primary Topic
Stochastic processes and statistical mechanics
Type
article
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article

Mixed-state branching evolution for cell division models

Shukai Chen, Lina Ji, Jie Xiong
Journal of Applied Probability
Stochastic processes and statistical mechanics
article

Mixed-state branching evolution for cell division models

Shukai Chen, Lina Ji, Jie Xiong
article en

Abstract

Abstract We prove a scaling limit theorem for two-type Galton–Watson branching processes with interaction. The limit theorem gives rise to a class of mixed-state branching processes with interaction used to simulate evolution for cell division affected by parasites. Such processes can also be obtained by the pathwise-unique solution to a stochastic equation system. Moreover, we present sufficient conditions for extinction with probability 1 and the exponential ergodicity in the $L^1$ -Wasserstein distance of such processes in some cases.

Journal of Applied ProbabilityVol. 63(1)
Fujian Normal University (CN), Southern University of Science and Technology (CN), Shenzhen Technology University (CN), Shenzhen MSU-BIT University
Openalex Percentile: Top 67%
Stochastic processes and statistical mechanics
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