A virtual look at Epstein-Barr virus infection: simulation mechanism

Epstein-Barr Virus Reactivation After Paediatric Haematopoietic Stem Cell Transplantation: Risk Factors and Sensitivity Analysis of Mathematical Model

Epstein-Barr virus (EBV) establishes a lifelong latent infection in healthy humans, kept under immune control by cytotoxic T cells (CTLs). Following paediatric haematopoetic stem cell transplantation (HSCT), a loss of immune surveillance leads to opportunistic outgrowth of EBV-infected cells, resulting in EBV reactivation, which can ultimately progress to post-transplant lymphoproliferative disorder (PTLD). The aims of this study were to identify risk factors for EBV reactivation in children in the first 100 days post-HSCT and to assess the suitability of a previously reported mathematical model to mechanistically model EBV reactivation kinetics in this cohort. Retrospective electronic data were collected from 56 children who underwent HSCT at Great Ormond Street Hospital (GOSH) between 2005 and 2016. Using EBV viral load (VL) measurements from weekly quantitative PCR (qPCR) monitoring post-HSCT, a multivariable Cox proportional hazards (Cox-PH) model was developed to assess time to first EBV reactivation event in the first 100 days post-HSCT. Sensitivity analysis of a previously reported mathematical model was performed to identify key parameters affecting EBV VL. Cox-PH modelling revealed EBV seropositivity of the HSCT recipient and administration of anti-thymocyte globulin (ATG) pre-HSCT to be significantly associated with an increased risk of EBV reactivation in the first 100 days post-HSCT (adjusted hazard ratio (AHR) = 2.32, P = 0.02; AHR = 2.55, P = 0.04). Five parameters were found to affect EBV VL in sensitivity analysis of the previously reported mathematical model. In conclusion, we have assessed the effect of multiple covariates on EBV reactivation in the first 100 days post-HSCT in children and have identified key parameters in a previously reported mechanistic mathematical model that affect EBV VL. Future work will aim to fit this model to patient EBV VLs, develop the model to account for interindividual variability and model the effect of clinically relevant covariates such as rituximab therapy and ATG on EBV VL.

HLA-DR Expression Level in CD8+ T Cells Correlates With the Severity of Children With Acute Infectious Mononucleosis

Background

This study aimed to assess the host immune signatures associated with EBV infection and its clinical value in indicating the severity of children with acute infectious mononucleosis (IM).

Methods

Twenty-eight pediatric patients with IM aged 3–8 years were enrolled. The immune phenotypes and cytokine secretion capability of T cells were detected.

Results

The percentages and absolute numbers of CD3⁺ and CD8⁺ T cells were significantly increased in IM patients compared with HCs. The percentages of Naïve CD4⁺ and CD8⁺ T cells were decreased but with increased percentages of memory CD4⁺ and CD8⁺ T subsets. Our results showed the upregulation of active marker HLA-DR, TCR-αβ, and inhibitory receptors PD-1, TIGIT in CD8⁺ T cells from IM patients, which suggested that effective cytotoxic T cells were highly against EBV infection. However, EBV exposure impaired the cytokine (IFN-γ, IL-2, and TNF-α) secretion capability of CD4⁺ and CD8⁺ T cells after stimulation with PMA/ionomycin in vitro. Multivariate analysis revealed that the percentage of HLA-DR⁺ CD8⁺ T cells was an independent prognostic marker for IM. The percentage of HLA-DR⁺ CD8⁺ T cells was significantly correlated with high viral load and abnormal liver function results.

Conclusion

Robust expansion and upregulation of HLA-DR in CD8⁺ T cells, accompanied with impaired cytokine secretion, were typical characteristics of children with acute IM. The percentage of HLA-DR⁺ CD8⁺ T cells might be used as a prominent marker not only for the early diagnosis but also for indicating the severity of IM.

