Systems Virology at Scale

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.