Cargando…
Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients
Patients with coronavirus disease 2019 (COVID-19) often exhibit diverse disease progressions associated with various infectious ability, symptoms, and clinical treatments. To systematically and thoroughly understand the heterogeneous progression of COVID-19, we developed a multi-scale computational...
Autores principales: | Wang, Shun, Hao, Mengqian, Pan, Zishu, Lei, Jinzhi, Zou, Xiufen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654229/ https://www.ncbi.nlm.nih.gov/pubmed/34818337 http://dx.doi.org/10.1371/journal.pcbi.1009587 |
Ejemplares similares
-
Modeling and Dynamical Analysis of Virus-Triggered Innate Immune Signaling Pathways
por: Tan, Jinying, et al.
Publicado: (2012) -
Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis
por: Li, Yuanyuan, et al.
Publicado: (2015) -
Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets
por: Hao, Mengqian, et al.
Publicado: (2021) -
A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2
por: Britton, Tom, et al.
Publicado: (2020) -
Mathematical modeling and quantitative analysis of HIV-1 Gag trafficking and polymerization
por: Liu, Yuewu, et al.
Publicado: (2017)