VTechWorks Repository :: Browsing by Author "Ciupe, Stanca M."

Browsing by Author "Ciupe, Stanca M."

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Algebraic Methods for Modeling Gene Regulatory Networks
Murrugarra Tomairo, David M. (Virginia Tech, 2012-07-18)
So called discrete models have been successfully used in engineering and computational systems biology. This thesis discusses algebraic methods for modeling and analysis of gene regulatory networks within the discrete modeling context. The first chapter gives a background for discrete models and put in context some of the main research problems that have been pursued in this field for the last fifty years. It also outlines the content of each subsequent chapter. The second chapter focuses on the problem of inferring dynamics from the structure (topology) of the network. It also discusses the characterization of the attractor structure of a network when a particular class of functions control the nodes of the network. Chapters~3 and 4 focus on the study of multi-state nested canalyzing functions as biologically inspired functions and the characterization of their dynamics. Chapter 5 focuses on stochastic methods, specifically on the development of a stochastic modeling framework for discrete models. Stochastic discrete modeling is an alternative approach from the well-known mathematical formalizations such as stochastic differential equations and Gillespie algorithm simulations. Within the discrete setting, a framework that incorporates propensity probabilities for activation and degradation is presented. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations. Finally, Chapter 6 discusses future research directions inspired by the work presented here.
Antibody Responses during Hepatitis B Viral Infection
Ciupe, Stanca M.; Ribeiro, Ruy M.; Perelson, Alan S. (PLOS, 2014-07)
Hepatitis B is a DNA virus that infects liver cells and can cause both acute and chronic disease. It is believed that both viral and host factors are responsible for determining whether the infection is cleared or becomes chronic. Here we investigate the mechanism of protection by developing a mathematical model of the antibody response following hepatitis B virus (HBV) infection. We fitted the model to data from seven infected adults identified during acute infection and determined the ability of the virus to escape neutralization through overproduction of non-infectious subviral particles, which have HBs proteins on their surface, but do not contain nucleocapsid protein and viral nucleic acids. We showed that viral clearance can be achieved for high anti-HBV antibody levels, as in vaccinated individuals, when: (1) the rate of synthesis of hepatitis B subviral particles is slow; (2) the rate of synthesis of hepatitis B subviral particles is high but either anti-HBV antibody production is fast, the antibody affinity is high, or the levels of pre-existent HBV-specific antibody at the time of infection are high, as could be attained by vaccination. We further showed that viral clearance can be achieved for low equilibrium anti-HBV antibody levels, as in unvaccinated individuals, when a strong cellular immune response controls early infection.
Bayesian Parameter Estimation on Three Models of Influenza
Torrence, Robert Billington (Virginia Tech, 2017-05-11)
Mathematical models of viral infections have been informing virology research for years. Estimating parameter values for these models can lead to understanding of biological values. This has been successful in HIV modeling for the estimation of values such as the lifetime of infected CD8 T-Cells. However, estimating these values is notoriously difficult, especially for highly complex models. We use Bayesian inference and Monte Carlo Markov Chain methods to estimate the underlying densities of the parameters (assumed to be continuous random variables) for three models of influenza. We discuss the advantages and limitations of parameter estimation using these methods. The data and influenza models used for this project are from the lab of Dr. Amber Smith in Memphis, Tennessee.
Bistable Mathematical Model of Neutrophil Migratory Patterns After LPS-Induced Epigenetic Reprogramming
Ciupe, Stanca M.; Boribong, Brittany P.; Kadelka, Sarah; Jones, Caroline N. (2021-02-23)
The highly controlled migration of neutrophils toward the site of an infection can be altered when they are trained with lipopolysaccharides (LPS), with high dose LPS enhancing neutrophil migratory pattern toward the bacterial derived source signal and super-low dose LPS inducing either migration toward an intermediary signal or dysregulation and oscillatory movement. Empirical studies that use microfluidic chemotaxis-chip devices with two opposing chemoattractants showed differential neutrophil migration after challenge with different LPS doses. The epigenetic alterations responsible for changes in neutrophil migratory behavior are unknown. We developed two mathematical models that evaluate the mechanistic interactions responsible for neutrophil migratory decision-making when exposed to competing chemoattractants and challenged with LPS. The first model, which considers the interactions between the receptor densities of two competing chemoattractants, their kinases, and LPS, displayed bistability between high and low ratios of primary to intermediary chemoattractant receptor densities. In particular, at equilibrium, we observe equal receptor densities for low LPS (< 15ng/mL); and dominance of receptors for the primary chemoattractant for high LPS (> 15ng/mL). The second model, which included additional interactions with an extracellular signal-regulated kinase in both phosphorylated and non-phosphorylated forms, has an additional dynamic outcome, oscillatory dynamics for both receptors, as seen in the data. In particular, it found equal receptor densities in the absence of oscillation for super-low and high LPS challenge (< 0.4 and 1.1 376 ng/mL). Predicting the mechanisms and the type of external LPS challenge responsible for neutrophils migration toward pro-inflammatory chemoattractants, migration toward pro-tolerant chemoattractants, or oscillatory movement is necessary knowledge in designing interventions against immune diseases, such as sepsis.
