Infection Models
Infection Models - Related Videos
12:30
Use of Galleria mellonella as a Model Organism to Study Legionella pneumophila Infection
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Cited by 108 •
2013
The larva of the wax moth Galleria mellonella was recently established as an in vivo model to study Legionella pneumophila infection. Here, we demonstrate fundamental techniques to characterize the pathogenesis of Legionella in the larvae, including inoculation, measurement of bacterial virulence and replication as well as extraction and analysis of infected hemocytes.
07:02
Animal Model of Implant-Associated Infections in Mice
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2025
The present protocol describes a mouse model employing subcutaneous dorsal implantation to investigate implant-associated infections, enabling comprehensive investigation of pathophysiological mechanisms and supporting the development of diagnostic criteria with targeted therapeutic strategies.
09:01
Chronic Salmonella Infected Mouse Model
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Cited by 12 •
2010
Establish a chronic bacterial infected mouse model with persistent Salmonella typhimurium colonization in intestine for 27 weeks.
02:21
Generation of a Murine Model with a Central Nervous System Infection
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2025
This video demonstrates the generation of a mouse model with a CNS infection. A suckling pup is injected with the virus both intracranially and intraperitoneally. The virus spreads, infecting the CNS causing neuronal cell death and inflammation. The pup is assessed for symptoms of infection as the disease progresses.
10:44
Influenza A Virus Studies in a Mouse Model of Infection
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Cited by 38 •
2017
Influenza A viruses (IAVs) are important human respiratory pathogens. To understand the pathogenicity of IAVs and to perform preclinical testing of novel vaccine approaches, animal models mimicking human physiology are required. Here, we describe techniques to evaluate IAV pathogenesis, humoral responses and vaccine efficacy using a mouse model of infection.
FAQs
Selection depends on the biological complexity required and whether the system can produce measurable outcomes relevant to the question. Cultured cells may support focused analysis of cellular interactions, whereas organoids or animal hosts can represent more complex tissue or host responses. Computational representations may provide another way to examine infection-related processes. The choice affects how confidently laboratory findings relate to human infection.
These systems provide different levels and forms of biological representation. Cultured cells emphasize interactions at the cellular level, organoids model organized tissue responses, and animal hosts provide a broader host setting. Computational representations approach infection through modeled biological relationships rather than direct pathogen-host interaction. Comparing these options helps investigators match model complexity to questions about invasion, replication, immunity, or tissue damage.
Outcomes vary with the pathogen introduced, the host, tissue, or cell context, and the biological processes selected for observation. Attachment, invasion, replication, immune recognition, and tissue damage may not appear with equal prominence in every system. Consequently, researchers must identify which interaction the model can represent and interpret differences as properties of both the pathogen and the experimental context.
Researchers monitor linked processes rather than relying on a single observation. Evidence may include pathogen attachment, invasion, replication, immune recognition, and tissue damage, depending on the model and study goal. Tracking these outcomes helps connect pathogen behavior with host responses. It also allows investigators to compare disease mechanisms and assess whether a system captures the biological event under investigation.
A study begins by selecting a system whose complexity and measurable outcomes fit the research question. Investigators then introduce a defined pathogen under controlled conditions and monitor relevant pathogen-host interactions, such as invasion, replication, immune recognition, or tissue damage. Results are compared across conditions or model systems when appropriate. This workflow supports evaluation of disease mechanisms, interventions, or factors affecting severity.
They are useful when investigators need to connect a candidate intervention or biological factor with observable pathogen or host outcomes. Models can support comparisons of disease mechanisms, antimicrobial or vaccine candidates, and factors that influence transmission or severity. Interpretation requires attention to biological complexity and measurable endpoints, because findings from a laboratory system may not fully represent infection in humans.