ArticleViruses2018
Accounting for Space—Quantification of Cell-To-Cell Transmission Kinetics Using Virus Dynamics Models.
Article in Viruses, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Multimodal data integration to determine viral and innate immune kinetics in human airway epithelium.PLoS computational biology · 2026Article
- Spatially structured models of viral dynamics: a scoping review.Microbiology and molecular biology reviews : MMBR · 2025Article
- Spatial information allows inference of the prevalence of direct cell-to-cell viral infection.PLoS computational biology · 2024Article
- Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review.Microorganisms · 2023Review
- FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes.Bioinformatics (Oxford, England) · 2023Article
- Review
- HCV Spread Kinetics Reveal Varying Contributions of Transmission Modes to Infection Dynamics.Viruses · 2021Article
- Quantifying the dynamics of viral recombination during free virus and cell-to-cell transmission in HIV-1 infection.Virus evolution · 2021Article
- Modeling the efficiency of filovirus entry into cells in vitro: Effects of SNP mutations in the receptor molecule.PLoS computational biology · 2020Article
- Review
- Viral cell-to-cell spread: Conventional and non-conventional ways.Advances in virus research · 2020Review
- A Parameter Estimation Method for Multiscale Models of Hepatitis C Virus Dynamics.Bulletin of mathematical biology · 2019Article
- Modeling the measles paradox reveals the importance of cellular immunity in regulating viral clearance.PLoS pathogens · 2018Article
- Validated Models of Immune Response to Virus Infection.Current opinion in systems biology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Mathematical models based on ordinary differential equations (ODE) that describe the population dynamics of viruses and infected cells have been an essential tool to characterize and quantify viral infection dynamics. Although an important aspect of viral infection is the dynamics of viral spread, which includes transmission by cell-free virions and direct cell-to-cell transmission, models used so far ignored cell-to-cell transmission completely, or accounted for this process by simple mass-action kinetics between infected and uninfected cells. In this study, we show that the simple mass-action approach falls short when describing viral spread in a spatially-defined environment. Using simulated data, we present a model extension that allows correct quantification of cell-to-cell transmission dynamics within a monolayer of cells. By considering the decreasing proportion of cells that can contribute to cell-to-cell spread with progressing infection, our extension accounts for the transmission dynamics on a single cell level while still remaining applicable to standard population-based experimental measurements. While the ability to infer the proportion of cells infected by either of the transmission modes depends on the viral diffusion rate, the improved estimates obtained using our novel approach emphasize the need to correctly account for spatial aspects when analyzing viral spread.
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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 Socratic graph.