A Bistable Switch in Virus Dynamics Can Explain the Differences in Disease Outcome Following SIV Infections in Rhesus Macaques
Ciupe, Stanca M.; Miller, Christopher J.; Forde, Jonathan E. (Frontiers, 2018-06-06)
Experimental studies have shown that the size and infectious-stage of viral inoculum influence disease outcomes in rhesus macaques infected with simian immunodeficiency virus. The possible contribution to disease outcome of antibody developed after transmission and/or present in the inoculum in free or bound form is not understood. In this study, we develop a mathematical model of virus-antibody immune complex formation and use it to predict their role in transmission and protection. The model exhibits a bistable switch between clearance and persistence states. We fitted it to temporal virus data and estimated the parameter values for free virus infectivity rate and antibody carrying capacity for which the model transitions between virus clearance and persistence when the initial conditions (in particular the ratio of immune complexes to free virus) vary. We used these results to quantify the minimum virus amount in the inoculum needed to establish persistent infections in the presence and absence of protective antibodies.
Deficient Generation of Spike-Specific Long-Lived Plasma Cells in the Bone Marrow After Severe Acute Respiratory Syndrome Coronavirus 2 Infection
Tehrani, Zahra R.; Habibzadeh, Parham; Flinko, Robin; Chen, Hegang; Abbasi, Abdolrahim; Yared, Jean A.; Ciupe, Stanca M.; Lewis, George K.; Sajadi, Mohammad M. (Oxford University Press, 2024-02-14)
Generation of a stable long-lived plasma cell (LLPC) population is the sine qua non of durable antibody responses after vaccination or infection. We studied 20 individuals with a prior coronavirus disease 2019 infection and characterized the antibody response using bone marrow aspiration and plasma samples. We noted deficient generation of spike-specific LLPCs in the bone marrow after severe acute respiratory syndrome coronavirus 2 infection. Furthermore, while the regression model explained 98% of the observed variance in anti-tetanus immunoglobulin G levels based on LLPC enzyme-linked immunospot assay, we were unable to fit the same model with anti-spike antibodies, again pointing to the lack of LLPC contribution to circulating anti-spike antibodies.
The Dynamics of T-Cell Receptor Repertoire Diversity Following Thymus Transplantation for DiGeorge Anomaly
Ciupe, Stanca M.; Devlin, Blythe H.; Markert, Mary L.; Kepler, Thomas B. (PLOS, 2009-06-01)
T cell populations are regulated both by signals specific to the T-cell receptor (TCR) and by signals and resources, such as cytokines and space, that act independently of TCR specificity. Although it has been demonstrated that disruption of either of these pathways has a profound effect on T-cell development, we do not yet have an understanding of the dynamical interactions of these pathways in their joint shaping of the T cell repertoire. Complete DiGeorge Anomaly is a developmental abnormality that results in the failure of the thymus to develop, absence of T cells, and profound immune deficiency. After receiving thymic tissue grafts, patients suffering from DiGeorge anomaly develop T cells derived from their own precursors but matured in the donor tissue. We followed three DiGeorge patients after thymus transplantation to utilize the remarkable opportunity these subjects provide to elucidate human T-cell developmental regulation. Our goal is the determination of the respective roles of TCR-specific vs. TCR-nonspecific regulatory signals in the growth of these emerging T-cell populations. During the course of the study, we measured peripheral blood T-cell concentrations, TCRβ V gene-segment usage and CDR3- length spectratypes over two years or more for each of the subjects. We find, through statistical analysis based on a novel stochastic population-dynamic T-cell model, that the carrying capacity corresponding to TCR-specific resources is approximately 1000-fold larger than that of TCR-nonspecific resources, implying that the size of the peripheral T-cell pool at steady state is determined almost entirely by TCR-nonspecific mechanisms. Nevertheless, the diversity of the TCR repertoire depends crucially on TCR-specific regulation. The estimated strength of this TCR-specific regulation is sufficient to ensure rapid establishment of TCR repertoire diversity in the early phase of T cell population growth, and to maintain TCR repertoire diversity in the face of substantial clonal expansion-induced perturbation from the steady state.
