Virus Dynamics Model Research Articles

A cellular automata model of Ebola virus dynamics

We construct a stochastic cellular automaton (SCA) model for the spread of the Ebola virus (EBOV). We make substantial modifications to an existing SCA model used for HIV, introduced by others and studied by the authors. We give a rigorous analysis of the similarities between models due to the spread of virus and the typical immune response to it, and the differences which reflect the drastically different timing of the course of EBOV. We demonstrate output from the model and compare it with clinical data.

Prediction of long-term treatment outcome in HCV following 24 day PEG-IFN alpha-2b therapy using population pharmacokinetic-pharmacodynamic mixture modeling and classification analysis

Mathematical models have been widely used for understanding the dynamics of the hepatitis C virus (HCV). We propose a method to predict final clinical outcome for 24 HIV–HCV - coinfected patients with the help of a mathematical model based on the first two weeks of PEG-IFN therapy. Applying a pharmacokinetic-pharmacodynamic (PKPD) approach, together with mixture models, to the adapted model of viral dynamics developed by Neumann et al., we have analyzed the influence of PEG-IFN on the kinetics and interaction of target cells, infected cells and virus mRNA. It was found that PEG-IFN pharmacokinetic parameters were similar in sustained virological responders and nonresponders, while the plasma PEG-IFN concentration that decreases HCV production by 50% (EC50) and the rate of infected cell death were different. The treatment outcome depended mainly on the initial viral mRNA concentration and the rate of infected cell death. The population PKPD approach with a mixture model enabled the determination of individual PKPD parameters and showed high sensitivity (93.5%) and specificity (97.4%) for the prediction of the treatment outcome.

Stability of a stochastic model for HIV-1 dynamics within a host

We consider a stochastically perturbed Nowak–May model of virus dynamics within a host. Using the direct Lyapunov method, we found sufficient conditions for the stability in probability of equilibrium states of this model.

Global stability of humoral immunity virus dynamics models with nonlinear infection rate and removal

In this paper, we investigate the dynamical behavior of two nonlinear models for viral infection with humoral immune response. The first model contains four compartments; uninfected target cells, actively infected cells, free virus particles and B cells. The intrinsic growth rate of uninfected cells, incidence rate of infection, removal rate of infected cells, production rate of viruses, neutralization rate of viruses, activation rate of B cells and removal rate of B cells are given by more general nonlinear functions. The second model is a modification of the first one by including an eclipse stage of infected cells. We assume that the latent-to-active conversion rate is also given by a more general nonlinear function. For each model we derive two threshold parameters and establish a set of conditions on the general functions which are sufficient to determine the global dynamics of the models. By using suitable Lyapunov functions and LaSalle’s invariance principle, we prove the global asymptotic stability of the all equilibria of the models. We perform some numerical simulations for the models with specific forms of the general functions and show that the numerical results are consistent with the theoretical results.

Solving a Viral Dynamics Model Through Symmetry

We use symmetry analysis to solve the differential equation that govern the dynamics behind viral infection after liver transplants. The traditional regular symmetries usually lead to expressions that are impossible to integrate, subsequently forcing the analyst to consider special cases that may not even be pratical. Here we modify the symmetries to avert this.

Antigenic cooperation among intrahost HCV variants organized into a complex network of cross-immunoreactivity

Hepatitis C virus (HCV) has the propensity to cause chronic infection. Continuous immune escape has been proposed as a mechanism of intrahost viral evolution contributing to HCV persistence. Although the pronounced genetic diversity of intrahost HCV populations supports this hypothesis, recent observations of long-term persistence of individual HCV variants, negative selection increase, and complex dynamics of viral subpopulations during infection as well as broad cross-immunoreactivity (CR) among variants are inconsistent with the immune-escape hypothesis. Here, we present a mathematical model of intrahost viral population dynamics under the condition of a complex CR network (CRN) of viral variants and examine the contribution of CR to establishing persistent HCV infection. The model suggests a mechanism of viral adaptation by antigenic cooperation (AC), with immune responses against one variant protecting other variants. AC reduces the capacity of the host's immune system to neutralize certain viral variants. CRN structure determines specific roles for each viral variant in host adaptation, with variants eliciting broad-CR antibodies facilitating persistence of other variants immunoreacting with these antibodies. The proposed mechanism is supported by empirical observations of intrahost HCV evolution. Interference with AC is a potential strategy for interruption and prevention of chronic HCV infection.

