Characterization of an interferon-producing oncolytic vesicular stomatitis virus

Type: Undergraduate
Author(s): Ayo Agboola Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy

Enhancing Graphene Quantum Dot Fluorescence with Surfactant-Stabilized Dispersion

Type: Undergraduate
Author(s): Judah Crawford Physics & Astronomy Mason McClure Physics & Astronomy
Advisor(s): Anton Naumov Physics & Astronomy

Graphene quantum dots GQDs possess broad potential in bioimaging and optoelectronics due to their unique optical properties, tunable structure, aqueous solubility, and minimal in vivo and in vitro toxicity. However, despite their solubility, GQD fluorescence may be quenched through interactions with water molecules and aggregation via non radiative decay pathways that reduce emission efficiency. Inspired by the ability of surfactants to prevent quenching interactions for single walled carbon nanotubes, we investigate their utility in preserving GQD fluorescence. Five structurally distinct surfactants, sodium dodecyl sulfate SDS, sodium dodecylbenzene sulfonate SDBS, sodium deoxycholate SDC, sodium cholate SC, and Pluronic F127, are tested across a range of concentrations for preserving fluorescence of top down and bottom up synthesized GQDs to determine optimal conditions. This work reveals that surfactant structure and concentration can non-linearly affect GQD emission in the visible and near-infrared, with SC and SDC providing maximum concentration dependent fluorescence increase. Zeta potential and dynamic light scattering measurements are conducted for each surfactant and GQD system to quantify interfacial charge, colloidal stability, and aggregate size distributions. The present study provides mechanistic understanding of how surfactants influence GQD photophysics, offering strategies to optimize GQD based probes for biomedical imaging and photonic applications establishing a structure-to-function framework that links solution phase organization to fluorescence emission.

Modelling Virus-Mediated Cell-Cell Fusion using a Probabilistic Agent-Based Model

Type: Graduate
Author(s): Anthony Gerg Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy

Viral entry in a host cell is mediated by interacting viral fusion proteins and cell receptors. After entry, newly translated viral fusion proteins can end up on the surface of the infected cell. If the infected cell comes into contact with a cell expressing the associated receptor, the interaction can result in membrane fusion. The result of this fusion is a multi-nucleated cell, called a syncytium. Syncytia can cause an increase in severity and duration of an infection, as well as cause damage to the surrounding tissue. Syncytia formation is heavily dependent on spatial interactions and some models are not able to represent this component whatsoever. Agent-based models (ABMs) can accurately represent the temporal and spatial components of syncytia formation by simulating interactions between individual cells. We developed an ABM that can model syncytia formation for up to one million cells at a time. Implementing this model computationally, we have begun fitting to cell-cell fusion experimental data. This model allows us to get new spatial parameters that have never been looked into before. By investigating the spatial aspects, we will develop a better understanding of the role of syncytia during viral infections.

The Impact of Interferon on the Antiviral Effects of Defective Interfering Particles

Type: Undergraduate
Author(s): Lucianne Gonzalez Physics & Astronomy
Advisor(s): Hana Dobrovonly Physics & Astronomy

Establishing the Role of Mucociliary Clearance for Lower Respiratory Tract Infections through a Compartmental Model

Type: Graduate
Author(s): Geoffrey Hennessy Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy

The lining of the human respiratory tract (HRT) has a layer of ciliated cells known as an epithelium. When exposed to virus, these cells actively push virus into mucous layers lining the epithelium and then funnel this mucous up and out of the human respiratory tract. This process is called mucociliary clearance (MCC) and is the first line of defense against a viral infection. We know that MCC plays a role in preventing respiratory infections, but we know little else. We hypothesize that, under the right conditions, MCC prevents infection by limiting the ability for virus to enter the lower respiratory tract. To test this, we constructed a compartmental model that uses a system of diffusion-driven partial differential equations to describe the virus propagation in the HRT as a travelling wave front with an advection term included to approximate MCC.  Our model shows that MCC can change the waveform of the virus propagation, and suggests that there exists a critical advection speed that prevents virus from entering the lower respiratory tract.

