Introduction to modeling viral infections and immunity

Perelson, Alan S.; Ribeiro, Ruy M.

doi:10.1111/imr.12700

Cited by 27 publications

(25 citation statements)

References 31 publications

(52 reference statements)

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“…There is a tradition of mathematical models that consider populations of infectious agents, target cells and infected cells [24,25,[66][67][68][69][70]. The usual assumption that new infectious agents are produced at a rate proportional to the number of infected cells, perhaps after an "eclipse" phase [27,28], may be appropriate in situations where infected cells, independently, release new infectious agents, one or a few at a time, on multiple occasions during their lifetime, a process known as "budding" [32].…”

Section: Discussionmentioning

confidence: 99%

Stochastic dynamics of Francisella tularensis infection and replication

et al. 2020

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We study the pathogenesis of Francisella tularensis infection with an experimental mouse model, agent-based computation and mathematical analysis. Following inhalational exposure to Francisella tularensis SCHU S4, a small initial number of bacteria enter lung host cells and proliferate inside them, eventually destroying the host cell and releasing numerous copies that infect other cells. Our analysis of disease progression is based on a stochastic model of a population of infectious agents inside one host cell, extending the birth-anddeath process by the occurrence of catastrophes: cell rupture events that affect all bacteria in a cell simultaneously. Closed expressions are obtained for the survival function of an infected cell, the number of bacteria released as a function of time after infection, and the total bacterial load. We compare our mathematical analysis with the results of agent-based computation and, making use of approximate Bayesian statistical inference, with experimental measurements carried out after murine aerosol infection with the virulent SCHU S4 strain of the bacterium Francisella tularensis, that infects alveolar macrophages. The posterior distribution of the rate of replication of intracellular bacteria is consistent with the estimate that the time between rounds of bacterial division is less than 6 hours in vivo.

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Section: Discussionmentioning

confidence: 99%

Stochastic dynamics of Francisella tularensis infection and replication

et al. 2020

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“…The field of viral dynamics modeling has been instrumental for understanding the emergence of HIV and the ensuing epidemic, particularly for understanding the evolution of host/virus interactions [ 59 – 67 ], predicting treatment responses [ 68 – 70 ], and designing novel and more effective therapeutic approaches [ 69 , 71 , 72 •]. Both Hill et al [ 73 ••] and Perelson [ 20 ] provide extensive reviews of viral dynamics in the context of HIV and in interaction with the immune system.…”

Section: Virusesmentioning

confidence: 99%

“…This highly versatile SIR approach has also inspired mathematical investigations of disease dynamics within the host. Existing reviews of within-host modeling have described in depth the extensive roles that mathematical models have played in elucidating effective immune responses to viruses and bacteria [ 18 – 20 ], improving drug or therapeutics treatments [ 21 ], and informing the design of vaccines [ 22 , 23 ] to protect against pathogens.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Computational Modeling to Understand Infectious Diseases

et al. 2020

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Purpose of Review Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation. Recent Findings Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps. Summary Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

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“…Applications have been summarized in a recent issue of Immunological Reviews (3), showing their relevance for understanding the host-pathogen interactions in both chronic and acute infections (4)(5)(6). In the last decade, parameter estimation of these models has increasingly relied on nonlinear mixed effect models (NLMEM), a statistical approach that improves both precision and accuracy of estimates by explicitly taking into account the between-subjects variability in the model (7,8).…”

Section: Introductionmentioning

confidence: 99%

Model Averaging in Viral Dynamic Models

Gonçalves

Mentré

Lemenuel‐Diot

et al. 2020

AAPS J

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The paucity of experimental data makes both inference and prediction particularly challenging in viral dynamic models. In presence of several candidate models, a common strategy is model selection (MS), in which models are fitted to the data but only results obtained with the "best model" are presented. However, this approach ignores model uncertainty, which may lead to inaccurate predictions. When several models provide a good fit to the data, another approach is model averaging (MA) that weights the predictions of each model according to its consistency to the data. Here we evaluated by simulations in a nonlinear mixed-effect model framework the performances of MS and MA in two realistic cases of acute viral infection: i) inference in presence of poorly identifiable parameters, namely initial viral inoculum and eclipse phase duration ii) uncertainty on the mechanisms of action of the immune response. MS was associated in some scenarios with a large rate of false selection. This led to a coverage rate lower than the nominal coverage rate of 0.95 in the majority of cases and below 0.50 in some scenarios. In contrast, MA provided better estimation of parameter uncertainty, with coverage rates ranging from 0.72 to 0.98 and mostly comprised within the nominal coverage rate. Finally, MA provided similar predictions than those obtained with MS. In conclusion, parameter estimates obtained with MS should be taken with caution, especially when several models well describe the data. In this situation, MA has better performances and could be performed to account for model uncertainty.

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Introduction to modeling viral infections and immunity

Cited by 27 publications

References 31 publications

Stochastic dynamics of Francisella tularensis infection and replication

Stochastic dynamics of Francisella tularensis infection and replication

Leveraging Computational Modeling to Understand Infectious Diseases

Model Averaging in Viral Dynamic Models

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