A novel mathematical model of AIDS-associated Kaposi's sarcoma: Analysis and optimal control

Kaposi's sarcoma (KS) has been the most common HHV-8 virus-induced neoplasm associated with HIV-1 infection. Although the standard KS therapy has not changed in 20 years, not all cases of KS will respond to the same therapy. The goal of current AIDS-KS treatment modalities is to reconstitute the immune system and suppress HIV-1 replication, but newer treatment modalities are on horizon. There are very few mathematical models that have included HIV-1 viral load (VL) measures, despite VL being a key determinant of treatment outcome. Here we introduce a mathematical model that consolidates the effect of both HIV-1 and HHV-8 VL on KS tumor progression by incorporating low or high VLs into the proliferation terms of the immune cell populations. Regulation of HIV-1/HHV-8 VL and viral reservoir cells is crucial for restoring a patient to an asymptomatic stage. Therefore, an optimal control strategy given by a combined antiretroviral therapy (cART) is derived. The results indicate that the drug treatment strategies are capable of removing the viral reservoirs faster and consequently, the HIV-1 and KS tumor burden is reduced. The predictions of the mathematical model have the potential to offer more effective therapeutic interventions based on viral and virus-infected cell load and support new studies addressing the superiority of VL over CD4⁺ T-cell count in HIV-1 pathogenesis.

Immersive Analytics: Theory and Research Agenda

Advances in a variety of computing fields, including “big data,” machine learning, visualization, and augmented/mixed/virtual reality, have combined to give rise to the emerging field of immersive analytics, which investigates how these new technologies support analysis and decision making. Thus far, we feel that immersive analytics research has been somewhat ad hoc, possibly owing to the fact that there is not yet an organizing framework for immersive analytics research. In this paper, we address this lack by proposing a definition for immersive analytics and identifying some general research areas and specific research questions that will be important for the development of this field. We also present three case studies that, while all being examples of what we would consider immersive analytics, present different challenges, and opportunities. These serve to demonstrate the breadth of immersive analytics and illustrate how the framework proposed in this paper applies to practical research.

Review of the systems biology of the immune system using agent-based models

The immune system is an inherent protection system in vertebrate animals including human beings that exhibit properties such as self-organisation, self-adaptation, learning, and recognition. It interacts with the other allied systems such as the gut and lymph nodes. There is a need for immune system modelling to know about its complex internal mechanism, to understand how it maintains the homoeostasis, and how it interacts with the other systems. There are two types of modelling techniques used for the simulation of features of the immune system: equation-based modelling (EBM) and agent-based modelling. Owing to certain shortcomings of the EBM, agent-based modelling techniques are being widely used. This technique provides various predictions for disease causes and treatments; it also helps in hypothesis verification. This study presents a review of agent-based modelling of the immune system and its interactions with the gut and lymph nodes. The authors also review the modelling of immune system interactions during tuberculosis and cancer. In addition, they also outline the future research directions for the immune system simulation through agent-based techniques such as the effects of stress on the immune system, evolution of the immune system, and identification of the parameters for a healthy immune system.

Transient Oral Human Cytomegalovirus Infections Indicate Inefficient Viral Spread from Very Few Initially Infected Cells

Cytomegalovirus (CMV) is acquired by the oral route in children, and primary infection is associated with abundant mucosal replication, as well as the establishment of latency in myeloid cells that results in lifelong infection. The efficiency of primary CMV infection in humans following oral exposure, however, is unknown. We consistently detected self-limited, low-level oral CMV shedding events, which we termed transient CMV infections, in a prospective birth cohort of 30 highly exposed CMV-uninfected infants. We estimated the likelihood of transient oral CMV infections by comparing their observed frequency to that of established primary infections, characterized by persistent high-level shedding, viremia, and seroconversion. We developed mathematical models of viral dynamics upon initial oral CMV infection and validated them using clinical shedding data. Transient infections comprised 76 to 88% of oral CMV shedding events. For this high percentage of transient infections to occur, we identified two mathematical prerequisites: a very small number of initially infected oral cells (1 to 4) and low viral infectivity (<1.5 new cells infected/cell). These observations indicate that oral CMV infection in infants typically begins with a single virus that spreads inefficiently to neighboring cells. Thus, although the incidence of CMV infection is high during infancy, our data provide a mechanistic framework to explain why multiple CMV exposures are typically required before infection is successfully established. These findings imply that a sufficiently primed immune response could prevent CMV from establishing latent infection in humans and support the achievability of a prophylactic CMV vaccine.IMPORTANCE CMV infects the majority of the world's population and is a major cause of birth defects. Developing a vaccine to prevent CMV infection would be extremely valuable but would be facilitated by a better understanding of how natural human CMV infection is acquired. We studied CMV acquisition in infants and found that infections are usually brief and self-limited and are successfully established relatively rarely. Thus, although most people eventually acquire CMV infection, it usually requires numerous exposures. Our analyses indicate that this is because the virus is surprisingly inefficient, barely replicating well enough to spread to neighboring cells in the mouth. Greater knowledge of why CMV infection usually fails may provide insight into how to prevent it from succeeding.