A mathematical model of influenza viral entry

Type: Undergraduate
Author(s): Ayur Madupur Physics & Astronomy
Advisor(s): Hana Dobrovonly Physics & Astronomy

Influenza virus causes periodic pandemics and thousands of deaths annually, but many of the details of the viral replication cycle are still poorly understood. This study develops a mathematical model of the dynamic transitions of a virus from the extracellular space through the initial intracellular replication processes. These stages include: binding, endocytosis, HA Acidification, Fusion, and Uncoating. Experimental data from the viral entry phases were fit to a system of differential equations, which represent the biological processes. The model parameters were estimated using optimization techniques that minimize the sum of squared residuals, thereby aligning model predictions with observations. An identifiability analysis was performed to see which parameters can be estimated with the given model and available data. We find that the model fits the experimental data well with identifiable parameters, allowing us to characterize the different stages of viral entry. The model can be used to compare different viral strains or treatment options, in addition to helping explain the kinetics of viral entry.

Vaccine optimization using a simplified epidemiological model

Type: Undergraduate
Author(s): Anvitha Pasam Physics & Astronomy
Advisor(s): Hana Dobrovonly Physics & Astronomy

Pandemics require quick decisions about how to distribute a limited number of vaccines, even when the disease is not fully understood and vaccine delivery is limited. We create a disease model that divides the population into groups based on how likely they are to be hospitalized and how likely they are to get infected, so we can test different group-based vaccination strategies. We compare vaccinating only one group, simple step-by-step priority policies that vaccinate groups for set time periods, and a sensitivity analysis to see which model factors most affect outcomes.

We find that vaccinating people who are both high-risk for hospitalization and highly likely to become infected leads to the biggest reductions in total hospitalized time and deaths, while vaccinating lower-risk groups gives little improvement in severe outcomes. A short step-by-step policy that quickly prioritizes high-risk groups can reduce infections and deaths within about 20 days. The sensitivity analysis shows that the death rate and the rate at which infected people move into hospitalization have the strongest influence on severe outcomes, showing that hospital and clinical processes matter a lot in addition to vaccination. Overall, these results support clear, practical, and easy-to-apply prioritization rules for reducing severe disease when vaccine supply is limited.

Protein Binding on Functionalized Charged Graphene Quantum Dots

Type: Graduate
Author(s): Himish Paul Physics & Astronomy Ugur Topkiran Physics & Astronomy Diya Vashani Physics & Astronomy
Advisor(s): Anton Naumov Physics & Astronomy

Graphene quantum dots (GQDs) have emerged in nanobiotechnology as useful tools for numerous biomedical applications, including non-invasive cellular imaging, drug delivery, and gene targeting. Several studies have shown the successful uptake of GQDs in healthy and cancerous cells. While some in vitro and in vivo studies highlight mechanisms underlying GQD internalization in cells, there is a gap in our understanding of GQD interactions with complex biological media, such as blood serum. Proteins in biofluid environments adsorb to the surface of nanoparticles such as carbon nanotubes, forming the “protein corona.” Graphene quantum dots have an abundance of charged surface functional groups, which are likely to interact with complementary charged regions in proteins. Herein, we investigate the interactions of individual proteins with negatively charged sodium citrate and reduced graphene oxide-derived GQDs, as well as positively charged nitrogen-doped GQDs. This study will advance our understanding of protein-GQD interactions in physiological environments, ultimately guiding the optimization of GQDs for biomedical applications.

Characterizing the effect of CD388 prophylactic treatment

Type: Undergraduate
Author(s): Avir Sankara Physics & Astronomy Krish Penumarthi Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy

The effect of cell-to-cell transmission on viral coinfections

Type: Undergraduate
Author(s): Sanjith Singaravelan Physics & Astronomy
Advisor(s): Hana Dobrovonly Physics & Astronomy

About one quarter of patients hospitalized with influenza-like illnesses are infected with more than one respiratory virus. Coinfections can lead to more serious outcomes for patients and are more complex to treat than infections with single viruses. Mathematical models can be used to help us understand the dynamics of viral coinfections and optimize treatment. Previous mathematical models of viral coinfections assume a cell-free transmission pathway with virus leaving one cell and traveling to nearby uninfected cells. However, viruses can also tunnel directly from one cell to another, which can affect how coinfecting viruses interact. This project analyzes a system of coupled ordinary differential equations that includes both cell-free and cell-to-cell transmission to better replicate actual viral spread. We measure coinfection duration for combinations of five common respiratory viruses as a function of the amount of cell-to-cell transmission. We find that coinfection duration depends nonlinearly on the cell-to-cell transmission rates, with differing patterns for different coinfecting viruses. This study highlights the importance of considering different transmission modes when modeling viral dynamics.