New virus dynamics in the presence of multiple infection

While most aspects of virus dynamics are well understood in standard models, the phenomenon of multiple infection (or coinfection) can change the properties of the dynamics, and this has so far not been fully explored. An important parameter in determining the properties of the model is the virus output from multiply infected cells compared to that from singly infected cells. If the amount of virus produced by infected cells during their life-span is independent of the infection multiplicity, then multiple infection does not change the dynamics. If, however, multiply infected cells produce more virus during their life-span than singly infected cells, then the properties of the dynamics can change fundamentally. This paper presents a detailed mathematical analysis of this scenario. We demonstrate that under some realistic conditions, the equilibrium structure of the solutions acquires novel properties. In particular, infection can persist even for values of the basic reproductive number, R0, smaller than unity. In this regime, we observe the phenomenon of bistability, when two stable equilibria are present simultaneously, and the outcome is determined by the initial conditions. The two possible solutions are the virus-free equilibrium, which is exactly the same as the one observed in the absence of multiple infection, and a novel infection equilibrium. In the presence of this outcome, it is clear that the meaning of the parameter R0 changes, as it no longer simply indicates the possibility of successful infection. This adds to our understanding and interpretation of R0 in virus dynamics models, and also provides further insights about conditions that can lead to virus extinction rather than persistence. It turns out that conditions for bistability depend (in a fully specified way) on the model structure, particularly on the way the infection term is formulated. We provide a general condition that informs us whether or not bistability occurs, and define what needs to be measured when examining the dynamics of multiple infection in specific biological systems.

Global stability of a diffusive virus dynamics model with general incidence function and time delay

In this paper, we propose a model of virus dynamics that includes diffusion, time delay and a general incidence function. By constructing Liapunov functionals, we show that the model has threshold dynamics: if the basic reproduction number R0≤1, then the infection-free equilibrium is globally asymptotically stable; whereas if R0>1, then there exists an infection equilibrium which is globally asymptotically stable. We pay particular attention to demonstrating that solutions are sufficiently bounded away from 0 that the Liapunov functionals are well-defined. Some applications are listed. Our results improve and generalize some known results.

The Stability Analyses of the Mathematical Models of Hepatitis C Virus Infection

There are two mathematical models of Hepatitis C virus (HCV) being discussed; the original model of HCV viral dynamics (Neumann et al., 1998) and its extended model (Dahari et al., 2007). The key aspects of the mathematical models have provided resources for analysing the stability of the uninfected and the infected steady states, in evaluating the antiviral effectiveness of therapy and for estimating the ranges of values of the parameters for clinical treatment. The original model is considered to be a deterministic model because of the predictive nature of the antiviral therapy within the constant target cells. Numerical simulations are carried out in the extended model, to explain the stability of the steady states in the absence or existence of migration in hepatocytes and, drug efficacy in treating HCV infection.

Global stability analysis of humoral immunity virus dynamics model including latently infected cells

In this paper, we propose and analyse a virus dynamics model with humoral immune response including latently infected cells. The incidence rate is given by Beddington–DeAngelis functional response. We have derived two threshold parameters, the basic infection reproduction number and the humoral immune response activation number which completely determined the basic and global properties of the virus dynamics model. By constructing suitable Lyapunov functions and applying LaSalle's invariance principle we have proven that if , then the infection-free equilibrium is globally asymptotically stable (GAS), if , then the chronic-infection equilibrium without humoral immune response is GAS, and if , then the chronic-infection equilibrium with humoral immune response is globally asymptotically stable. These results are further illustrated by numerical simulations.

