Spatiotemporal Dynamics of Virus Infection Spreading in Tissues
PLOS ONE
| dc.creator | Bocharov, Gennady | |
| dc.creator | Meyerhans, Andreas | |
| dc.creator | Bessonov, Nickolai | |
| dc.creator | Trofimchuk, Sergei | |
| dc.creator | Volpert, Vitaly | |
| dc.date | 2021-08-23T22:48:46Z | |
| dc.date | 2022-07-08T20:19:54Z | |
| dc.date | 2021-08-23T22:48:46Z | |
| dc.date | 2022-07-08T20:19:54Z | |
| dc.date | 2016 | |
| dc.date.accessioned | 2023-08-23T00:19:25Z | |
| dc.date.available | 2023-08-23T00:19:25Z | |
| dc.identifier | 1150480 | |
| dc.identifier | 1150480 | |
| dc.identifier | https://hdl.handle.net/10533/250211 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8354686 | |
| dc.description | Virus spreading in tissues is determined by virus transport, virus multiplication in host cells and the virus-induced immune response. Cytotoxic T cells remove infected cells with a rate determined by the infection level. The intensity of the immune response has a bell-shaped dependence on the concentration of virus, i.e., it increases at low and decays at high infection levels. A combination of these effects and a time delay in the immune response determine the development of virus infection in tissues like spleen or lymph nodes. The mathematical model described in this work consists of reaction-diffusion equations with a delay. It shows that the different regimes of infection spreading like the establishment of a low level infection, a high level infection or a transition between both are determined by the initial virus load and by the intensity of the immune response. The dynamics of the model solutions include simple and composed waves, and periodic and aperiodic oscillations. The results of analytical and numerical studies of the model provide a systematic basis for a quantitative understanding and interpretation of the determinants of the infection process in target organs and tissues from the image-derived data as well as of the spatiotemporal mechanisms of viral disease pathogenesis, and have direct implications for a biopsy-based medical testing of the chronic infection processes caused by viruses, e.g. HIV, HCV and HBV. | |
| dc.description | Regular 2015 | |
| dc.description | FONDECYT | |
| dc.description | FONDECYT | |
| dc.language | eng | |
| dc.relation | handle/10533/111557 | |
| dc.relation | handle/10533/111541 | |
| dc.relation | handle/10533/108045 | |
| dc.relation | https://doi.org/10.1371/journal.pone.0168576 | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.rights | info:eu-repo/semantics/article | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.title | Spatiotemporal Dynamics of Virus Infection Spreading in Tissues | |
| dc.title | PLOS ONE | |
| dc.type | Articulo | |
| dc.type | info:eu-repo/semantics/publishedVersion |