A Spatial Whole-Cell Model for Hepatitis B Viral Infection and Drug Interactions

Despite a vaccine, hepatitis B virus (HBV) remains a world-wide source of infections and deaths, and tackling the infection requires a multimodal approach against the virus. We develop a whole-cell computational platform combining spatial and kinetic models for the infection cycle of a virus host cell (hepatocyte) by HBV. We simulate a near complete viral infection cycle with this whole-cell platform stochastically for 10 minutes of biological time, to predict viral infection, map out virus-host as well as virus-drug interactions. We find that with an established infection, decreasing the copy number of the viral envelope proteins can shift the dominant infection pathways from secreting the capsids from the cell to re-importing the capsids back to the nucleus, resulting in higher viral DNA referred to as covalently closed circular DNA (cccDNA) copy number. This scenario can mimic the consequence of drugs designed to manipulate viral gene expression (such as siRNAs). Viral capsid mutants lead to their destabilization such that they disassemble at nuclear pore complexes, result in an increase in cccDNA copy number. However, excessive destabilization leading to cytoplasmic disassembly does not increase the cccDNA copy number. Finally, our simulations can predict the best drug dosage and timing of its administration to reduce the cccDNA copy number which is the hallmark of infection. Our adaptable computational platform can be utilized to study other viruses, more complex host-virus interactions, and identify the most central viral pathways that can be targeted by drugs or a combination of them.