scholarly journals Competitive exclusion during co-infection as a strategy to prevent the spread of a virus: A computational perspective

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247200
Author(s):  
Safar Vafadar ◽  
Maryam Shahdoust ◽  
Ata Kalirad ◽  
Pooya Zakeri ◽  
Mehdi Sadeghi

Inspired by the competition exclusion principle, this work aims at providing a computational framework to explore the theoretical feasibility of viral co-infection as a possible strategy to reduce the spread of a fatal strain in a population. We propose a stochastic-based model—called Co-Wish—to understand how competition between two viruses over a shared niche can affect the spread of each virus in infected tissue. To demonstrate the co-infection of two viruses, we first simulate the characteristics of two virus growth processes separately. Then, we examine their interactions until one can dominate the other. We use Co-Wish to explore how the model varies as the parameters of each virus growth process change when two viruses infect the host simultaneously. We will also investigate the effect of the delayed initiation of each infection. Moreover, Co-Wish not only examines the co-infection at the cell level but also includes the innate immune response during viral infection. The results highlight that the waiting times in the five stages of the viral infection of a cell in the model—namely attachment, penetration, eclipse, replication, and release—play an essential role in the competition between the two viruses. While it could prove challenging to fully understand the therapeutic potentials of viral co-infection, we discuss that our theoretical framework hints at an intriguing research direction in applying co-infection dynamics in controlling any viral outbreak’s speed.

2014 ◽  
Vol 51 (3) ◽  
pp. 599-612 ◽  
Author(s):  
J. E. Björnberg ◽  
T. Britton ◽  
E. I. Broman ◽  
E. Natan

In this work we introduce a stochastic model for the spread of a virus in a cell population where the virus has two ways of spreading: either by allowing its host cell to live and duplicate, or by multiplying in large numbers within the host cell, causing the host cell to burst and thereby let the virus enter new uninfected cells. The model is a kind of interacting Markov branching process. We focus in particular on the probability that the virus population survives and how this depends on a certain parameter λ which quantifies the ‘aggressiveness’ of the virus. Our main goal is to determine the optimal balance between aggressive growth and long-term success. Our analysis shows that the optimal strategy of the virus (in terms of survival) is obtained when the virus has no effect on the host cell's life cycle, corresponding to λ = 0. This is in agreement with experimental data about real viruses.


Bioimpacts ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 169-172
Author(s):  
Mohammad Hossein Ahmadi

The coronavirus disease 2019 (COVID-19) is an emerged infectious disease characterized by a severe pneumonia leading to death in some cases. Currently, no licensed vaccines, drugs, or biologics have been confirmed to be absolutely effective in prophylaxis or treatment of this novel infection. Therefore, the treatment of this highly contagious disease remains a global concern and emergency. The viral interference is a competition phenomenon by which a primary virus infecting a cell prohibits the infection of the same cell by another (secondary) virus. The phenomenon has recently been indicated to be exploited for antiviral strategies. This strategy, particularly when there is no efficient drug against a viral infection, is of high importance. Some researchers have studied the application of the phenomenon among different viruses. In this paper, I discussed the possibility of the application of interference phenomenon in prophylaxis of the disease.


2019 ◽  
Vol 5 (10) ◽  
pp. eaax4761 ◽  
Author(s):  
Wu Liu ◽  
Mehmet U. Caglar ◽  
Zhangming Mao ◽  
Andrew Woodman ◽  
Jamie J. Arnold ◽  
...  

Because many aspects of viral infection dynamics and inhibition are governed by stochastic processes, single-cell analysis should provide more information than approaches using population averaging. We have developed a microfluidic device composed of ~6000 wells, with each well containing a microstructure to capture single, infected cells replicating an enterovirus expressing a fluorescent reporter protein. We have used this system to characterize enterovirus inhibitors with distinct mechanisms of action. Single-cell analysis reveals that each class of inhibitor interferes with the viral infection cycle in a manner that can be distinguished by principal component analysis. Single-cell analysis of antiviral candidates not only reveals efficacy but also facilitates clustering of drugs with the same mechanism of action and provides some indication of the ease with which resistance will develop.


2013 ◽  
Vol 94 (6) ◽  
pp. 1421-1425 ◽  
Author(s):  
Yuanyuan Ma ◽  
Wei Wu ◽  
Hongyan Chen ◽  
Qifei Liu ◽  
Dongsheng Jia ◽  
...  

A cell line from the small brown planthopper (SBPH; Laodelphax striatellus) was established to study replication of rice stripe virus (RSV), a tenuivirus. The SBPH cell line, which had been subcultured through 30 passages, formed monolayers of epithelial-like cells. Inoculation of cultured vector cells with RSV resulted in a persistent infection. During viral infection in the SBPH cell line, the viral non-structural protein NS3 co-localized with the filamentous ribonucleoprotein particles of RSV, as revealed by electron and confocal microscopy. The knockdown of NS3 expression due to RNA interference induced by synthesized double-stranded RNAs from the NS3 gene significantly inhibited viral infection in the SBPH cell line. These results demonstrated that NS3 of RSV might be involved in viral replication or assembly. The persistent infection of the SBPH cell line by RSV will enable a better understanding of the complex relationship between RSV and its insect vector.


2014 ◽  
Vol 12 (05) ◽  
pp. 1450028 ◽  
Author(s):  
Abolfazl Rezvan ◽  
Sayed-Amir Marashi ◽  
Changiz Eslahchi

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.


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