Dynamics of Persistent Oral Cytomegalovirus Shedding During Primary Infection in Ugandan Infants

Cytomegalovirus (CMV) infection occurs frequently in young children, who, when infected, are then a major source of transmission. Oral CMV shedding by 14 infants with primary infection was comprehensively characterized using quantitative polymerase chain reaction weekly for ≥9 months. Three phases of oral shedding were identified: expansion, transition, and clearance. Viral expansion occurred over a median of 7 weeks, with a median doubling time of 3 days. During the transition phase, expansion slowed over a median of 6 weeks before peak viral load was reached. Clearance was slow (22-day median half-life), and shedding did not resolve during observation for any infant. Mathematical modeling demonstrated that prolonged oral CMV expansion is explained by a low within-host reproduction number (median, 1.63) and a delayed immune response that only decreases the infected cell half-life by 44%. Thus, the prolonged oral CMV shedding observed during primary infection can be explained by slow viral expansion and inefficient immunologic control.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

Applications of systems science in biomedical research regarding obesity and noncommunicable chronic diseases: opportunities, promise, and challenges

Interest in the application of systems science (SS) in biomedical research, particularly regarding obesity and noncommunicable chronic disease (NCD) research, has been growing rapidly over the past decade. SS is a broad term referring to a family of research approaches that include modeling. As an emerging approach being adopted in public health, SS focuses on the complex dynamic interaction between agents (e.g., people) and subsystems defined at different levels. SS provides a conceptual framework for interdisciplinary and transdisciplinary approaches that address complex problems. SS has unique advantages for studying obesity and NCD problems in comparison to the traditional analytic approaches. The application of SS in biomedical research dates back to the 1960s with the development of computing capacity and simulation software. In recent decades, SS has been applied to addressing the growing global obesity epidemic. There is growing appreciation and support for using SS in the public health field, with many promising opportunities. There are also many challenges and uncertainties, including methodologic, funding, and institutional barriers. Integrated efforts by stakeholders that address these challenges are critical for the successful application of SS in the future.

Epstein-Barr virus effect on frequency of functionally distinct T cell subsets in children with infectious mononucleosis

PURPOSE

Epstein-Barr virus is a common human pathogen which infects the great majority of population worldwide. A striking proliferation of CD8⁺ T cells is an immune response to EBV invasion of B lymphocytes during infectious mononucleosis. The aim of the study was to analyze frequencies of CD28⁺CD95⁻, CD28⁺CD95⁺, CD28⁻CD95⁺ T cell subsets putative naïve (T(N)), central (T(CM)) and effector memory (T(EM)) T cells in children with infectious mononucleosis.

MATERIAL/METHODS

Multiparameter flow cytometric analysis of CD4⁺ and CD8⁺ T cell subsets was performed in 19 children with acute infectious mononucleosis.

RESULTS

The CD4⁺/CD8⁺ ratio was found to be decreased (0.53) in children with infectious mononucleosis. Median T(N), T(CM), T(EM) frequencies were estimated to be 3.7, 4.5, 15.1% of CD8⁺ and 23, 59.3, 5.5% of CD4⁺ T cells, respectively. In the present study we demonstrated negative correlations between CD8⁺CD28⁺CD95⁺ and CD8⁺CD28⁻CD95⁺ T cells and both VCA IgM antibody titers and disease duration. However, no such correlation was found when subset of CD4⁺ T cells or CD8⁺CD28⁺CD95⁻ cells was compared.

CONCLUSIONS

We conclude that there is a rapid decrease in the number of memory CD8⁺ T cells in early acute stage of infectious mononucleosis.

A MATHEMATICAL MODEL FOR THE TREATMENT OF AIDS-RELATED KAPOSI'S SARCOMA

We formulate a mathematical model to study the dynamics of HIV-1 related Kaposi's Sarcoma (KS) pathogenesis. KS progression is modeled as a dual process involving the primary infection of B cells, which sustains HHV-8 replication and the secondary infection of progenitor cells by HHV-8, which sustains the KS cell replication. We incorporate the pharmacodynamics of highly active antiretroviral therapy (HAART), or combination therapy (HAART plus KS therapy) and consider how each treatment strategy alters the disease progression. Our results indicate that administration of HAART to individuals co-infected with the HIV-1 and HHV-8 viruses can greatly amplify the therapeutic response of low-dose KS therapies. We have found that adherence levels above 85% can significantly reduce the risk of KS and HIV for a treatment periods under 1 year. For longer treatment periods, however, at least 90% adherence level is recommended.

