Towards Systems Biology of Adenovirus Transmission
Infectious diseases due to viruses are a major health problem across the world. We still lack effective anti-viral treatments. Viral disease requires viral propagation, and involves entry, replication, assembly and spreading of progeny from the infected cells to neighboring cells and individuals. Viruses spread between cells, tissues and organisms by cell-free and cell-cell transmissions. Both mechanisms enhance disease, but it is difficult to distinguish between them. We have recently shown that for human adenoviruses, which cause respiratory disease or epidemic keratoconjunctivitis, the cell-free model of transmission is preferred in cultured cells, and requires lytic infection. In addition, adenovirus induced cell lysis is being actively exploited as an anti-cancer strategy in clinical oncology.
We build this one-year project upon results from our wet lab experiments, and a recently developed multi-scale computational model for virus spread in
2-dimensional cell cultures. This model accurately describes the spreading behavior of adenovirus in cultured cells. The basis of the model are experimentally determined parameters, such as the diffusion constant of adenovirus in the extracellular medium, the probability of infection depending on local virus concentration, the probability and the average time for an infected cell to lyse, and the probability of cell death for uninfected cells.
In the project here, we will use characterized small compounds to identify host factors, which affect the spreading of infection, in particular the lysis of infected cells. We will complement the data acquisition with cutting edge spatial simulation algorithms, and extend the analyses towards a computer-aided cell perturbation study. We expect that results from our studies will provide valuable novel insights for the field of oncolytic viruses, suggest targets for combinatorial oncolytic strategies and fertilize other fields of life sciences that use imaging and simulation.
Keywords: Multi-scale Model, Computational Simulation, Virus Infection Spreading, Cell Lysisback