ReviewCellular and molecular life sciences : CMLS2020
Trends in mathematical modeling of host-pathogen interactions.
Review in Cellular and molecular life sciences : CMLS, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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.
The trial behind it
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Who cites it
26 citing papers in PubMed.
- Review
- Environmental degradation as a recipe for emerging viral diseases: implications for global health.One health outlook · 2026Review
- Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction.Briefings in bioinformatics · 2026Review
- Engineered Tissue Models to Decode Host-Microbiota Interactions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Article
- Review
- Article
- Inference of Host-Pathogen Interaction Matrices from Genome-Wide Polymorphism Data.Molecular biology and evolution · 2024Article
- Quantitative principles of microbial metabolism shared across scales.Nature microbiology · 2024Review
- Optimal control of ribosome population for gene expression under periodic nutrient intake.Journal of the Royal Society, Interface · 2024Article
- Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections.NPJ systems biology and applications · 2023Article
- Article
- Agent-based modelling of iron cycling bacteria provides a framework for testing alternative environmental conditions and modes of action.Royal Society open science · 2022Article
- Antimicrobial Stewardship Programs: A Review of Strategies to Avoid Polymyxins and Carbapenems Misuse in Low Middle-Income Countries.Antibiotics (Basel, Switzerland) · 2022Review
- Article
- Dynamic optimization reveals alveolar epithelial cells as key mediators of host defense in invasive aspergillosis.PLoS computational biology · 2021Article
- Article
- Interdisciplinary Approaches to COVID-19.Advances in experimental medicine and biology · 2021Article
- A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions.Frontiers in microbiology · 2021Review
- Competitive exclusion during co-infection as a strategy to prevent the spread of a virus: A computational perspective.PloS one · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host-pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.
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Registered trials
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.