Precursor-Dependent Optical and Structural Properties of Eleven NIR-Emissive Graphene Quantum Dots for Bioimaging Applications

Type: Graduate
Author(s): Diya Vashani Physics & Astronomy Himish Paul Physics & Astronomy Ugur Topkiran Physics & Astronomy
Advisor(s): Anton Naumov Physics & Astronomy

Characterization of oncolytic adenovirus ICVB-1042

Type: Undergraduate
Author(s): Lakshitha Vengadeswaran Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy

Oncolytic adenoviruses are promising cancer therapies because they can selectively infect and destroy tumor cells, however their replication in cancer cells is sometimes limited leading to incomplete tumor suppression. Recently, researchers have started to modify viruses to enhance their replication in cancer cells. In this study, we use a system of ordinary differential equations (ODEs) to model tumor growth and compare viral treatment dynamics of a modified oncolytic adenovirus ICVB-1042 and a wild-type adenovirus type 5 (Wt Ad5). The model was fit to experimental allowing us to estimate important model parameters for both viruses: infection rate, infected cell death rate, rate of cell protection by the immune response, rate of cell resistance loss, viral production rate, and viral clearance rate. We found differences in the viral production rates and the clearance rates between the two viruses, providing insight into how genetic modifications have altered viral dynamics. These findings highlight how viral properties determine the effectiveness of oncolytic virus therapy.

Studying the Influence of Structural Differences between GaOOH Microparticles on their Antibacterial Efficiency.

Type: Graduate
Author(s): John Brannon Physics & Astronomy Pavan Ahluwalia Physics & Astronomy Arabella Blom Physics & Astronomy Louise Hutchison Biology Dustin Johnson Physics & Astronomy Sriman Reddi Physics & Astronomy
Advisor(s): Yuri Strzhemechny Physics & Astronomy Shauna McGillivray Biology
Location: Basement, Table 4, Position 1, 11:30-1:30

Ga2O3, an ultrawide-bandgap semiconducting material, sees widespread use in optoelectronic, pharmaceutical, and other industrial applications. Additionally, as antibiotic resistance grows, interest rises in the antibacterial properties of Ga2O3 and other gallium-containing compounds. In many cases, GaOOH is a precursor to synthesis of Ga2O3 with similar physiochemical properties. For microparticles, surface effects become heavily amplified. In particular, the surface effects may significantly influence antibacterial action. We synthesize GaOOH and Ga2O3 microparticles via hydrothermal growth. We employ scanning electron microscopy to image samples and energy dispersive X-ray spectroscopy to characterize the stoichiometry. X-ray diffraction spectroscopy is used by us to monitor bulk structural differences between the GaOOH precursor and Ga2O3. To monitor crystal defects we utilize photoluminescence spectroscopy. For antibacterial assays, we test our materials against Staphylococcus aureus bacteria using optical density measurement at 600 nm.

Interplay of syncytia and antibodies during viral infections

Type: Undergraduate
Author(s): Aubrey Chiarelli Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy
Location: FirstFloor, Table 6, Position 1, 11:30-1:30

Several viruses can cause cells to fuse into large multinucleated cells called syncytia. Syncytia formation allows the virus to spread without entering the extracellular space, where it might be exposed to immune responses. However, there is evidence that antibodies can also hinder the fusion process. This project uses mathematical analysis to find different possible infection outcomes. A stability analysis of the coinfection model is used to find the fixed points of the model and their stability. This gives us parameter space regions that lead to different possible infection outcomes. Simulations were made to verify the mathematical analysis and see how different syncytia formation properties affect the resulting dynamics. These findings could help develop strategies for controlling viral spread.

