Tuesday, June 15 at 10:30pm (PDT)Wednesday, June 16 at 06:30am (BST)Wednesday, June 16 02:30pm (KST)
SMB2021 FollowTuesday (Wednesday) during the "CT05" time block.
The University of Melbourne
"Modelling the effect of MUC1 on influenza virus infection kinetics and macrophage dynamics"
The host immune response is important to defend against influenza viral infection. However, overstimulation of the host immune response can lead to pathology, indicating a subtle balance between a protective and a destruct response. Dysregulated immune responses are often associated with an excessive recruitment of macrophages. MUC1 belongs to the family of cell surface (cs-) mucins and has been shown to be an important and dynamic component of the host innate immune response, associated with recruitment of macrophages. Experimental evidence indicates that its presence reduces influenza infection severity. However, the detailed effects of MUC1 in vivo remain elusive, limiting our ability to predict the efficacy of potential treatments that target MUC1. To address this limitation, we fit two mathematical models to available in vivo kinetic data for both virus and macrophage populations in wild-type and MUC1 knockout mice. Both models provide evidence that MUC1 reduces the susceptibility of epithelial cells and show that the MUC1 regulates the recruitment of macrophages and thus the host immune response. This study improves our understanding of the dynamic role of MUC1 against influenza infection and may support the development of novel antiviral treatments.
Juan Antonio Magalang
Theoretical Physics Group, National Institute of Physics, University of the Philippines
"Stochastic resetting antiviral therapies prevent drug resistance development"
We study minimal mean-field models of viral drug resistance development in which the efficacy of a therapy is described by a one-dimensional stochastic resetting process with mixed reflecting-absorbing boundary conditions. We derive analytical expressions for the mean survival time for the virus to develop complete resistance to the drug. We show that the optimal therapy resetting rates that achieve a minimum and maximum mean survival times undergo a second- and first-order phase transition-like behaviour as a function of the therapy efficacy drift. We illustrate our results with simulations of a population dynamics model of HIV-1 infection.