Phase Transitions in a Particle Model for the Self-Adaptive Response to Cancer Dynamics
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916882713411584 |
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| author | Kuehn, Christian Vogel, Quirin |
| author_facet | Kuehn, Christian Vogel, Quirin |
| contents | In this paper, we present a probabilistic analysis of a dynamical particle model for the self-adaptive immune response to cancer, as proposed by the first author in a previous work. The model is motivated by the interplay between immune surveillance and cancer evolution. We rigorously confirm the sharp phase transition in immune system learning predicted in the original work. Additionally, we compute the expected amount of information acquired by the immune system about cancer cells over time. Our analysis relies on time-reversal techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03577 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phase Transitions in a Particle Model for the Self-Adaptive Response to Cancer Dynamics Kuehn, Christian Vogel, Quirin Probability Adaptation and Self-Organizing Systems In this paper, we present a probabilistic analysis of a dynamical particle model for the self-adaptive immune response to cancer, as proposed by the first author in a previous work. The model is motivated by the interplay between immune surveillance and cancer evolution. We rigorously confirm the sharp phase transition in immune system learning predicted in the original work. Additionally, we compute the expected amount of information acquired by the immune system about cancer cells over time. Our analysis relies on time-reversal techniques. |
| title | Phase Transitions in a Particle Model for the Self-Adaptive Response to Cancer Dynamics |
| topic | Probability Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2508.03577 |