IMMU Subgroup Contributed Talks

Adquate Mhlanga

Loyola University Chicago

"Mathematical modeling of hepatitis D virus and hepatitis B virus interplay during anti-HDV treatment"

Hepatitis D virus (HDV) and hepatitis B virus (HBV) coinfection is the most severe form of chronic viral hepatitis. HDV is considered a satellite virus because it relies on hepatitis B surface antigen (HBsAg) to propagate. Treatment against HDV chronic infection with pegylated interferon-α2a (pegIFN) therapy is suboptimal and affects both HDV and HBV. The investigational drug called lonafarnib (LNF) targets HDV only, providing a unique opportunity to study the interplay between HDV and HBV. We recently developed a mathematical model to study the interplay between HDV and HBV in chronic HDV/HBV patients [1]. I will present our efforts to characterize the frequent kinetic data of HDV, HBV, HBsAg, and LNF pharmacokinetics obtained from 15 patients who were treated with LNF, LNF+ritonavir, or LNF+pegIFN [2].In addition, I will present our modeling efforts in extending our model [1] to account also for HBsAg kinetics and to estimate HDV/HBV kinetic parameters and LNF±pegIFN efficacies using both individual and population fitting approaches.

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Caroline I. Larkin

University of Pittsburgh

"Rule-based modeling of Eastern Equine Encephalitis Virus replication dynamics"

Eastern Equine Encephalitis Virus (EEEV) is an arthropod-borne, single-stranded positive-sense RNA virus that poses a significant threat to public health and national security. Compared to similar viruses such as SARS-CoV-2 or Hepatitis C virus, EEEV causes severe encephalitis when neuroinvasive leading to a human mortality rate of ~30-70%. Moreover, there are no preventative or standardized therapies, leaving patients to rely solely on supportive care. In addition, studies have shown that EEEV is easily aerosolized making it an ideal biowarfare agent. Although the molecular components and interactions of infection, replication, and amplification of EEEV within the host cell are well-studied, how these mechanisms integrate to determine the dynamics of RNA viral replication and host immune responses remains unclear, limiting our ability to advance therapeutic development. Computational models provide a powerful tool for probing both quantitative and qualitative effects arising from the modulation of viral infections. Here, we present a systems modeling approach to elucidate the mechanisms regulating the precise dynamics of EEEV replication through the development of a mechanistic mathematical model. Specifically, this model describes attachment, entry, uncoating, replication, assembly, and export of both infectious virions and virus-like particles within mammalian cells. The model recapitulates known characteristics of EEEV infection, including the timing and amplitude of virion production, and identifies genome replication as the significant rate-limiting step during infection. Additionally, this model highlights the possibility, which will be tested experimentally, that a mismatch between the production of viral RNA and viral proteins could result in the inability to produce infectious virions 12 hours post-infection. We are currently working to expand the model to incorporate type I interferon induction within an infected host cell. This will provide a comprehensive perspective on the conditions required for maximizing host response efficacy and determine the key steps of immune system activation required for successful suppression of viral infection.
Additional authors: Jason E. Shoemaker, Departments of Computational and Systems Biology, Chemical and Petroleum Engineering, University of Pittsburgh; William B. Klimstra, Department of Immunology and The Center for Vaccine Research, University of Pittsburgh; James R. Faeder, Department of Computational and Systems Biology, University of Pittsburgh

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Hayashi Rena

Kyushu University

"Establishment chance of a mutant strain decreases over time after infection with the original strain."

After infecting a host, a viral strain may increase rapidly within the body and produce mutants with a faster proliferation rate than the virus itself. However, most of the mutants become extinct because of the stochasticity caused by the small number of infected cells. In addition, the mean growth rate of a mutant strain decreases with time because the number of susceptible target cells is reduced by the wild-type strain. In this study, we calculated the fraction of mutants that can escape stochastic extinction, based on a continuous-time branching process with a time-dependent growth rate. We analyzed two cases differing in the mode of viral transmission: (1) an infected cell transmits the virus through cell-to-cell contact with a susceptible target cell; (2) an infected cell releases numerous free viral particles that subsequently infect susceptible target cells. The chance for a mutant strain to be established decreases with time after infection of the wild-type strain, and it may oscillate before convergence at the stationary value. We then calculated the probability of escaping stochastic extinction for a drug-resistant mutant when a patient received an antiviral drug that suppressed the original strain. Combining the rate of mutant production from the original strain and the chance of escaping stochastic extinction, the number of emerging drug-resistant mutations may have two peaks: one soon after the infection of the original type and the second at the start of antiviral drug administration. Hayashi R, Iwami S, and *Iwasa Y. 2022. Escaping stochastic extinction of mutant virus: temporal pattern of emergence of drug resistance within a host. Journal of Theoretical Biology 537, 111029. Hayashi R., and Iwasa, Y. Temporal pattern of the emergence of a mutant virus escaping cross-immunity and stochastic extinction within a host. (in review)

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Quintessa Hay

Virginia Commonwealth University

"A Mathematical Model for Wound Healing in Reef-Building Coral Pocillopora damicornis"

Coral reefs, among the most diverse ecosystems in the ocean, currently face major threats due to multiple stressors such as pollution, unsustainable fishing practices, and perturbations in environmental parameters brought on by climate change. Reefs also sustain regular wounding from other sea life and human activity. Recent reef preservation practices have even involved intentional wounding by systematically breaking coral fragments and relocating them to revitalize damaged reefs. Despite its importance, very little research has explored the inner mechanisms of wound healing in corals. Some reef-building corals have been observed to initiate an immunological response similar to those observed in humans and other vertebrates. Utilizing past models of inflammation and early proliferation and remodeling, we formulated a mechanistic model for wound healing in corals. The model consists of four differential equations mediating wound debris, inflammation, proliferation, and wound closure. The model is coupled with experimental data for linear and circular shaped wounds on Pocillopora damicornis fragments. A preliminary parameter set was obtained by fitting to the wound closure times obtained empirically and to expected temporal trends observed in other coral species and in humans and other vertebrates. A variety of mathematical methods were applied for model analysis including local sensitivity analysis. Results were used to define an identifiable set of parameters. The parameter space was also explored to exhibit the diverse model outcomes and their biological implications. Keywords: stony corals, inflammation, differential equations, parameter estimation, sensitivity analysis
Additional authors: Quintessa Hay1, Luke Gardner1, Eunice Pak3, Liza M. Roger2,4,5, Rebecca A. Segal1, Anna Shaw1, Nastassja A. Lewinski2, and Angela M. Reynolds1 1 Department of Mathematics & Applied Mathematics, Virginia Commonwealth University, Richmond, VA, USA. 2 Department of Chemical & Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA. 3 Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, USA. 4 School of Molecular Sciences, Arizona State University, Tempe, AZ, USA. 5 School of Ocean Futures, Arizona State University, Tempe, AZ, USA.

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