Models of Viral Population Dynamics.

Models of viral population dynamics have contributed enormously to our understanding of the pathogenesis and transmission of several infectious diseases, the coevolutionary dynamics of viruses and their hosts, the mechanisms of action of drugs, and the effectiveness of interventions. In this chapter, we review major advances in the modeling of the population dynamics of the human immunodeficiency virus (HIV) and briefly discuss adaptations to other viruses.

Global stability of an age-structured virus dynamics model with Beddington-DeAngelis infection function.

In this paper, we study an age-structured virus dynamics model with Beddington-DeAngelis infection function. An explicit formula for the basic reproductive number R0 of the model is obtained. We investigate the global behavior of the model in terms of R0: if R0 ≤ 1, then the infection-free equilibrium is globally asymptotically stable, whereas if R0 > 1, then the infection equilibrium is globally asymptotically stable. Finally, some special cases, which reduce to some known HIV infection models studied by other researchers, are considered.

Virus dynamics model with intracellular delays and immune response.

In this paper, we incorporate an extra logistic growth term for uninfected CD4+ T-cells into an HIV-1 infection model with both intracellular delay and immune response delay which was studied by Pawelek et al. in [26]. First, we proved that if the basic reproduction number R0 < 1, then the infection-free steady state is globally asymptotically stable. Second, when R0 > 1, then the system is uniformly persistent, suggesting that the clearance or the uniform persistence of the virus is completely determined by R0. Furthermore, given both the two delays are zero, then the infected steady state is asymptotically stable when the intrinsic growth rate of the extra logistic term is sufficiently small. When the two delays are not zero, we showed that both the immune response delay and the intracellular delay may destabilize the infected steady state by leading to Hopf bifurcation and stable periodic oscillations, on which we analyzed the direction of the Hopf bifurcation as well as the stability of the bifurcating periodic orbits by normal form and center manifold theory introduced by Hassard et al. Third, we engaged numerical simulations to explore the rich dynamics like chaotic oscillations, complicated bifurcation diagram of viral load due to the logistic term of target cells and the two time delays.

Control exponential growth of tumor cells with slow spread of oncolytic virus.

Great attention has been paid to cancer therapy by means of oncolytic viruses, but the fast virus-spread, which eliminates all tumor cells, cannot be applied to solid tumors. As slow virus-spread is applied, solid tumors are expected to be controlled but complicated dynamical behaviors appear. In this paper we investigate bifurcations of equilibria in the oncolytic virus dynamics model with exponential growth of tumor cells and slow virus-spread. We find conditions of parameters for saddle-node bifurcation, Hopf bifurcation and Bogdanov-Takens bifurcation. Those conditions give thresholds for slow virus-spread to control the population of tumor cells within an appropriate range.

Estimating HIV-1 fitness characteristics from cross-sectional genotype data.

Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.

Mathematical modeling of multi-drugs therapy: a challenge for determining the optimal combinations of antiviral drugs

In the current era of antiviral drug therapy, combining multiple drugs is a primary approach for improving antiviral effects, reducing the doses of individual drugs, relieving the side effects of strong antiviral drugs, and preventing the emergence of drug-resistant viruses. Although a variety of new drugs have been developed for HIV, HCV and influenza virus, the optimal combinations of multiple drugs are incompletely understood. To optimize the benefits of multi-drugs combinations, we must investigate the interactions between the combined drugs and their target viruses. Mathematical models of viral infection dynamics provide an ideal tool for this purpose. Additionally, whether drug combinations computed by these models are synergistic can be assessed by two prominent drug combination theories, Loewe additivity and Bliss independence. By combining the mathematical modeling of virus dynamics with drug combination theories, we could show the principles by which drug combinations yield a synergistic effect. Here, we describe the theoretical aspects of multi-drugs therapy and discuss their application to antiviral research.

