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

Greyson R. Lewis
Wallace F. Marshall
Barbara A. Jones

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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.

Author summary

Virus, in particular RNA viruses, often produce offspring with slightly altered genetic composition. This process occurs both across host populations and within a single host over time. Here, we study the interactions of viruses with cells inside a host over time. In our model, the viruses encounter host cell defenses characterized by two parameters: permissivity to viral entry T and immune response A ). The viruses then mutate upon reproduction, eventually resulting in a distribution of related viral types termed a quasi-species distribution. Across varying host conditions ( T, A ), three distinct viral quasi-species types emerge over time, corresponding to three classes of viral infections: acute, chronic and opportunistic. We interpret these results in terms of real viral types such as common flu, hepatitis, and also SARS-CoV-2019. Analysis of viral of viral mutant populations over a wide range of permissivity and immunity, for large numbers of cells and viruses, reveals phase transitions that separate the three classes of viruses, both in the infection-cycle dynamics and at steady state. We believe that such a multiscale approach for the study of within-host viral infections, spanning individual proteins to collections of cells, can provide insight into developing more effective therapies for viral disease.

SciScore for 10.1101/2021.12.16.473030: (What is this?)

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Table 2: Resources

Software and Algorithms
Sentences	Resources
We have implemented simulations in Python 3 using Numpy accelerated with Numba package [29, 30].	Python suggested: (IPython, RRID:SCR_001658) Numpy suggested: (NumPy, RRID:SCR_008633) Numba suggested: None

Results from OddPub: Thank you for sharing your code.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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