Biology subjects

Bernhauerova, V.

Publications and source records attributed to Bernhauerova, V..

3 recordsLinked to original sources

bioRxiv2025

Phenomenological model of transthyretin stabilization

Transthyretin is a tetrameric transport protein whose monomers, when destabilized, can misfold and form amyloid fibrils, leading to serious diseases like transthyretin amyloidosis cardiomyopathy and neuropathy. While kinetic stabilisers such as tafamidis or acoramidis are designed to prevent tetramer dissociation, clinical data show a puzzling increase in TTR levels after treatment - an effect our study seeks to investigate by exploring possible underlying mechanisms. Using a simple phenomenological model, we explore whether reduced dissociation alone accounts for this rise or if other mechanisms contribute. We propose that stabilisers may alter TTR clearance by slowing its cellular internalisation or degradation, or even by influencing its synthesis through pharmacological chaperoning. We also examine the role of monomer removal from circulation via re-association into tetramers or through other, possibly pathogenic processes. By integrating pharmacokinetic and pharmacodynamic data with experimental observations, our model provides fresh insights into TTR homeostasis and offers testable predictions for future research. This study highlights the power of simplified, hypothesis-driven models in uncovering biological mechanisms - or, at the very least, in identifying key questions that remain to be answered.

pathology↗

bioRxiv2019

Modeling cell infection via virus-producing cells rather than free infectious virus significantly improves fits of an in vitro viral kinetic data

Chikungunya and Zika viruses are arthropod-borne viruses that pose significant threat to public health. Experimental data show that during in vitro infection both viruses exhibit qualitatively distinct replication cycle kinetics. Chikungunya viral load rapidly accumulates within the first several hours post infection whereas Zika virus begins to increase at much later times. We sought to characterize these qualitatively distinct in vitro kinetics of chikungunya and Zika viruses by fitting a family of mathematical models to time course viral load datasets. We demonstrate that the standard viral kinetic model, which considers that new infections result only from free virus penetrating susceptible cells, does not fit experimental data as well as a model in which the number of virus-infected cells is the primary determinant of infection rate. We provide biologically meaningful quantifications of the main viral kinetic parameters and show that our results support cell-to-cell or localized transmission as a significant contributor to viral infection with chikungunya and Zika viruses.\n\nImportanceMathematical modeling has become a useful tool to tease out information about virus-host interactions and thus complements experimental work in characterizing and quantifying processes within viral replication cycle. Importantly, mathematical models can fill in incomplete data sets and identify key parameters of infection, provided the appropriate model is used. The in vitro time course dynamics of mosquito transmitted viruses, such as chikungunya and Zika, have not been studied by mathematical modeling and thus limits our knowledge about quantitative description of the individual determinants of viral replication cycle. This study employs dynamical modeling framework to show that the rate at which cells become virus-infected is proportional to the number or virus-infected cells rather than free extracellular virus in the milieu, a widely accepted assumption in models of viral infections. Using the refined mathematical model in combination with viral load data, we provide quantification of the main drivers of chikungunya and Zika in vitro kinetics. Together, our results bring quantitative understanding of the basic components of chikungunya and Zika virus dynamics.

microbiology↗

bioRxiv2018

Influenza Virus Infection Model With Density Dependence Supports Biphasic Viral Decay

Mathematical models that describe infection kinetics help elucidate the time scales, effectiveness, and mechanisms underlying viral growth and infection resolution. For influenza A virus (IAV) infections, the standard viral kinetic model has been used to investigate the effect of different IAV proteins, immune mechanisms, antiviral actions, and bacterial coinfection, among others. We sought to further define the kinetics of IAV infections by infecting mice with influenza A/PR8 and measuring viral loads with high frequency and precision over the course of infection. The data highlighted dynamics that were not previously noted, including viral titers that remain elevated for several days during mid-infection and a sharp 4-5 log10 decline in virus within one day as the infection resolves. The standard viral kinetic model, which has been widely used within the field, could not capture these dynamics. Thus, we developed a new model that could simultaneously quantify the different phases of viral growth and decay with high accuracy. The model suggests that the slow and fast phases of virus decay are due to the infected cell clearance rate changing as the density of infected cells changes. To characterize this model, we fit the model to the viral load data, examined the parameter behavior, and connected the results and parameters to linear regression estimates. The resulting parameters and model dynamics revealed that the rate of viral clearance during resolution occurs 25 times faster than the clearance during mid-infection and that small decreases to this rate can significantly prolong the infection. This likely reflects the high efficiency of the adaptive immune response. The new model provides a well-characterized representation of IAV infection dynamics, is useful for analyzing and interpreting viral load dynamics in the absence of immunological data, and gives further insight into the regulation of viral control.

systems biology↗