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Systems virology models are advancing our understanding of virus-host interactions. Enhanced computing power and data enable complex models to explore viral infection across scales, but more research is needed.

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Area of Science:

  • Virology
  • Computational Biology
  • Data Science

Background:

  • Subcellular and multicellular models are crucial for studying virus-host interactions and susceptibility.
  • Advancements in computing power, model architectures, and host-derived datasets are driving progress.
  • Systems virology models abstract molecular and cellular networks.

Purpose of the Study:

  • To summarize recent advances in viral modeling and data science.
  • To illustrate how systems models have expanded in scale, detail, and biological context.
  • To highlight the potential of these models for understanding viral infections.

Main Methods:

  • Review of recent progress in viral modeling and data science.
  • Analysis of how systems models traverse different scales and levels of detail.
  • Integration of systems virology with public-health transmission models.

Main Results:

  • Systems models have successfully expanded across increasing time-length scales, levels of detail, and biological contexts.
  • Current progress, while encouraging, represents only a fraction of the potential given the diversity of viruses.
  • Systems virology complements established public-health models of viral transmission.

Conclusions:

  • Modern computational and data science approaches are enhancing our ability to model viral infections.
  • The scale of research efforts in systems virology should match the vast challenge posed by viral diversity.
  • Integrated modeling approaches offer powerful tools for both fundamental research and public health.