Biology subjects

Ciupe, S. M.

Publications and source records attributed to Ciupe, S. M..

3 recordsLinked to original sources

bioRxiv2025

From Bird Viremia to Bird Surveillance: Identifiability in a Multiscale Vector-Borne Model of Usutu Virus Infection

Usutu virus is an emerging mosquito-borne flavivirus, maintained through an enzootic cycle involving wild birds and mosquitoes, with occasional spillover to humans. Understanding how interactions across these biological scales shape transmission dynamics is essential for predicting outbreaks and improving surveillance strategies. In this study, we developed a multiscale vector-borne model of Usutu virus infection that links within-host viral kinetics in birds, the per-bite probability of mosquito infection, and population-level mosquito-bird transmission dynamics. Model parameters were validated using two laboratory datasets collected under an optimally designed experimental framework and one surveillance dataset from wild bird populations. Structural and practical identifiability analyses were conducted to evaluate parameter robustness under varying levels of measurement noise. We found that simultaneous multiscale fitting to integrated datasets improved parameter identifiability and robustness. These results highlight the importance of combining microscale and macroscale data to enhance the predictive reliability of vector-borne disease models and demonstrate the broader utility of multiscale modeling frameworks for understanding the transmission dynamics of emerging arboviruses. Author summaryIn this study, we developed a multiscale vector-borne model of Usutu virus infection and validated its parameters using both laboratory data collected under an optimally designed experimental framework and published surveillance data from wild bird populations. Using this model, we quantified the robustness of parameter estimates and found that multiscale fitting to integrated datasets improves the reliability and identifiability of model parameters. The results highlight the importance of combining microscale and macroscale data to enhance the predictive reliability of vector-borne disease models.

immunology↗

bioRxiv2024

Identifiability investigation of within-host models of acute virus infection

Uncertainty in parameter estimates from fitting within-host models to empirical data limits the models 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. Author summaryWithin-host models of virus infections fitted to data have improved our understanding of mechanisms of infection, disease progression, and allowed us to provide guidelines for pharmaceutical interventions. Given their predictive power, it is essential that we properly uncover and address uncertainty in model predictions and parameter estimation. Here, we focus on the effect of model structure and data availability on our ability to uncover unknown parameters. To address these questions, 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 analysis. 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 insights into the sources of uncertainty in parameter estimations and provide guidelines for the types of model assumptions, optimal experimental design, and biological information needed for improved predictions.

immunology↗

bioRxiv2020

Understanding the antiviral effects of RNAi-based therapy on chronic hepatitis B infection

Reaching hepatitis B surface antigen (HBsAg) loss (called functional cure) with approved treatment with pegylated interferon- (IFN) and/or nucleos(t)ide analogues (NAs) in chronic hepatitis B virus (HBV) infected patients is suboptimal. The RNA interference (RNAi) drug ARC-520 was shown to be effective in reducing serum HBV DNA, HBsAg and hepatitis B e antigen (HBeAg) in chimpanzees and small animals. A recent clinical study (Heparc-2001) showed reduction of serum HBV DNA, HBeAg and HBsAg in HBeAg-positive patients treated with a single dose of ARC-520 and daily NA (entecavir). To provide insights into HBV dynamics under ARC-520 treatment and its efficacy in blocking HBV DNA, HBsAg, and HBeAg production we developed a a multi-compartmental pharmacokinetic-pharamacodynamic model and calibrated it with measured HBV data. We showed that the time-dependent ARC-520 efficacies in blocking HBsAg and HBeAg are more than 96% effective around day 1, and slowly wane to 50% in 1-4 months. The combined ARC-520 and entecavir effect on HBV DNA is constant over time, with efficacy of more than 99.8%. HBV DNA loss is entecavir mediated and the strong but transient HBsAg and HBeAg decays are solely ARC-520 mediated. We added complexity to the model in order to reproduce current long-term therapy outcomes with NAs by considering the tradeoff between hepatocyte loss and hepatocyte division, and used it to make in-silico long-term predictions for virus, HBsAg and HBeAg titer dynamics. These results may help assess ongoing RNAi drug development for hepatitis B virus infection. Author summaryWith about 300 million persons infected worldwide and 800,000 deaths annually, chronic infection with hepatitis B virus (HBV) is a major public health burden with high endemic areas around the world. Current treatment options focus on removing circulating HBV DNA but are suboptimal in removing hepatitis B s- and e-antigens. ARC-520, a RNA interference drug, had induced substantial hepatitis B s- and e- antigen reductions in animals and patients receiving therapy. We study the effect of ARC-520 on hepatitis B s- and e-antigen decline by developing mathematical models for the dynamics of intracellular and serum viral replication, and compare it to patient HBV DNA, hepatitis B s- and e-antigen data from a clinical trial with one ARC-520 injection and daily nucleoside analogue therapy. We examine biological parameters describing the different phases of HBV DNA, s-antigen and e-antigen decline and rebound after treatment initiation, and estimate treatment effectiveness. Such approach can inform the RNA interference drug therapy.

immunology↗