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Synopsis Viral evolution unfolds across nested layers of adaptation, much like a set of Matryoshka dolls. The outermost, well-studied layer involves interactions between viruses and their hosts—where immune evasion, cross-species transmission, and long-term coevolution drive viral diversification. Yet, hidden within this framework is an often-overlooked inner layer: the coevolution of viruses with their own molecular parasites, defective interfering (DI) particles, and defective viral genomes (DVGs). These molecular parasites exploit viral replication machinery, reshaping infection dynamics and imposing selective pressures that influence viral fitness, transmission, and persistence. This perspective synthesizes evidence from experimental evolution, mathematical modeling, and molecular virology to propose a more integrated view of viral evolution. By framing host–virus interactions and virus-DI particle dynamics within a unified evolutionary framework, we highlight the underappreciated role of DI particles as evolutionary players, not just aberrant byproducts. Recognizing these internal layers of viral evolution may inform the development of antiviral strategies and broader questions in host–pathogen coevolution.
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Incorporating Intracellular Processes in Virus Dynamics Models
In-host models have been essential for understanding the dynamics of virus infection inside an infected individual. When used together with biological data, they provide insight into viral life cycle, intracellular and cellular virus–host interactions, and the role, efficacy, and mode of action of therapeutics. In this review, we present the standard model of virus dynamics and highlight situations where added model complexity accounting for intracellular processes is needed. We present several examples from acute and chronic viral infections where such inclusion in explicit and implicit manner has led to improvement in parameter estimates, unification of conclusions, guidance for targeted therapeutics, and crossover among model systems. We also discuss trade-offs between model realism and predictive power and highlight the need of increased data collection at finer scale of resolution to better validate complex models.
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- Award ID(s):
- 2051820
- PAR ID:
- 10534283
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Microorganisms
- Volume:
- 12
- Issue:
- 5
- ISSN:
- 2076-2607
- Page Range / eLocation ID:
- 900
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/microorganisms12050900