Editorial: Integrative Computational Systems Biology Approaches in Immunology and Medicine
Kaderali, Lars; Theis, Fabian; Ganusov, Vitaly V.; Ciupe, Stanca M.; Mehr, Ramit; Ribeiro, Ruy M.; Hernandez-Vargas, Esteban A. (Frontiers, 2019-01-23)
New technologies provide the ability to generate massive amounts of immunological data in health and disease. However, this “Big data” trend is becoming more challenging and unfeasible by the steep increase in the number of different pieces of information and the complexity of large datasets. Thus, systematic and tractable approaches that integrate a variety of biological and medical research data at multiple scales into mathematical, statistical, and computational models are crucial to harness knowledge and to develop new therapies...
The effect of model structure and data availability on Usutu virus dynamics at three biological scales
Heitzman-Breen, Nora; Liyanage, Yuganthi R.; Duggal, Nisha; Tuncer, Necibe; Ciupe, Stanca M. (Royal Society, 2024-02-07)
Understanding the epidemiology of emerging pathogens, such as Usutu virus (USUV) infections, requires systems investigation at each scale involved in the host-virus transmission cycle, from individual bird infections, to bird-to-vector transmissions, and to USUV incidence in bird and vector populations. For new pathogens field data are sparse, and predictions can be aided by the use of laboratory-type inoculation and transmission experiments combined with dynamical mathematical modelling. In this study, we investigated the dynamics of two strains of USUV by constructing mathematical models for the within-host scale, bird-to-vector transmission scale and vector-borne epidemiological scale. We used individual within-host infectious virus data and per cent mosquito infection data to predict USUV incidence in birds and mosquitoes. We addressed the dependence of predictions on model structure, data uncertainty and experimental design. We found that uncertainty in predictions at one scale change predicted results at another scale. We proposed in silico experiments that showed that sampling every 12 hours ensures practical identifiability of the within-host scale model. At the same time, we showed that practical identifiability of the transmission scale functions can only be improved under unrealistically high sampling regimes. Instead, we proposed optimal experimental designs and suggested the types of experiments that can ensure identifiability at the transmission scale and, hence, induce robustness in predictions at the epidemiological scale.
Identifiability investigation of within-host models of acute virus infection.
Liyanage, Yuganthi R.; Heitzman-Breen, Nora; Tuncer, Necibe; Ciupe, Stanca M. (Cold Spring Harbor Laboratory, 2024-05-10)
Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model's ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is important for informing model development and experimental design. To address sources of uncertainty in parameter estimation, we use four mathematical models of influenza A infection with increased degrees of biological realism. We test the ability of each model to reveal its parameters in the presence of unlimited data by performing structural identifiability analyses. We then refine the results by predicting practical identifiability of parameters under daily influenza A virus titers alone or together with daily adaptive immune cell data. Using these approaches, we present insight into the sources of uncertainty in parameter estimation and provide guidelines for the types of model assumptions, optimal experimental design, and biological information needed for improved predictions.
Identifiability of parameters in mathematical models of SARS-CoV-2 infections in humans
Ciupe, Stanca M.; Tuncer, Necibe (Nature Portfolio, 2022-08-27)
Determining accurate estimates for the characteristics of the severe acute respiratory syndrome coronavirus 2 in the upper and lower respiratory tracts, by fitting mathematical models to data, is made difficult by the lack of measurements early in the infection. To determine the sensitivity of the parameter estimates to the noise in the data, we developed a novel two-patch within-host mathematical model that considered the infection of both respiratory tracts and assumed that the viral load in the lower respiratory tract decays in a density dependent manner and investigated its ability to match population level data. We proposed several approaches that can improve practical identifiability of parameters, including an optimal experimental approach, and found that availability of viral data early in the infection is of essence for improving the accuracy of the estimates. Our findings can be useful for designing interventions.