Surfactant Effect on Flourescence of Graphene Quantum Dots

Type: Undergraduate
Author(s): Judah Crawford Physics & Astronomy Mason McClure Physics & Astronomy
Advisor(s): Anton Naumov Physics & Astronomy
Location: Basement, Table 1, Position 3, 1:45-3:45

Graphene Quantum Dots (GQDs) are nanoscale carbon based graphene sheets that exhibit unique fluorescent properties throughout a wide range of wavelengths. Given their uniquely small size, low toxicity, biocompatibility, and fluorescent capabilities, GQDs have many unique and important roles. To name a few, GQDs are used in drug delivery, fluorescent imaging, and biosensing thanks to their unique ability to fluoresce under different wavelengths of light. Furthermore, there are different types of GQDs with their own unique properties. Knowing this, five amphipathic molecules, called surfactants, were added to two different types of GQDs to test if they would impact the resulting fluorescence. Furthermore, concentrations of these added surfactants were varied to test how different concentrations of a given surfactant might affect the fluorescence for a given GQD. We observed that some of these surfactants provided a beneficial boost to GQDs fluorescence, while others slightly inhibited the fluorescence. Moreover, we saw that the increase in fluorescence varied based on the concentration of surfactant added yielding lower fluorescence for extremely low and high concentrations, while increasing the fluorescence at a more moderate concentration.

Right Place, Right Time: GQDs for Controlled Chemotherapy Release

Type: Undergraduate
Author(s): Walter Daniel Engineering Ugur Topkiran Physics & Astronomy Anna Tucci Engineering
Advisor(s): Anton Naumov Physics & Astronomy
Location: Third Floor, Table 6, Position 2, 1:45-3:45

With cancer rates increasing at an alarming rate, many traditional methods for cancer treatment begin to feel outdated. This is where engineering nanomaterials, such as Graphene Quantum Dots (GQDs), offer a promising approach to making chemotherapy a more targeted treatment and therefore minimizing the side effects. This study focuses on optimizing drug delivery mechanisms using GQDs, specifically Reduced Graphene Quantum Dots (RGQDs) synthesized via a top-down approach from reduced graphene oxide, and Hyaluronic Acid Graphene Quantum Dots (HAGQDs) synthesized bottom-up from hyaluronic acid. The process is done by loading chemotherapeutics Gemcitabine, Paclitaxel, and Doxorubicin (DOX) HCl onto GQDs through sonication, this is followed by a centrifugal purification which isolates properly drug-loaded GQDs. To evaluate their controlled release, photothermal properties of GQDs are utilized. Samples are excited with an 808 nm laser at 1, 5, and 10 minutes, and they are later compared to a control group. This analysis provides insights into how laser stimulation affects drug release efficiency, paving the way for advancements in GQD based cancer treatments.

Quantifying Spatial Heterogeneity of Syncytial Cells using Alpha Shapes

Type: Graduate
Author(s): Anthony Gerg Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy
Location: FirstFloor, Table 6, Position 2, 1:45-3:45

We introduce a structural method used for quantifying the spatial heterogeneity(or clumpiness) of viral syncytial cells in a transfection bioassay. The solution lies in an inter-disciplinary process based on simplicial topology being applied to a biological system. Our method revolves around using topological theories including Delaunay tessellations and Voronoi graphs to signify cell-cell interaction probability. The main emphasis is the subset of Delaunay tessellation called Alpha shapes. By applying a filtration to the overall Delaunay tessellation, we can obtain unique Alpha Shapes that have cell-cell interactions removed. The emphasis of the filtration is to find the correct shape where there were no connection crossing syncytia, only between healthy neighborhoods of cells. The process allows for the associated alpha number to be assigned to the clumpiness. Alpha numbers can then be used to separate different bioassays, or quantify temporal changes found in a single viral transfection due to syncytia.

Analyzing a Mathematical Model for Virus Propagation of the Trachea

Type: Graduate
Author(s): Geoffrey Hennessy Physics & Astronomy
Advisor(s): Hana Drobrovolny Physics & Astronomy
Location: FirstFloor, Table 4, Position 1, 1:45-3:45

In virology, mathematical models are often deployed to examine and test various behaviors of viruses. For example, one for the flu it is speculated that lethality is linked to the virus’s ability to propagate down the trachea, specifically in how ciliated cells push virus up through mucous layers in a process known as advection. We propose a model for this process, believing that this model can reveal links and critical points between lethality and advection. To solve this model, we utilize three techniques: Laplacian transform, non-linear analysis, and quasi-state analysis. We discuss the findings of each method.

