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#cell

81 posts16 participants5 posts today
Rxiv mechanobio

📰 "Investigation of a two-patch within-host model of hepatitis B viral infection"
arxiv.org/abs/2507.23038 #Dynamics #Q-Bio.Ot #Cell

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arXiv.orgInvestigation of a two-patch within-host model of hepatitis B viral infectionChronic infection with hepatitis B virus (HBV) can lead to formation of abnormal nodular structures within the liver. To address how changes in liver anatomy affect overall virus-host dynamics, we developed within-host ordinary differential equation models of two-patch hepatitis B infection, one that assumes irreversible and one that assumes reversible movement between nodular structures. We investigated the models analytically and numerically, and determined the contribution of patch susceptibility, immune responses, and virus movement on within-patch and whole-liver virus dynamics. We explored the structural and practical identifiability of the models by implementing a differential algebra approach and the Monte Carlo approach for a specific HBV data set. We determined conditions for viral clearance, viral localization, and systemic viral infection. Our study suggests that cell susceptibility to infection within modular structures, the movement rate between patches, and the immune-mediated infected cell killing have the most influence on HBV dynamics. Our results can help inform intervention strategies.
Rxiv mechanobio

📰 "Unveiling Cancer Stem Cell Marker Networks: A Hypergraph Approach"
arxiv.org/abs/2407.19330 #Physics.Bio-Ph #Q-Bio.Qm #Dynamics #Cell

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arXiv.orgUnveiling Cancer Stem Cell Marker Networks: A Hypergraph ApproachWe propose a novel computational framework leveraging hypergraph theory to analyse cancer stem cell markers (CSCMs) across multiple organs. Hypergraphs provide a robust representation of CSCM co-expression patterns, capturing their complex multi-organ relationships more comprehensively than traditional graph-based methods. By integrating mutual information analysis and Markov models, we identify key markers driving tumour heterogeneity and metastasis, offering detailed insights into their interdependencies. This approach establishes hypergraphs as a computationally powerful tool to model cancer progression and metastatic dynamics, contributing to the understanding of complex biological systems and supporting the development of targeted therapeutic strategies.
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