ArticleViruses2020
Structure and Hierarchy of SARS-CoV-2 Infection Dynamics Models Revealed by Reaction Network Analysis.
Article in Viruses, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology.Briefings in bioinformatics · 2026Review
- Carbon dots: A promising tool for viral infection treatment.Materials today. Bio · 2025Review
- Dynamics of spindle assembly and position checkpoints: Integrating molecular mechanisms with computational models.Computational and structural biotechnology journal · 2025Review
- Cell Cycle Complexity: Exploring the Structure of Persistent Subsystems in 414 Models.Biomedicines · 2024Article
- Revealing the hierarchical structure of microbial communities.Scientific reports · 2024Article
- Computing all persistent subspaces of a reaction-diffusion system.Scientific reports · 2023Article
- The spatial dynamics of immune response upon virus infection through hybrid dynamical computational model.Frontiers in immunology · 2023Article
- Infectious Disease Modeling with Socio-Viral Behavioral Aspects-Lessons Learned from the Spread of SARS-CoV-2 in a University.Tropical medicine and infectious disease · 2022Article
- Article
- A hybrid PDE-ABM model for viral dynamics with application to SARS-CoV-2 and influenza.Royal Society open science · 2021Article
- Mathematical modeling of ventilator-induced lung inflammation.Journal of theoretical biology · 2021Article
- Current and prospective computational approaches and challenges for developing COVID-19 vaccines.Advanced drug delivery reviews · 2021Review
- Competitive exclusion during co-infection as a strategy to prevent the spread of a virus: A computational perspective.PloS one · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work provides a mathematical technique for analyzing and comparing infection dynamics models with respect to their potential long-term behavior, resulting in a hierarchy integrating all models. We apply our technique to coupled ordinary and partial differential equation models of SARS-CoV-2 infection dynamics operating on different scales, that is, within a single organism and between several hosts. The structure of a model is assessed by the theory of chemical organizations, not requiring quantitative kinetic information. We present the Hasse diagrams of organizations for the twelve virus models analyzed within this study. For comparing models, each organization is characterized by the types of species it contains. For this, each species is mapped to one out of four types, representing uninfected, infected, immune system, and bacterial species, respectively. Subsequently, we can integrate these results with those of our former work on Influenza-A virus resulting in a single joint hierarchy of 24 models. It appears that the SARS-CoV-2 models are simpler with respect to their long term behavior and thus display a simpler hierarchy with little dependencies compared to the Influenza-A models. Our results can support further development towards more complex SARS-CoV-2 models targeting the higher levels of the hierarchy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.