Inflows or Outflows: Tracing the Path of Gaseous Arms in the LMC

Type: Graduate
Author(s): April Horton Physics & Astronomy Suraj Poudel Physics & Astronomy
Advisor(s): Kat Barger Physics & Astronomy
Location: Third Floor, Table 6, Position 1, 1:45-3:45

Our neighboring galaxies, the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC), interact with each other as they move through the hot, outer region of the Milky Way. This interaction can pull and sweep away gas from the edges of the galaxies, forming large, stretched-out clouds of gas. The LMC has two gas filaments that resemble arms, which connect to a region where stars are formed, possibly hinting toward their origin or their final destination. In this study, we used radio observations and data from the Hubble Space Telescope to search for signs of these gas arms near the star-forming region. We find a continuous stream of gas that could be the arms located at least partially in front of the LMC. The positioning of these arms raises two competing questions: 1) Is the gas flow fueling new star formation in the LMC, or 2) Is gas from exploded stars in the LMC flowing out into these arms? While the inflow of gas makes sense for these gas flows, we also conducted simulations of outflows from the starburst region. Our results suggest that it is possible for debris from exploded stars to be swept into the arms. Future observations will help us better reconstruct the arms’ evolutionary history.

Antiviral Treatment in Syncytia Forming Viruses

Type: Undergraduate
Author(s): Kiara Johnson Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy
Location: SecondFloor, Table 6, Position 1, 11:30-1:30

Some viruses have the ability to form syncytia. Syncytia are multi-nucleated cells formed via membrane fusion. Syncytia formation allows viruses to spread infection to other cells without entering the extracellular space where it could be exposed to antiviral drugs or immune responses such as antibodies. This project explores how syncytia formation can help viruses avoid antiviral drugs. Drug efficacy parameters are applied to a mathematical model of differential equations to explore the impact of antiviral drugs on cell infection, cell fusion, and viral production to model respiratory syncytial virus. The models show that as syncytia formation increases the drugs become less effective. This information will help physicians treat patients with syncytia forming viruses.

Mathematical Modeling of Antiviral Drug Mechanisms, GHP-88309 and ERDRP-0519, for Measles Treatment

Type: Undergraduate
Author(s): Shriya Kaza Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy
Location: SecondFloor, Table 7, Position 2, 11:30-1:30

After the COVID-19 pandemic, over 40 million children worldwide are at risk of measles due to delayed vaccination and temporary SARS-CoV-2 viral dominance. The lasting immunosuppression caused by the disease presents a major health threat, and treatment options are urgently needed, especially for low- and middle-income countries. The manuscript by Cox et al. (2024) explores features of canine distemper virus (CDV) in ferrets, using this model as a surrogate for measles to evaluate two possible antiviral treatments, ERDRP-0519 and GHP-88309. Ferrets were infected with a lethal challenge of CDV and treated with either drug or therapeutic vaccination. We aim to characterize both the infection dynamics and efficacy of the two drug treatments using the data from the PBMC (peripheral blood mononuclear cell) associated viremia titers of CDV infected ferrets and the lymphocyte counts measured during the duration of the study. A differential mathematical model was fitted to the experimental data by minimizing the sum of squared residuals (SSR), and errors in the parameter fits were estimated using Monte Carlo Markov Chain (MCMC). We visualized the key parameter distributions for each dataset using histograms, allowing us to directly compare how each treatment influences infection dynamics. The results revealed that ERDRP-0519 reduced viral entry and enhanced clearance while GHP-88309 improved target cell growth and increased the rate of infected cell death. These findings suggest that both drugs are potentially effective measles treatment options, with ERDRP-0519 having a direct antiviral effect and GHP-88309 aiding in immune recovery. Overall, these insights provide a foundation for optimizing treatment strategies and highlight the potential for both drugs to combat measles and related morbillivirus infections, especially in areas with limited resources and vaccines.

Cytokine enhancement of oncolytic Sindbis virus

Type: Undergraduate
Author(s): Shriya Makam Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy
Location: Basement, Table 11, Position 1, 1:45-3:45