Immunoepidemiological Modeling of Dengue Viral Infection
Nikin-Beers, Ryan Patrick (Virginia Tech, 2018-04-25)
Dengue viral infection is a mosquito-borne disease with four distinct strains, where the interactions between these strains have implications on the severity of the disease outcomes. The two competing hypotheses for the increased severity during secondary infections are antibody dependent enhancement and original antigenic sin. Antibody dependent enhancement suggests that long-lived antibodies from primary infection remain during secondary infection but do not neutralize the virus. Original antigenic sin proposes that T cells specific to primary infection dominate cellular immune responses during secondary infections, but are inefficient at clearing cells infected with non-specific strains. To analyze these hypotheses, we developed within-host mathematical models. In previous work, we predicted a decreased non-neutralizing antibody effect during secondary infection. Since this effect accounts for decreased viral clearance and the virus is in quasi-equilibrium with infected cells, we could be accounting for reduced cell killing and the original antigenic sin hypothesis. To further understand these interactions, we develop a model of T cell responses to primary and secondary dengue virus infections that considers the effect of T cell cross-reactivity in disease enhancement. We fit the models to published patient data and show that the overall infected cell killing is similar in dengue heterologous infections, resulting in dengue fever and dengue hemorrhagic fever. The contribution to overall killing, however, is dominated by non-specific T cell responses during the majority of secondary dengue hemorrhagic fever cases. By contrast, more than half of secondary dengue fever cases have predominant strain-specific T cell responses. These results support the hypothesis that cross-reactive T cell responses occur mainly during severe disease cases of heterologous dengue virus infections. Finally, using the results from our within-host models, we develop a multiscale model of dengue viral infection which couples the within-host virus dynamics to the population level dynamics through a system of partial differential equations. We analytically determine the relationship between the model parameters and the characteristics of the solutions, and find thresholds under which infections persist in the population. Furthermore, we develop and implement a full numerical scheme for our model.
Incorporating Intracellular Processes in Virus Dynamics Models
Ciupe, Stanca M.; Conway, Jessica M. (MDPI, 2024-04-30)
In-host models have been essential for understanding the dynamics of virus infection inside an infected individual. When used together with biological data, they provide insight into viral life cycle, intracellular and cellular virus–host interactions, and the role, efficacy, and mode of action of therapeutics. In this review, we present the standard model of virus dynamics and highlight situations where added model complexity accounting for intracellular processes is needed. We present several examples from acute and chronic viral infections where such inclusion in explicit and implicit manner has led to improvement in parameter estimates, unification of conclusions, guidance for targeted therapeutics, and crossover among model systems. We also discuss trade-offs between model realism and predictive power and highlight the need of increased data collection at finer scale of resolution to better validate complex models.
Mathematical and Numerical Investigation of Immune System Development and Function
Kadelka, Mirjam Sarah (Virginia Tech, 2020-04-14)
Mathematical models have long been used to describe complex biological interactions with the aim of predicting mechanistic interactions hard to distinguish from data. This dissertation uses modeling, mathematical analyses, and data fitting techniques to provide hypotheses on the mechanisms of immune response formation and function. The immune system, comprised of the innate and adaptive immune responses, is responsible for protecting the body against invading pathogens, with disease or vaccine induced immune memory leading to fast responses to subsequent infections. While there is some agreement about the underlying mechanisms of adaptive immune memory, innate immune memory is poorly understood. Stimulation with lipopolysaccharide induces differential phenotypes in innate immune cells depending on the strength of the stimulus, such that a secondary lipopolysaccharide encounter of a constant dose results in either strong or weak inflammatory cytokine expression. We model the biochemical kinetics of three molecules involved in macrophages responses to lipopolysaccharide and find that once a macrophage is programed to show a weak inflammatory response this cannot be reverted. Contrarily, a secondary lipopolysaccharide stimulus of a very high dose or applied prior to waning of the effects of the primary stimulus can induce a phenotype switch in macrophages initially programed to show strong inflammatory responses. Some pathogens, such as the hepatitis B virus, have developed strategies that hinder an efficient innate immune response. Hepatitis B virus infection is a worldwide pandemic with approximately 257 million chronically infected people. One beneficial event in disease progression is the seroclearance of hepatitis B e antigen often in combination with hepatitis B antibody formation. We propose mathematical models of within-host interactions and use them to predict that hepatitis B e antibody formation causes hepatitis B e antigen seroclearance and the subsequent reactivation of cytotoxic T cell immune responses. We use the model to quantify the time between antibody formation and antigen clearance and the average monthly hepatocyte turnover during that time. We further expand the study of hepatitis B infection, by investigating the kinetics of the virus under an experimental drug administered during a clinical trial. Available drugs usually fail to induce hepatitis B s antigen clearance, defined as the functional cure point of chronic hepatitis B infections. Drug therapy clinical trials that combined RNA interference drug ARC-520 with entecavir have shown promising results in reducing hepatitis B s antigen titers. We develop pharmacokinetic-pharmacodynamic models describing the mechanistic interactions of the drugs, hepatitis B virus DNA, and virus proteins. We fit the model to clinical trial data and predict that ARC-520 alone is responsible for the reduction of hepatitis B s and e antigens, while entecavir is the driving force behind viral reduction. This work was supported by Simons Foundation, Grant No. 427115, and National Science Foundation, Grant No. 1813011.
