ReviewFrontiers in physiology2017
Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection.
Review in Frontiers in physiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 24 citations in OpenAlex.
- CLL to Richter syndrome: Integrating network strategies with experiments elucidating disease drivers and personalized therapies.Science advances · 2025Article
- Role of Tidal Volume on Ventilator-Induced Lung Injury Under Heterogeneous Immunological Capabilities: A Mathematical Model Study.Life (Basel, Switzerland) · 2025Article
- A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective.NPJ systems biology and applications · 2023Article
- Discrete Logic Modeling of Cell Signaling Pathways.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Mathematical modeling of ventilator-induced lung inflammation.Journal of theoretical biology · 2021Article
- Multiscale modeling in disease.Current opinion in systems biology · 2021Article
- Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic.International journal of molecular sciences · 2021Review
- The Role of Age, Neutrophil Infiltration and Antibiotics Timing in the Severity ofInternational journal of molecular sciences · 2020Article
- Computational Approach to Identifying Universal Macrophage Biomarkers.Frontiers in physiology · 2020Article
- A Dynamic Variation of Pulmonary ACE2 Is Required to Modulate Neutrophilic Inflammation in Response toJournal of immunology (Baltimore, Md. : 1950) · 2019Article
- Bacterial Adherence and Dwelling Probability: Two Drivers of Early Alveolar Infection byFrontiers in cellular and infection microbiology · 2018Article
- Can Gamification Contribute to Computer Modeling-Driven Biomedical Research?Frontiers in physiology · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 most frequent causes of death worldwide. These infections still lack effective treatments in many developing countries and in immunocompromised populations like infants, elderly people and transplanted patients. The interaction between bacteria and the host is a complex system of interlinked intercellular and the intracellular processes, enriched in regulatory structures like positive and negative feedback loops. Severe pathological condition can emerge when the immune system of the host fails to neutralize the infection. This failure can result in systemic spreading of pathogens or overwhelming immune response followed by a systemic inflammatory response. Mathematical modeling is a promising tool to dissect the complexity underlying pathogenesis of bacterial lung infection at the molecular, cellular and tissue levels, and also at the interfaces among levels. In this article, we introduce mathematical and computational modeling frameworks that can be used for investigating molecular and cellular mechanisms underlying bacterial lung infection. Then, we compile and discuss published results on the modeling of regulatory pathways and cell populations relevant for lung infection and inflammation. Finally, we discuss how to make use of this multiplicity of modeling approaches to open new avenues in the search of the molecular and cellular mechanisms underlying bacterial infection in the lung.
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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.