Modeling viral spread

• Verfasst von: Graw, Frederik [VerfasserIn]
• Verfasst von: Perelson, Alan S. [VerfasserIn]
• Titel: Modeling viral spread
• Verf.angabe: Frederik Graw and Alan S. Perelson
• E-Jahr: 2016
• Jahr: August 31, 2016
• Umfang: 18 S.
• Fussnoten: Gesehen am 31.07.2020
• Titel Quelle: Enthalten in: Annual review of virology
• Ort Quelle: Palo Alto, Calif. : Annual Reviews, 2014
• Jahr Quelle: 2016
• Band/Heft Quelle: 3(2016), 1, Seite 555-572
• ISSN Quelle: 2327-0578
• DOI: doi:10.1146/annurev-virology-110615-042249
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: agent-based models
• Sach-SW: cell-free virus infection
• Sach-SW: cell-to-cell infection
• Sach-SW: HCV
• Sach-SW: Hepacivirus
• Sach-SW: Hepatitis C
• Sach-SW: HIV
• Sach-SW: HIV Infections
• Sach-SW: HIV-1
• Sach-SW: Humans
• Sach-SW: mathematical modeling
• Sach-SW: Models, Biological
• Sach-SW: Virus Internalization
• K10plus-PPN: 172601133X

Abstract

The way in which a viral infection spreads within a host is a complex process that is not well understood. Different viruses, such as human immunodeficiency virus type 1 and hepatitis C virus, have evolved different strategies, including direct cell-to-cell transmission and cell-free transmission, to spread within a host. To what extent these two modes of transmission are exploited in vivo is still unknown. Mathematical modeling has been an essential tool to get a better systematic and quantitative understanding of viral processes that are difficult to discern through strictly experimental approaches. In this review, we discuss recent attempts that combine experimental data and mathematical modeling in order to determine and quantify viral transmission modes. We also discuss the current challenges for a systems-level understanding of viral spread, and we highlight the promises and challenges that novel experimental techniques and data will bring to the field.