ArticleTheoretical biology & medical modelling2010
Dynamic models of immune responses: what is the ideal level of detail?
Article in Theoretical biology & medical modelling, 2010. 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.
- Value of Bioinformatics Models for Predicting Translational Control of Angiogenesis.Circulation research · 2025Review
- Cancer immune control dynamics: a clinical data driven model of systemic immunity in patients with metastatic melanoma.BMC bioinformatics · 2021Article
- Incorporating Time Delays in the Mathematical Modelling of the Human Immune Response in Viral Infections.Procedia computer science · 2021Article
- Computational Nosology and Precision Psychiatry.Computational psychiatry (Cambridge, Mass.) · 2017Article
- The role of the hypothalamic-pituitary-adrenal axis in modulating seasonal changes in immunity.Physiological genomics · 2016Article
- Modeling immune response and its effect on infectious disease outbreak dynamics.Theoretical biology & medical modelling · 2016Article
- Boolean Modeling of Cellular and Molecular Pathways Involved in Influenza Infection.Computational and mathematical methods in medicine · 2016Article
- Heterologous Infection of Pregnant Mice Induces Low Birth Weight and Modifies Offspring Susceptibility to Malaria.PloS one · 2016Article
- A comparison of elasticities of viral levels to specific immune response mechanisms in human immunodeficiency virus infection.BMC research notes · 2014Article
- A cognitive computational model inspired by the immune system response.BioMed research international · 2014Article
- Article
- On the role of CD8 T cells in the control of persistent infections.Biophysical journal · 2012Article
- Drug treatment of malaria infections can reduce levels of protection transferred to offspring via maternal immunity.Proceedings. Biological sciences · 2012Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundOne of the goals of computational immunology is to facilitate the study of infectious diseases. Dynamic modeling is a powerful tool to integrate empirical data from independent sources, make novel predictions, and to foresee the gaps in the current knowledge. Dynamic models constructed to study the interactions between pathogens and hosts' immune responses have revealed key regulatory processes in the infection. OPTIMUM COMPLEXITY AND DYNAMIC MODELING: We discuss the usability of various deterministic dynamic modeling approaches to study the progression of infectious diseases. The complexity of these models is dependent on the number of components and the temporal resolution in the model. We comment on the specific use of simple and complex models in the study of the progression of infectious diseases.
conclusionsModels of sub-systems or simplified immune response can be used to hypothesize phenomena of host-pathogen interactions and to estimate rates and parameters. Nevertheless, to study the pathogenesis of an infection we need to develop models describing the dynamics of the immune components involved in the progression of the disease. Incorporation of the large number and variety of immune processes involved in pathogenesis requires tradeoffs in modeling.
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