A cell-level model to predict the spatiotemporal dynamics of neurodegenerative disease
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910024203239424 |
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| author | Huang, Shih-Huan Cotton, Matthew W. Knowles, Tuomas P. J. Klenerman, David Meisl, Georg |
| author_facet | Huang, Shih-Huan Cotton, Matthew W. Knowles, Tuomas P. J. Klenerman, David Meisl, Georg |
| contents | A central challenge in modeling neurodegenerative diseases is connecting cellular-level mechanisms to tissue-level pathology, in particular to determine whether pathology is driven primarily by cell-autonomous triggers or by propagation from cells that are already in a pathological, runaway aggregation state. To bridge this gap, we here develop a bottom-up physical model that explicitly incorporates these two fundamental cell-level drivers of protein aggregation dynamics. We show that our model naturally explains the characteristic long, slow development of pathology followed by a rapid acceleration, a hallmark of many neurodegenerative diseases. Furthermore, the model reveals the existence of a critical switch point at which the system's dynamics transition from being dominated by slow, spontaneous formation of diseased cells to being driven by fast propagation. This framework provides a robust physical foundation for interpreting pathological data and offers a method to predict which class of therapeutic strategies is best matched to the underlying drivers of a specific disease. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A cell-level model to predict the spatiotemporal dynamics of neurodegenerative disease Huang, Shih-Huan Cotton, Matthew W. Knowles, Tuomas P. J. Klenerman, David Meisl, Georg Quantitative Methods Soft Condensed Matter A central challenge in modeling neurodegenerative diseases is connecting cellular-level mechanisms to tissue-level pathology, in particular to determine whether pathology is driven primarily by cell-autonomous triggers or by propagation from cells that are already in a pathological, runaway aggregation state. To bridge this gap, we here develop a bottom-up physical model that explicitly incorporates these two fundamental cell-level drivers of protein aggregation dynamics. We show that our model naturally explains the characteristic long, slow development of pathology followed by a rapid acceleration, a hallmark of many neurodegenerative diseases. Furthermore, the model reveals the existence of a critical switch point at which the system's dynamics transition from being dominated by slow, spontaneous formation of diseased cells to being driven by fast propagation. This framework provides a robust physical foundation for interpreting pathological data and offers a method to predict which class of therapeutic strategies is best matched to the underlying drivers of a specific disease. |
| title | A cell-level model to predict the spatiotemporal dynamics of neurodegenerative disease |
| topic | Quantitative Methods Soft Condensed Matter |
| url | https://arxiv.org/abs/2508.15046 |