Mathematical investigation of HBeAg seroconversion
Kadelka, Sarah; Ciupe, Stanca M. (2019-08-20)
Spontaneous or drug-induced loss of hepatitis B e antigen is considered a beneficial event in the disease progression of chronic hepatitis B virus infections. Mathematical models of within-host interactions are proposed; which provide insight into hepatitis B e antibody formation, its influence on hepatitis B e antigen seroclearance, and reversion of anergic cytotoxic immune responses. They predict that antibody expansion causes immune activation and hepatitis B e antigen seroclearance. Quantification of the time between antibody expansion and hepatitis B e antigen seroclearance in the presence and absence of treatment shows that potent short-term treatment speeds up the time between antibody expansion and hepatitis B e antigen seroclearance. The monthly hepatocyte turnover during this time can be increased or decreased by treatment depending on the amount of core promoter or precore mutated virus produced. The results can inform human interventions.
A Mathematical Model of a Denitrification Metabolic Network in Pseudomonas aeruginosa
Arat, Seda (Virginia Tech, 2012-11-30)
Lake Erie, one of the Great Lakes in North America, has witnessed recurrent summertime low oxygen dead zones for decades. This is a yearly phenomenon that causes microbial production of the greenhouse gas nitrous oxide from denitrification. Complete denitrification is a microbial process of reduction of nitrate to nitrogen gas via nitrite, nitric oxide, and greenhouse gas nitrous oxide. After scanning the microbial community in Lake Erie, Pseudomonas aeruginosa is decided to be examined, not because it is abundant in Lake Erie, but because it can perform denitrification under anaerobic conditions. This study focuses on a mathematical model of the metabolic network in Pseudomonas aeruginosa under denitrification and testable hypotheses generation using polynomial dynamical systems and stochastic discrete dynamical systems. Analysis of the long-term behavior of the system changing the concentration level of oxygen, nitrate, and phosphate suggests that phosphate highly affects the denitrification performance of the network.
Mathematical model of broadly reactive plasma cell production
Erwin, Samantha; Childs, Lauren M.; Ciupe, Stanca M. (Nature Research, 2020)
Strain-specific plasma cells are capable of producing neutralizing antibodies that are essential for clearance of challenging pathogens. These neutralizing antibodies also function as a main defense against disease establishment in a host. However, when a rapidly mutating pathogen infects a host, successful control of the invasion requires shifting the production of plasma cells from strain-specific to broadly reactive. In this study, we develop a mathematical model of germinal center dynamics and use it to predict the events that lead to improved breadth of the plasma cell response. We examine scenarios that lead to germinal centers that are composed of B-cells that come from a single strain-specific clone, a single broadly reactive clone or both clones. We find that the initial B-cell clonal composition, T-follicular helper cell signaling, increased rounds of productive somatic hypermutation, and B-cell selection strength are among the mechanisms differentiating between strain-specific and broadly reactive plasma cell production during infections. Understanding the contribution of these factors to emergence of breadth may assist in boosting broadly reactive plasma cells production.
