Spatio-Temporal stochastic models for phenotype and gene regulation by the human papillomavirus
Human papillomaviruses (HPVs) cause a range of serious diseases, with particular regard to cervical cancer, most anal cancers and half of head and neck cancers, and the need for new and effective antiviral therapies is of paramount importance. Important advancements regarding the HPV infection, throughout the infected epithelium, have been recently made. However, a full mechanistic understanding of how stochastic and dynamical properties of the HPV gene network interact with the cellular circuitry that controls proliferation/differentiation and cell-to-cell communication affects responses at the single cell and tissue level during infection is lacking. A better understanding of these aspects is critical to understand viral persistence, cancer progression, and to develop novel strategies for antiviral therapies. Mathematical models, developed under rigorous mathematical and biological assumptions, can be of great help in generating optimal solutions to these problems. STEPV project aims at improving the current available frameworks for stochastic tissue-level mathematical modeling, by tackling their limitations and specialize them in the context of HPVs, as well as to improve clinical/biological discoveries about HPVs infection. The specific goals are: (1) development of novel spatio-temporal modeling frameworks in order to describe HPVs gene expression and its connection with the phenotype control; (2) use of the developed models to understand the phenotype regulation by oncoproteins, understand viral persistence and propose novel antiviral strategies. By achieving these goals, STEPV will provide, for the first time, innovative modeling frameworks in the field of the computational systems biology applied to the context of HPVs infection, allowing quantitative and noninvasive tools to deeply investigate still elusive mechanisms, regarding HPVs infection, as well as investigate inaccessible or poorly understood clinical/biological scenarios.