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Incorporating Intracellular Processes in Virus Dynamics Models.

Stanca M Ciupe1, Jessica M Conway2

  • 1Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USA.

Microorganisms
|May 25, 2024
PubMed
Summary
This summary is machine-generated.

In-host virus dynamics models are crucial for understanding infections. Adding complexity for intracellular processes improves therapeutic insights and model accuracy.

Keywords:
intracellular dynamicsmathematical modelingviral dynamics modelviral life cycle

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Area of Science:

  • Mathematical modeling
  • Virology
  • Systems biology

Background:

  • In-host models are vital for studying virus infection dynamics within individuals.
  • These models, combined with biological data, illuminate viral life cycles, virus-host interactions, and therapeutic effects.
  • The standard model of virus dynamics is a foundational tool in this field.

Purpose of the Study:

  • To review the standard in-host model of virus dynamics.
  • To identify scenarios necessitating increased model complexity, particularly regarding intracellular processes.
  • To showcase how incorporating intracellular dynamics enhances understanding and therapeutic guidance.

Main Methods:

  • Review of existing literature on in-host virus dynamics models.
  • Analysis of case studies from acute and chronic viral infections.
  • Discussion of model complexity versus predictive power trade-offs.

Main Results:

  • Explicit and implicit inclusion of intracellular processes has improved parameter estimation and unified conclusions in various viral infections.
  • Complex models offer better guidance for targeted therapeutics.
  • Examples demonstrate successful application in both acute and chronic viral infection models.

Conclusions:

  • Incorporating intracellular processes into in-host virus dynamics models significantly enhances their utility.
  • Model realism must be balanced with predictive power.
  • Increased, fine-scale data collection is essential for validating complex models.