Gliomas account for approximately 27% of all primary central nervous system tumors and exhibit highly aggressive growth patterns, making conventional treatments ineffective. Previous research has demonstrated that a replication-competent Sindbis virus (SINV) combined with cytokines (IL-7, IL-12, and GM-CSF) shows promising results in slowing down glioma progression. While prior research demonstrated that SINV combined with cytokines reduces tumor growth, a quantitative understanding of its effects remains limited. This study aims to develop and fit a mathematical model of oncolytic virus infection to data from previous research to quantify key biological processes in glioma treatment. By parameterizing the Sindbis virus-glioma interaction and estimating the effects of cytokine therapy, this model aims to evaluate the efficacy of different SINV variants, with and without cytokine combinations, in controlling tumor growth. We use an Ordinary Differential Equation (ODE) model to describe tumor growth inhibition by the oncolytic SINVs. The model includes variables for uninfected and infected tumor cells, viral load, and cytokine concentration. The data extracted from published tumor growth curves will be used to estimate key parameters, including viral replication rate, tumor growth rate, and cytokine effects. Parameter fitting will be conducted by minimizing the Sum of Squared Residuals (SSR) between model predictions and experimental data. Error in the parameters will be estimated through bootstrapping to find the best fit parameters with 95% confidence intervals. Preliminary analysis suggests that the model effectively captures tumor growth rates observed in the experimental data. Parameter estimation provides insights into the viral infection rate, cytokine-induced tumor suppression, and the timing of viral injections. These findings will help refine our understanding of how the SINVs and cytokine therapy interact in glioma treatment. This study provides a quantitative framework for evaluating the therapeutic effects of an oncolytic SINV combined with cytokines in glioma treatment. By providing parameter estimates for key biological processes, our model can help optimize treatment strategies and guide future experimental research in oncolytic virotherapy.

Time-varying production in virus dynamics models

Type: Undergraduate
Author(s): Page Matthews Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy
Location: Basement, Table 2, Position 2, 11:30-1:30

Mathematical modeling of viral kinetics can be used to gain further insight into the viral replication cycle and virus-host interactions. However, many virus dynamics models do not incorporate the cell-to-cell heterogeneity of virus yield or the time-dependent factor of virus production. A recent study of the kinetics of the vesicular stomatitis virus (VSV) in single BHK cells determined that both the virus production rate and the yield of virus particles vary widely between individual cells of the same cell population. We used the results of this study to determine the distribution that best describes the time course of viral production within single cells. The best distribution was then used to incorporate time-varying production into a standard model of viral kinetics. The best-fit model was determined by fitting potential distributions to cumulative viral production from single cells and comparing the Akaike Information Criterion (AIC). The results show that the best fit for most cells was log-normal. Time-dependent viral production was modeled with an integro-differential equation that incorporated the log-normal probability distribution into a standard constant production model of viral kinetics. This time-dependent model was compared to one of constant production by examining the differences between the viral peak, time of the peak, upslope, downslope, and area under the curve. These findings could have further-reaching implications for helping define the time course and nature of a particular virus infection within the human body as well as improving the dose-timing and efficacy of anti-viral treatments.

Structural and Practical Identifiability Analysis of Models for Syncytia Growth

Type: Undergraduate
Author(s): Gabriel McCarthy Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy
Location: Third Floor, Table 7, Position 1, 11:30-1:30

Syncytia are multinucleated cells that can occur due to virus infection of cells. Mathematical models in the form of ordinary differential equations can be used to simulate the growth of syncytia. Several novel ODE models can explain syncytia growth. Before employing these models on actual data, it is essential to analyze their structural (theoretical) and practical identifiability using computer software. Structural identifiability is an inherent property of each model and its parameters, referring to our ability to determine parameter values for the model given particular experimental measurements. Practical Identifiability analysis of a model is concerned with determining our ability to accurately determine parameter values given experimental error. Combining these two techniques enables us to determine whether or not the parameters of our syncytia models can be accurately determined. Obtaining accurate parameter values allows us to make conclusions about our data that can provide insight into the nature of the spread of syncytia. From this, we can plan experiments to parameterize the syncytia growth in the contexts of our models.

Applications of Mathematical Models of Virus to Mpox

Type: Undergraduate
Author(s): Gabriel McCarthy Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy
Location: Basement, Table 12, Position 1, 1:45-3:45

Mpox virus is a type of virus similar to smallpox that can cause diseases in humans. Several experiments have been done to collect data on how mpox evolves within an infected host. This data can be analyzed within the context of mathematical models to determine important characteristics of mpox. From this analysis, we can estimate the growth rate, reproduction number, and infecting time of mpox.  We can also construct confidence intervals to estimate the error in our predictions using bootstrapping.  Bootstrapping allows us to analyze parameter correlations within mpox data to understand how parameter values within the model affect each other in our model. From these values and confidence intervals, we can learn about how mpox evolves within the body over time. This information, in turn, may allow us to make predictions on how mpox evolves within people during infection that could inform future treatment regimens.