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Size and Structure of Viral Genomes

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Basic stochastic models for viral infection within a host.

Sukhitha W Vidurupola1, Linda J S Allen

  • 1Texas Tech University, Department of Mathematics and Statistics, Lubbock, Texas 79409-1042, United States. sukhitha.vidurupola@ttu.edu

Mathematical Biosciences and Engineering : MBE
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This summary is machine-generated.

Stochastic differential equation (SDE) models reveal greater variability in early viral infection dynamics, especially at low virus concentrations. These models offer a more realistic view of viral entry and release compared to ordinary differential equation (ODE) models.

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Area of Science:

  • Virology
  • Mathematical Biology
  • Infectious Disease Modeling

Background:

  • Early viral infection dynamics are crucial for understanding disease progression.
  • Ordinary differential equation (ODE) models simplify complex biological processes.
  • Stochastic differential equation (SDE) models can incorporate more biological realism.

Purpose of the Study:

  • To develop and analyze stochastic differential equation (SDE) models for intra-host virus-cell dynamics.
  • To compare SDE models with traditional ordinary differential equation (ODE) models.
  • To investigate the impact of viral concentration and release mechanisms on infection dynamics.

Main Methods:

  • Formulation of SDE models for virus-cell interactions.
  • Analysis of model behavior concerning viral concentration.
  • Comparison of SDE model predictions with ODE model predictions.
  • Investigation of viral peak timing based on release mechanisms (bursting vs. budding).

Main Results:

  • SDE models exhibit greater variability than ODE models, particularly at low viral concentrations.
  • Variability in SDE models is concentration-dependent, with higher variability at lower concentrations.
  • Significant variability in the timing of the viral peak was observed in SDE models.
  • Viral peaks occur earlier when viruses are released via bursting compared to budding.

Conclusions:

  • SDE models provide a more realistic representation of early viral infection dynamics.
  • Stochasticity plays a significant role in viral load fluctuations and peak timing.
  • The mechanism of viral release (bursting vs. budding) influences the speed of infection progression.