The cycle of EBV infection explains persistence, the sizes of the infected cell populations and which come under CTL regulation

Previous analysis of Epstein-Barr virus (EBV) persistent infection has involved biological and immunological studies to identify and quantify infected cell populations and the immune response to them. This led to a biological model whereby EBV infects and activates naive B-cells, which then transit through the germinal center to become resting memory B-cells where the virus resides quiescently. Occasionally the virus reactivates from these memory cells to produce infectious virions. Some of this virus infects new naive B-cells, completing a cycle of infection. What has been lacking is an understanding of the dynamic interactions between these components and how their regulation by the immune response produces the observed pattern of viral persistence. We have recently provided a mathematical analysis of a pathogen which, like EBV, has a cycle of infected stages. In this paper we have developed biologically credible values for all of the parameters governing this model and show that with these values, it successfully recapitulates persistent EBV infection with remarkable accuracy. This includes correctly predicting the observed patterns of cytotoxic T-cell regulation (which and by how much each infected population is regulated by the immune response) and the size of the infected germinal center and memory populations. Furthermore, we find that viral quiescence in the memory compartment dictates the pattern of regulation but is not required for persistence; it is the cycle of infection that explains persistence and provides the stability that allows EBV to persist at extremely low levels. This shifts the focus away from a single infected stage, the memory B-cell, to the whole cycle of infection. We conclude that the mathematical description of the biological model of EBV persistence provides a sound basis for quantitative analysis of viral persistence and provides testable predictions about the nature of EBV-associated diseases and how to curb or prevent them.

Epstein-Barr virus (EBV) is a herpesvirus that establishes a lifelong persistent infection in virtually all human beings. This infection is a risk factor for the subsequent development of certain tumors and possibly also autoimmune diseases. In order to understand the origin of these diseases, it is necessary to first understand how EBV maintains persistent infection. We have used mathematical analysis to study this question. We find that the characteristic cycle of infected stages that EBV establishes in vivo allows it to persist stably at extremely low levels. This represents a consistent mathematical description of EBV infection and allows us to describe what must change to convert benign infection into pathogenic infection, as well as what kind of efficacy drugs and vaccines must have in order to be useful.

The pathogenesis of Epstein-Barr virus persistent infection

Epstein-Barr virus (EBV) maintains a lifelong infection. According to the germinal center model (GCM), latently infected B cells transit the germinal center (GC) to become resting memory cells. Here, the virus resides quiescently, occasionally reactivating to infect new B cells, completing the cycle of infection. The GCM remains the only model that explains EBV biology and the pathogenesis of lymphoma. Recent work suggests modifications to the model notably that the virus contributes only modestly to the GC process and predictions from mathematical models that quiescence within memory B cells shapes the overall structure of viral infection but is not essential for persistence. Rather, it is the cycle of infection which allows viral persistence at the very low levels observed.

Modeling the dynamics of virus shedding into the saliva of Epstein-Barr virus positive individuals

Epstein-Barr virus (EBV) can infect both B cells and epithelial cells. Infection of B cells enables the virus to persist within a host while infection of epithelial cells is suggested to amplify viral output. Data from a recent study have shown that the virus shedding in EBV positive individuals is relatively stable over short periods of time but varies significantly over long periods. The mechanisms underlying the regulation of virus shedding within a host are not fully understood. In this paper, we construct a model of ordinary differential equations to study the dynamics of virus shedding into the saliva of infected hosts. Infection of epithelial cells is further separated into infection by virus released from B cells and virus released from epithelial cells. We use the model to investigate whether the long-term variation and short-term stability of virus shedding can be generated by three possible factors: stochastic variations in the number of epithelial cells susceptible to virus released from infected B cells, to virus released from infected epithelial cells, or random variation in the probability that CD8(+) T cells encounter and successfully kill infected cells. The results support all three factors to explain the long-term variation but only the first and third factors to explain the short-term stability of virus shedding into saliva. Our analysis also shows that clearance of virus shedding is possible only when there is no virus reactivation from B cells.

