Viral Dynamics Analysis
Viral Dynamics Analysis - Related Videos
13:43
Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
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Cited by 15 •
2013
We describe a correlative microscopy method that combines high-speed 3D live-cell fluorescent light microscopy and high-resolution cryo-electron tomography. We demonstrate the capability of the correlative method by imaging dynamic, small HIV-1 particles interacting with host HeLa cells.
02:23
Obtaining Viral RNA for Real-Time Quantitative Polymerase Chain Reaction (qPCR) Analysis
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2026
Source: Suzuki, Y., et al. Measuring Dengue Virus RNA in the Culture Supernatant of Infected Cells by Real-time Quantitative Polymerase Chain Reaction. J. Vis. Exp. (2018).This video demonstrates the procedure for preparing viral samples for quantitative PCR (Polymerase chain reaction) analysis. Viral samples and RNA standards are serially diluted, treated with a proteinase K buffer, and incubated under controlled temperatures to release RNA. The processed RNA is then stabilized and stored for...
08:14
Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience
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Cited by 6 •
2014
This manuscript describes a protocol that applies comprehensive profiling for analysis of transcriptional programs induced in specific brain nuclei of rodents following behavioral paradigms. Herein, this approach is illustrated in the context of profiling genes induced in the nucleus accumbens (NAc) of mice following acute cocaine exposure, utilizing microfluidic qPCR arrays.
13:45
Using a Pan-Viral Microarray Assay (Virochip) to Screen Clinical Samples for Viral Pathogens
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Cited by 84 •
2011
The Virochip is a pan-viral microarray designed to simultaneously detect all known viruses as well as novel viruses on the basis of conserved sequence homology. Here we demonstrate how to run a Virochip assay to analyze clinical samples for the presence of both known and unknown viruses.
00:57
Viral Recombination
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2019
Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment. Viral Recombination Can Create New Diseases Some diseases can infect multiple species. For example, pigs can be infected by some human and...
FAQs
The analysis can estimate rates describing target-cell infection, production of new virions, and viral clearance. When longitudinal measurements are available, changes in viral load over time provide evidence about how quickly the virus expands or declines. These parameters help researchers characterize infection behavior quantitatively rather than relying only on a single measurement or clinical time point.
Immune markers can be analyzed alongside viral-load measurements to evaluate whether changes in virus levels correspond with immune-mediated clearance. Including these measurements helps distinguish viral replication from host-control processes and clarifies how immune responses shape infection. In immunology research, this connection supports interpretation of why viral burden changes during disease progression.
Mathematical models can represent treatment-driven clearance separately from immune-mediated clearance, allowing researchers to assess how antiviral therapy changes infection behavior. Comparing model estimates before and during treatment can indicate whether declining viral load is associated with the intervention, host responses, or both. This distinction improves interpretation of therapeutic effects and treatment outcomes.
A basic workflow combines longitudinal viral-load measurements with immune markers collected over the course of infection or treatment. Researchers then use mathematical representations of target-cell infection, virion production, and clearance to interpret the time-dependent data. The resulting model provides estimates of replication and clearance rates and links observed measurements to underlying infection processes.
It is useful when researchers need to quantify how a therapy changes viral replication or clearance rather than simply record whether viral load decreases. Model-based estimates can assess antiviral drug effects, compare treatment-related changes with immune-mediated control, and support predictions about treatment outcomes. This makes the approach relevant during therapeutic evaluation and infection-monitoring studies.
By relating viral-load trajectories to replication, production, clearance, and immune measurements, the models help explain how infection changes over time. They can identify quantitative patterns associated with increasing or declining viral burden and clarify the contribution of host responses. Such results support interpretation of disease progression across different viral infections and experimental settings.
At the host level, models describe replication, immune control, and clearance within an infected individual over time. At the population level, the same quantitative perspective can be used to examine how viral behavior is represented across infections or groups. This distinction helps researchers frame model results appropriately when comparing disease patterns or treatment outcomes.