Front propagation speeds of T7 virus mutants

Rioja, Víctor López de; Fort, Joaquim; Isern, Neus

doi:10.1016/j.jtbi.2015.08.005

Cited by 7 publications

(9 citation statements)

References 28 publications

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“…Thus infected cells will not die proportionally to the density of infected cells at the present time, k 2 [ I ]( r , t ), but proportionally to the density of infected cells at a previous instant t − τ , k 2 [ I ]( r , t − τ ), to properly include this time delay effect on the decay process. It has been shown that the term − k 2 [ I ]( r , t − τ ) agrees well with experimental data in a different context (infections of non-tumor cells) [ 23 ]. Other reaction-diffusion models do also apply t − τ , although in an alternative way [ 24 , 25 ].…”

Section: Methodssupporting

confidence: 65%

A mathematical approach to virus therapy of glioblastomas

2016

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BackgroundIt is widely believed that the treatment of glioblastomas (GBM) could benefit from oncolytic virus therapy. Clinical research has shown that Vesicular Stomatitis Virus (VSV) has strong oncolytic properties. In addition, mathematical models of virus treatment of tumors have been developed in recent years. Some experiments in vitro and in vivo have been done and shown promising results, but have been never compared quantitatively with mathematical models. We use in vitro data of this virus applied to glioblastoma.ResultsWe describe three increasingly realistic mathematical models for the VSV-GBM in vitro experiment with progressive incorporation of time-delay effects. For the virus dynamics, we obtain results consistent with the in vitro experimental speed data only when applying the more complex and comprehensive model, with time-delay effects both in the reactive and diffusive terms. The tumor speed is given by the minimum of a very simple function that nonetheless yields results within the experimental measured range.ConclusionsWe have improved a previous model with new ideas and carefully incorporated concepts from experimental results. We have shown that the delay time τ is the crucial parameter in this kind of models. We have demonstrated that our new model can satisfactorily predict the front speed for the lytic action of oncolytic VSV on glioblastoma observed in vitro. We provide a basis that can be applied in the near future to realistically simulate in vivo virus treatments of several cancers.ReviewersThis article was reviewed by Yang Kuang and Georg Luebeck. For the full reviews, please go to the Reviewers’ comments section.

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Section: Methodssupporting

confidence: 65%

A mathematical approach to virus therapy of glioblastomas

2016

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“…Our model introduces two ingredients that are biologically and physically relevant, and that are expected to affect the front dynamic. First, in contrast to previous work [21,[23][24][25][33][34][35], where the diffusion coefficient D only depends on the initial bacterial concentration B 0 (b = B0 Bmax in Eq. 1), we allow D to vary in time and space according to the local bacterial density (b = B+I Bmax in Eq.…”

Section: δTmentioning

confidence: 90%

“…We model the spatial dynamics of bacteriophage plaque growth by considering the interactions between three populations: viruses (phage) V , uninfected host bacteria B and infected host bacteria I, similar to [21,[23][24][25][32][33][34][35]. The process may be summarised as…”

Section: Modelling Plaque Growth: Density-dependent Diffusion Anmentioning

confidence: 99%

Virus-host interactions shape viral dispersal giving rise to distinct classes of travelling waves in spatial expansions

Hunter

Liu

Möbius

et al. 2020

Preprint

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Reaction-diffusion waves have long been used to describe the growth and spread of populations undergoing a spatial range expansion. Such waves are generally classed as either pulled, where the dynamics are driven by the very tip of the front and stochastic fluctuations are high, or pushed, where cooperation in growth or dispersal results in a bulk-driven wave in which fluctuations are suppressed. These concepts have been well studied experimentally in populations where the cooperation leads to a density-dependent growth rate. By contrast, relatively little is known about experimental populations that exhibit a density-dependent dispersal rate. Using bacteriophage T7 as a test organism, we present novel experimental measurements that demonstrate that the diffusion of phage T7, in a lawn of host E. coli, is hindered by the physical presence of the host bacteria cells. The coupling between host density, phage dispersal and cell lysis caused by viral infection results in an effective density-dependent diffusion rate akin to cooperative behavior. Using a system of reaction-diffusion equations, we show that this effect can result in a transition from a pulled to pushed expansion. Moreover, we find that a second independent density-dependent effect on phage dispersal spontaneously emerges as a result of the viral incubation period, during which phage is trapped inside the host unable to disperse. Our results indicate both that bacteriophage can be used as a controllable laboratory population to investigate the impact of density-dependent dispersal on evolution, and that the genetic diversity and adaptability of expanding viral populations could be much greater than is currently assumed.

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“…In contrast, proposed models of viral dynamics with MI on individuals cells have focused in an immunological framework where viruses infect individual cells of a larger organism, without inclusion of explicit spatial effects [19,20]. Prior spatial models of microbe-virus dynamics have considered plaque growth using PDEs [21,22,23,24] and IBMs [25,26] and the evolution of viral parameters using individual-based models (IBMs) [27,28,29,30]. Only [27] included MI; however, the analysis did not quantify levels of MI and instead addressed whether MIs enhance virusmicrobe coexistence.…”

Section: Introductionmentioning

confidence: 99%

Emergence of increased frequency and severity of multiple infections by viruses due to spatial clustering of hosts

2017

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Multiple virus particles can infect a target host cell. Such multiple infections (MIs) have significant and varied ecological and evolutionary consequences for both virus and host populations. Yet, the in situ rates and drivers of MIs in virus-microbe systems remain largely unknown. Here, we develop an individual-based model (IBM) of virus-microbe dynamics to probe how spatial interactions drive the frequency and nature of MIs. In our IBMs, we identify increasingly spatially correlated clusters of viruses given sufficient decreases in viral movement. We also identify increasingly spatially correlated clusters of viruses and clusters of hosts given sufficient increases in viral infectivity. The emergence of clusters is associated with an increase in multiply infected hosts as compared to expectations from an analogous mean field model. We also observe long-tails in the distribution of the multiplicity of infection in contrast to mean field expectations that such events are exponentially rare. We show that increases in both the frequency and severity of MIs occur when viruses invade a cluster of uninfected microbes. We contend that population-scale enhancement of MI arises from an aggregate of invasion dynamics over a distribution of microbe cluster sizes. Our work highlights the need to consider spatially explicit interactions as a potentially key driver underlying the ecology and evolution of virus-microbe communities.

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Front propagation speeds of T7 virus mutants

Cited by 7 publications

References 28 publications

A mathematical approach to virus therapy of glioblastomas

A mathematical approach to virus therapy of glioblastomas

Virus-host interactions shape viral dispersal giving rise to distinct classes of travelling waves in spatial expansions

Emergence of increased frequency and severity of multiple infections by viruses due to spatial clustering of hosts

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