Agent-based dynamic knowledge representation of Pseudomonas aeruginosa virulence activation in the stressed gut: Towards characterizing host-pathogen interactions in gut-derived sepsis

Background

There is a growing realization that alterations in host-pathogen interactions (HPI) can generate disease phenotypes without pathogen invasion. The gut represents a prime region where such HPI can arise and manifest. Under normal conditions intestinal microbial communities maintain a stable, mutually beneficial ecosystem. However, host stress can lead to changes in environmental conditions that shift the nature of the host-microbe dialogue, resulting in escalation of virulence expression, immune activation and ultimately systemic disease. Effective modulation of these dynamics requires the ability to characterize the complexity of the HPI, and dynamic computational modeling can aid in this task. Agent-based modeling is a computational method that is suited to representing spatially diverse, dynamical systems. We propose that dynamic knowledge representation of gut HPI with agent-based modeling will aid in the investigation of the pathogenesis of gut-derived sepsis.

Methodology/Principal Findings

An agent-based model (ABM) of virulence regulation in Pseudomonas aeruginosa was developed by translating bacterial and host cell sense-and-response mechanisms into behavioral rules for computational agents and integrated into a virtual environment representing the host-microbe interface in the gut. The resulting gut milieu ABM (GMABM) was used to: 1) investigate a potential clinically relevant laboratory experimental condition not yet developed - i.e. non-lethal transient segmental intestinal ischemia, 2) examine the sufficiency of existing hypotheses to explain experimental data - i.e. lethality in a model of major surgical insult and stress, and 3) produce behavior to potentially guide future experimental design - i.e. suggested sample points for a potential laboratory model of non-lethal transient intestinal ischemia. Furthermore, hypotheses were generated to explain certain discrepancies between the behaviors of the GMABM and biological experiments, and new investigatory avenues proposed to test those hypotheses.

Conclusions/Significance

Agent-based modeling can account for the spatio-temporal dynamics of an HPI, and, even when carried out with a relatively high degree of abstraction, can be useful in the investigation of system-level consequences of putative mechanisms operating at the individual agent level. We suggest that an integrated and iterative heuristic relationship between computational modeling and more traditional laboratory and clinical investigations, with a focus on identifying useful and sufficient degrees of abstraction, will enhance the efficiency and translational productivity of biomedical research.

Progress and problems in understanding and managing primary Epstein-Barr virus infections. Clin Microbiol Rev. 2011;24:193-209. [PMID: 21233512 DOI: 10.1128/cmr.00044-10]

Epstein-Barr virus (EBV) is a gammaherpesvirus that infects a large fraction of the human population. Primary infection is often asymptomatic but results in lifelong infection, which is kept in check by the host immune system. In some cases, primary infection can result in infectious mononucleosis. Furthermore, when host-virus balance is not achieved, the virus can drive potentially lethal lymphoproliferation and lymphomagenesis. In this review, we describe the biology of EBV and the host immune response. We review the diagnosis of EBV infection and discuss the characteristics and pathogenesis of infectious mononucleosis. These topics are approached in the context of developing therapeutic and preventative strategies.

New take on comparative immunology: relevance to immunotherapy

It is becoming increasingly recognized that experimental animal models, while useful to address monothematic biological questions, bear unpredictable relevance to human disease. Several reasons have been proposed. However, the uncontrollable nature of human genetics and the heterogeneity of disease that can only be replicated with difficulty experimentally play a leading role. Comparative immunology is a term that generally refers to the analysis of shared or diverging facets of immunology among species; these comparisons are carried out according to the principle that evolutionarily conserved themes outline biologic functions universally relevant for survival. We propose that a similar strategy could be applied to searching for themes shared by distinct immune pathologies within our own species. Identification of common patterns may outline pathways necessary for a particular determinism to occur, such as tissue-specific rejection or tolerance. This approach is founded on the unproven but sensible presumption that nature does not require an infinite plethora of redundant mechanisms to reach its purposes. Thus, immune pathologies must follow, at least in part, common means that determine their onset and maintenance. Commonalities among diseases can, in turn, be segregated from disease-specific patterns uncovering essential mechanisms that may represent universal targets for immunotherapy.

