A hybrid stochastic–deterministic computational model accurately describes spatial dynamics and virus diffusion in HIV-1 growth competition assay

Immonen, Taina T.; Gibson, Richard; Leitner, Thomas; Miller, Melanie A.; Arts, Eric J.; Somersalo, Erkki; Calvetti, Daniela

doi:10.1016/j.jtbi.2012.07.005

Cited by 10 publications

(7 citation statements)

References 47 publications

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“…Initially, the extracellular environment does not contain any extracellular virus, cytokines, oxidative agents or immune cells. We introduce infection by creating a single infected epithelial cell at the center of the epithelial sheet, comparably to (but less than) initial conditions of similar works that model discrete cellular distributions [18, 31]. To illustrate the full range of dynamics of viral infection in the presence of an immune response, we established a baseline set of parameters (Table 1) for which the immune response is strong enough to slow the spread of the infection, but insufficient to prevent widespread infection and death of all epithelial cells (Fig 3).…”

Section: Resultsmentioning

confidence: 99%

“…ABMs are also well suited for extending existing models by modular integration of biological subcomponents, and their model parameters should be validated by experiment and studied through sensitivity analysis [17]. ABMs have been developed to account for infection dynamics in different biological compartments (such as the lung and lymph nodes [29, 30]) and to model disease progression of HIV [15,25,31–33] and dissemination of influenza virus to the lower respiratory tract [18, 34].…”

Section: Introductionmentioning

confidence: 99%

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A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness

Sego

Aponte-Serrano

Ferrari-Gianlupi

et al. 2020

Preprint

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The COVID-19 crisis has shown that classic sequential models for scientific research are too slow and do not easily encourage multidisciplinary scientific collaboration. The need to rapidly understand the causes of differing infection outcomes and vulnerabilities, to provide mechanistic frameworks for the interpretation of experimental and clinical data and to suggest drug and therapeutic targets and to design optimized personalized interventions all require the development of detailed predictive quantitative models of all aspects of COVID-19. Many of these models will require the use of common submodels describing specific aspects of infection ( e.g. , viral replication) but combine them in novel configurations. As a contribution to this development and as a proof-of-concept for some components of these models, we present a multi-layered 2D multiscale, multi-cell model and associated computer simulations of the infection of epithelial tissue by a virus, the proliferation and spread of the virus, the cellular immune response and tissue damage. Our initial, proof-of-concept model is built of modular components to allow it to be easily extended and adapted to describe specific viral infections, tissue types and immune responses. Immediately after a cell becomes infected, the virus replicates inside the cell. After an eclipse period, the infected cells start shedding diffusing infectious virus, infecting nearby cells, and secretes a short-diffusing cytokine signal. Neighboring cells can take up the diffusing extracellular virus and become infected. The cytokine signal calls for immune cells from a simple model of the systemic immune response. These immune cells chemotax and activate within the tissue in response to the cytokine profile. Activated immune cells can kill underlying epithelial cells directly or by secreting a short-diffusible toxic chemical. Infected cells can also die by apoptosis due to the stress of viral replication. We do not include direct cytokine mediated protective factors in the tissue or distinguish the complexity of the immune response in this simple model. Despite unrealistically fast viral production and immune response, the current base model allows us to define three parameter regimes, where the immune system rapidly controls the virus, where it controls the virus after extensive tissue damage, and where the virus escapes control and infects and kills all / cells. We can simulate a number of drug therapy concepts, like delayed rate of production of viral RNAs, reduced viral entry, and higher and lower levels of immune response, which we demonstrate with simulation results of parameter sweeps of select model parameters. From results of these sweeps, we found that successful containment of infection in simulation directly relates to inhibited viral internalization and rapid immune cell recruitment, while spread of infection occurs in simulations with fast viral internalization and slower immune response. In contrast to other simulations of viral infection, our simulated tissue demonstrates...

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness

Sego

Aponte-Serrano

Ferrari-Gianlupi

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In our FRP system, the first round of viral infection produces diverse HIV-1 env recombinants requiring a relatively long culture period to grow out. We monitored virus production every two days and based on low production of recombinant virus, susceptible cell numbers in culture, and previous models on dual infection [43] we likely not reach titers sufficient to enable the co-infection of a cell with two different recombinants (but of course not impossible). Only when we achieved high MOI would recombinants of recombinants be produced with multiple breakpoints.…”

Section: Discussionmentioning

confidence: 99%

An in vitro Model to Mimic Selection of Replication-Competent HIV-1 Intersubtype Recombination in Dual or Superinfected Patients

Bagaya

Tian

Nickel

et al. 2017

Journal of Molecular Biology

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The low frequency of HIV-1 recombinants within entire viral populations in both individual patients and in culture-based infection models impedes investigation of the underlying factors contributing either to the occurrence of recombinants or the survival of recombinants once they are formed. So far, most of the related studies have no consideration of recombinants’ functionality. Here we established a Functional Recombinant Production (FRP) system to produce pure and functional HIV-1 intersubtype Env recombinants, and utilized 454 pyrosequencing to investigate the distribution of over 4,000 functional and non-functional recombination breakpoints from either the FRP system or dual infection cultures. The results revealed that most of the breakpoints converged in gp41 (62%) and C1 (25.3%) domains of gp120, which has strong correlation with the similarity between the two recombining sequences. Yet, the breakpoints also appeared in C2 (5.2%) and C5 (4.6%) domains not correlated with the recombining sequence similarity. Interestingly, none of the intersubtype gp120 recombinants recombined between C1 and gp41 regions either from the FRP system or from the dual infection culture, and very few from the HIV epidemic, were functional. The present study suggests that the selection of functional Env recombinants is one of the reasons for the predominance of C1 and gp41 Env recombinants in the HIV epidemic, and provides an in vitro model to mimic selection of replication-competent HIV-1 intersubtype recombination in dual or superinfected patients.

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“…In life sciences, agent-based models have been used to describe how infectious diseases affect a population comprising, e.g., susceptible, infected and recovered individuals, where the probability of an agent to transition from susceptible to infected grows with the proximity of other infected agents, see, e.g., Beauchemin et al (2005), Immonen et al (2012). While what is known are the traits of the agents, often the target is to find whether and what kind of global, emergent phenomena result from their interactions.…”

Section: The Selection Of a Model: Scale And Complexitymentioning

confidence: 99%

Life sciences through mathematical models

Calvetti

Somersalo

2015

Rend. Fis. Acc. Lincei

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This article discusses some of the basic principles of mathematical modeling in life sciences, and in particular the special features that make the modeling task fundamentally different from the traditional reductive modeling. The intricacies of the modeling in living systems are elucidated by simple and tractable examples that underline the problems of parametric models and the issue of non-scalability of the models.

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A hybrid stochastic–deterministic computational model accurately describes spatial dynamics and virus diffusion in HIV-1 growth competition assay

Cited by 10 publications

References 47 publications

A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness

A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness

An in vitro Model to Mimic Selection of Replication-Competent HIV-1 Intersubtype Recombination in Dual or Superinfected Patients

Life sciences through mathematical models

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