Global analysis of a humoral and cellular immunity virus dynamics model with the Beddington–DeAngelis incidence rate

In this paper, a humoral and cellular immunity virus dynamics model with the Beddington‐DeAngelis incidence rate is set up. We derive the basic reproductive number R0, the cytotoxic T lymphocytes immune response reproductive number R1, the humoral immune response reproductive number R2, humoral immune response competitive reproductive number R3, and cytotoxic T lymphocytes immune response competitive reproductive number R4, and a full description of the relation between the existence of the equilibria and reproductive numbers is given. The global properties of the five equilibria are obtained by constructing Lyapunov functions. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Global dynamics of a PDE in-host viral model

We consider an in-host virus dynamics model with spatial heterogeneity on the general bounded domain, and homogenous Neumann boundary condition. Under the assumption that only the free virus diffuse and the host cells (infected and uninfected) are not mobile, the model turns out to be hybrid in the sense that it consists of two point-wise ODEs and an PDE. We explore the virus dynamics by analyzing the model and identifying the basic reproduction number. When all model parameters are constants, the global dynamics of the model are fully determined.

On the laws of virus spread through cell populations.

The dynamics of viral infections have been investigated extensively, often with a combination of experimental and mathematical approaches. Mathematical descriptions of virus spread through cell populations are well established in the literature and have yielded important insights, yet the formulation of certain fundamental aspects of virus dynamics models remains uncertain and untested. Here, we investigate the process of infection and, in particular, the effect of varying the target cell population size on the number of productively infected cells generated. Using an in vitro single-round HIV-1 infection system, we find that the established modeling framework cannot accurately fit the data. If the model is fit to data with the lowest number of cells and is used to predict data generated with larger cell populations, the model significantly overestimates the number of productively infected cells generated. Interestingly, this deviation becomes stronger under experimental conditions that promote mixing of cells and viruses. The reason for the deviation is that the standard model makes certain oversimplifying assumptions about the fate of viruses that fail to find a cell in their immediate proximity. We derive from stochastic processes a different model that assumes simultaneous access of the virus to multiple target cells. In this scenario, if no cell is available to the virus at its location, it has a chance to interact with other cells, a process that can be promoted by mixing of the populations. This model can accurately fit the experimental data and suggests a new interpretation of mass action in virus dynamics models. Understanding the principles of virus growth through cell populations is of fundamental importance to virology. It helps us make informed decisions about intervention strategies aimed at preventing virus growth, such as drug treatment or vaccination approaches, e.g., in HIV infection, yet considerable uncertainty remains in this respect. An important variable in this context is the number of susceptible cells available for virus replication. How does the number of susceptible cells influence the growth potential of the virus? Besides the importance of such information for clinical responses, a thorough understanding of this is also important for the prediction of virus levels in patients and the estimation of crucial patient parameters through the use of mathematical models. This paper investigates the relationship between target cell availability and the virus growth potential with a combination of experimental and mathematical approaches and provides significant new insights.

Within-host viral dynamics of dengue serotype 1 infection.

Dengue, the most common mosquito-borne viral infection of humans, is endemic across much of the world, including much of tropical Asia and is increasing in its geographical range. Here, we present a mathematical model of dengue virus dynamics within infected individuals, detailing the interaction between virus and a simple immune response. We fit this model to measurements of plasma viral titre from cases of primary and secondary DENV 1 infection in Vietnam. We show that variation in model parameters governing the immune response is sufficient to create the observed variation in virus dynamics between individuals. Estimating model parameter values, we find parameter differences between primary and secondary cases consistent with the theory of antibody-dependent enhancement (namely enhanced rates of viral entry to target cells in secondary cases). Finally, we use our model to examine the potential impact of an antiviral drug on the within-host dynamics of dengue. We conclude that the impact of antiviral therapy on virus dynamics is likely to be limited if therapy is only started at the onset of symptoms, owing to the typically late stage of viral pathogenesis reached by the time symptoms are manifested and thus treatment is started.