Alternating host cell tropism shapes the persistence, evolution and coexistence of epstein-barr virus infections in human

Epstein-Barr virus (EBV) infects and can persist in a majority of people worldwide. Within an infected host, EBV targets two major cell types, B cells and epithelial cells, and viruses emerging from one cell type preferentially infect the other. We use mathematical models to understand why EBV infects epithelial cells when B cells serve as a stable refuge for the virus and how switching between infecting each cell type affects virus persistence and shedding. We propose a mathematical model to describe the regulation of EBV infection within a host. This model is used to study the effects of parameter values on optimal viral strategies for transmission, persistence, and intrahost competition. Most often, the optimal strategy to maximize transmission is for viruses to infect epithelial cells, but the optimal strategy for maximizing intrahost competition is for viruses to mainly infect B cells. Applying the results of the within-host model, we derive a model of EBV dynamics in a homogeneous population of hosts that includes superinfection. We use this model to study the conditions necessary for invasion and coexistence of various viral strategies at the population level. When the importance of intrahost competition is weak, we show that coexistence of different strategies is possible.

Germinal center B cells latently infected with Epstein-Barr virus proliferate extensively but do not increase in number

In this study we show that in long-term persistent infection, Epstein-Barr virus (EBV)-infected cells undergoing a germinal center (GC) reaction in the tonsils are limited to the follicles and proliferate extensively. Despite this, the absolute number of infected cells per GC remains small (average of 3 to 4 cells per germinal center; range, 1 to 9 cells), and only about 38 to 55% (average, 45%) of all GCs carry infected cells. The data fit a model where, on average, cells in the GC divide approximately three times; however, only one progeny cell survives to undergo a further three divisions. Thus, a fraction of cells undergo multiple rounds of division without increasing in numbers; i.e., they die at the same rate that they are dividing. We conclude that EBV-infected cells in the GC undergo the extensive proliferation characteristic of GC cells but that the absolute number is limited either by the immune response or by the availability of an essential survival factor. We suggest that this behavior is a relic of the mechanism by which EBV establishes persistence during acute infection. Lastly, the expression of the viral latent protein LMP1 in GC B cells, unlike in vitro, does not correlate directly with the expression of bcl-2 or bcl-6. This emphasizes our claim that observations made regarding the functions of EBV proteins in cell lines or in transgenic mice should be treated with skepticism unless verified in vivo.

Parameter estimation and sensitivity analysis in an agent-based model of Leishmania major infection

Computer models of disease take a systems biology approach toward understanding host-pathogen interactions. In particular, data driven computer model calibration is the basis for inference of immunological and pathogen parameters, assessment of model validity, and comparison between alternative models of immune or pathogen behavior. In this paper we describe the calibration and analysis of an agent-based model of Leishmania major infection. A model of macrophage loss following uptake of necrotic tissue is proposed to explain macrophage depletion following peak infection. Using Gaussian processes to approximate the computer code, we perform a sensitivity analysis to identify important parameters and to characterize their influence on the simulated infection. The analysis indicates that increasing growth rate can favor or suppress pathogen loads, depending on the infection stage and the pathogen's ability to avoid detection. Subsequent calibration of the model against previously published biological observations suggests that L. major has a relatively slow growth rate and can replicate for an extended period of time before damaging the host cell.

Reproducibility and usability of chronic virus infection model using agent-based simulation; comparing with a mathematical model

We created agent-based models that visually simulate conditions of chronic viral infections using two software. The results from two models were consistent, when they have same parameters during the actual simulation. The simulation results comprise a transient phase and an equilibrium phase, and unlike the mathematical model, virus count transit smoothly to the equilibrium phase without overshooting which correlates with actual biology in vivo of certain viruses. We investigated the effects caused by varying all the parameters included in concept; increasing virus lifespan, uninfected cell lifespan, uninfected cell regeneration rate, virus production count from infected cells, and infection rate had positive effects to the virus count during the equilibrium period, whereas increasing the latent period, the lifespan-shortening ratio for infected cells, and the cell cycle speed had negative effects. Virus count at the start did not influence the equilibrium conditions, but it influenced the infection development rate. The space size had no intrinsic effect on the equilibrium period, but virus count maximized when the virus moving speed was twice the space size. These agent-based simulation models reproducibly provide a visual representation of the disease, and enable a simulation that encompasses parameters those are difficult to account for in a mathematical model.

Agent-based modeling of host-pathogen systems: The successes and challenges

Agent-based models have been employed to describe numerous processes in immunology. Simulations based on these types of models have been used to enhance our understanding of immunology and disease pathology. We review various agent-based models relevant to host–pathogen systems and discuss their contributions to our understanding of biological processes. We then point out some limitations and challenges of agent-based models and encourage efforts towards reproducibility and model validation.