A Mathematical Model of the Iron Regulatory Network in Aspergilus Fumigatus
Brandon, Madison Gayle (Virginia Tech, 2013-05-23)
Aspergillus fumigatus is an opportunistic fungal pathogen responsible for invasive aspergillosis in immunocompromised individuals. Current detection and treatment strategies for invasive aspergillosis, as well as other invasive fungal infections, are poor. Iron has been shown to be essential for Aspergillus fumigatus virulence. Furthermore, mechanisms in the iron regulatory network are believed to be potential drug targets since iron management in fungi is vastly different from that in mammals and other eukaryotes. Therefore a better understanding of iron homeostasis in Aspergillus fumigatus could help improve drug therapies for invasive aspergillosis. In this research a discrete model of iron uptake, storage and utilization in Aspergillus fumigatus with particular focus on siderophore-mediated iron acquisition is constructed. The model predicts oscillations in gene expression as the fungus adapts to a switch from an iron depleted to an iron replete environment. The model is validated via in vitro experiments.
Mathematical Modeling of Circadian Gene Expression in Mammalian Cells
Yao, Xiangyu (Virginia Tech, 2023-06-28)
Circadian rhythms in mammals are self-sustained repeating activities driven by the circadian gene expression in cells, which is regulated at both transcriptional and posttranscriptional stages. In this work, we first used mathematical modeling to investigate the transcriptional regulation of circadian gene expression, with a focus on the mechanisms of robust genetic oscillations in the mammalian circadian core clock. Secondly, we built a coarse-grained model to study the post-transcriptional regulation of the rhythmicities of poly(A) tail length observed in hundreds of mRNAs in mouse liver. Lastly, we examined the application of Sobol indices, which is a global sensitivity analysis method, to mathematical models of biological oscillation systems, and proposed two methods tailored for the calculation of circular Sobol indices. In the first project, we modified the core negative feedback loop in a mathematical model of the mammalian genetic oscillator so that the unrealistic tight binding between the repressor PER and the activator BMAL1 is relaxed for robust oscillations. By studying the modified extended models, we found that the auxiliary positive feedback loop, rather than the auxiliary negative feedback loop, makes the oscillations more robust, yet they are similar when accounting for circadian rhythms (~24h period). In the second project, we investigated the regulation of rhythmicities in poly(A) tail length by four coupled rhythmic processes, which are transcription, deadenylation, polyadenylation, and degradation. We found that rhythmic deadenylation is the strongest contributor to the rhythmicity in poly(A) tail length and the rhythmicity in the abundance of the mRNA subpopulation with long poly(A) tails. In line with this finding, the model further showed that the experimentally observed distinct peak phases in the expression of deadenylases, regardless of other rhythmic controls, can robustly cluster the rhythmic mRNAs by their peak phases in poly(A) tail length and abundance of the long-tailed subpopulation. In the last project, we reviewed the theoretical basis of Sobol indices and identified potential problems when it is applied to mathematical models of biological oscillation systems. Based on circular statistics, we proposed two methods for the calculation of circular Sobol indices and compared their performance with the original Sobol indices in several models. We found that though the relative rankings of the contribution from parameters are the same across three methods, circular Sobol indices can better quantitatively distinguish the contribution of individual parameters. Through this work, we showed that mathematical modeling combined with sensitivity analysis can help us understand the mechanisms underlying the circadian gene expression in mammalian cells. Also, testable predictions are made for future experiments and new ideas are provided that can enable potential chronopharmacology research.
Mathematical Modeling of Dengue Viral Infection
Nikin-Beers, Ryan Patrick (Virginia Tech, 2014-06-06)
In recent years, dengue viral infection has become one of the most widely-spread mosquito-borne diseases in the world, with an estimated 50-100 million cases annually, resulting in 500,000 hospitalizations. Due to the nature of the immune response to each of the four serotypes of dengue virus, secondary infections of dengue put patients at higher risk for more severe infection as opposed to primary infections. The current hypothesis for this phenomenon is antibody-dependent enhancement, where strain-specific antibodies from the primary infection enhance infection by a heterologous serotype. To determine the mechanisms responsible for the increase in disease severity, we develop mathematical models of within-host virus-cell interaction, epidemiological models of virus transmission, and a combination of the within-host and between-host models. The main results of this thesis focus on the within-host model. We model the effects of antibody responses against primary and secondary virus strains. We find that secondary infections lead to a reduction of virus removal. This is slightly different than the current antibody-dependent enhancement hypothesis, which suggests that the rate of virus infectivity is higher during secondary infections due to antibody failure to neutralize the virus. We use the results from the within-host model in an epidemiological multi-scale model. We start by constructing a two-strain SIR model and vary the parameters to account for the effect of antibody-dependent enhancement.

Browsing by Author "Ciupe, Stanca M."

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