Sparse model identification and prediction of microglial cells during ischemic stroke

Autor:	Amato, Sara, Arnold, Andrea
Rok vydání:	2024
Předmět:	Quantitative Biology - Cell Behavior Quantitative Biology - Quantitative Methods Statistics - Applications Statistics - Computation
Druh dokumentu:	Working Paper
Popis:	Dynamics between key neuroinflammatory components, detrimental M1 and beneficial M2 microglial cells, are not fully understood post-ischemic stroke. To discover, model, and predict these dynamics, we use a method based on sparse identification of nonlinear dynamics (SINDy). The resulting data-driven dynamical system involves constant and linear terms but does not include nonlinear interactions between cells. Results show M2 microglial cell dominance of four days. Forward predictions capture potential long-term dynamics of microglial cells and suggest a persistent inflammatory response. Comment: 4 pages, 5 figures. Accepted, 8th International Conference on Computational and Mathematical Biomedical Engineering (CMBE2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.10915 Zobrazit plný text záznamu View this record from Arxiv