Modeling the dynamics of within-host viral infection and evolution predicts quasispecies distributions and phase boundaries separating distinct classes of infections

Abstract

We use computational modeling to study within-host viral infection and evolution. In our model, viruses exhibit variable binding to cells, with better infection and replication countered by a stronger immune response and a high rate of mutation. By varying host conditions (permissivity to viral entry T and immune clearance intensity A) for large numbers of cells and viruses, we study the dynamics of how viral populations evolve from initial infection to steady state and obtain a phase diagram of the range of cell and viral responses. We find three distinct replicative strategies corresponding to three physiological classes of viral infections: acute, chronic, and opportunistic. We show similarities between our findings and the behavior of real viral infections such as common flu, hepatitis, and SARS-CoV-2019. The phases associated with the three strategies are separated by a phase transition of primarily first order, in addition to a crossover region. Our simulations also reveal a wide range of physical phenomena, including metastable states, periodicity, and glassy dynamics. Lastly, our results suggest that the resolution of acute viral disease in patients whose immunity cannot be boosted can only be achieved by significant inhibition of viral infection and replication.

Comparative efficacy of antiviral strategies targeting different stages of the viral life cycle: A viral quasispecies dynamics study

“…In this work, we use this framework to systematically study the impact of different anti-viral therapeutic strategies that target different aspects of the life-cycle of respiratory viruses such as influenza, cold viruses and particularly SARS-CoV-2. We build on previous studies [3], [4] that model the viral life cycle as three main discrete stages of infection, immune clearance and reproduction, and investigate the aftermath of administering antivirals at a specific time after first infection, that selectively reduce the infection rate, the reproduction rate, or the fecundity of the virus, where we define fecundity as the maximum number of new viruses that can be produced from each progenitor The cell occupation probability and the virus population are calculated during 1) Infection, 2) Immune Clearance and 3) Reproduction. We modeled the actions of the therapeutics as effects on the infection rate, fecundity and reproduction rate.…”

“…(a) Virus population in the bloodstream and (b) virus population inside cells at steady state as a function of the change in fecundity, f 0 − f following the application of the antiviral. (c) Heat map showing distinct regions for acute, chronic and opportunistic phases as described in [4]. The four rectangular boxes in red, black, blue and cyan show the four cases studied in this paper, which focus only on the acute phase (yellow in (b)).…”

“…The full phase space of the model discussed in this work has three main phases of disease: acute, opportunistic, and chronic [4]. Here we focus on the acute phase representing respiratory diseases including flu, SARS, and in particular COVID-like diseases.…”