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by claude@2026-06, 2026-06-24
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This preprint reviews how bacteriophage ecology has been modeled using long-standing mathematical frameworks, including resource-based trophic, epidemiological, stochastic, metacommunity, and network-based models, and discusses ecological theories such as Kill-the-Winner and Piggyback-the-Winner. It argues that theoretical insights have historically been only loosely connected to environmental observations and that many studies have remained descriptive, while genomic and longitudinal metagenomic datasets now enable data-driven inference (e.g., co-occurrence/correlation, network inference, dynamic modeling, and machine learning) to infer phage–host interactions and viral lifestyles. A stated caveat is that the work is a preprint and not peer reviewed, and it functions as a synthesis rather than presenting new empirical results. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Viruses, particularly bacteriophages, are the most abundant biological entities across nearly all ecosystems and play a central role in shaping microbial community structure, ecosystem function, and evolution. Consequently, there has been growing interest in studying phages and their interactions within microbiomes. Mathematical modeling has long provided a foundation for investigating phage–host interactions, formalizing diverse infection strategies (lytic, lysogenic and chronic infection) and their governing parameters. These approaches include resource-based trophic models that describe the flow of resources from microbes to viruses, epidemiological models that focus on the transmission of infection, stochastic models that capture environmental and demographic randomness, metacommunity models that account for spatial organization, and network-based models that characterize the topology of phage–host interactions. Together, these frameworks have given rise to influential ecological theories, such as Kill-the-Winner and Piggyback-the-Winner, which offer mechanistic explanations for phage–host dynamics. Despite these advances, theoretical insights have remained only loosely connected to environmental observations, and studies of phage communities have often been limited to descriptive characterization. Metagenomics now provides a unique opportunity to directly observe phage–host dynamics across diverse environments and timescales. The increasing availability of large-scale and longitudinal metagenomic datasets enables the application of statistical and data-driven approaches, including co-occurrence and correlation analyses, network inference, dynamic modeling, and machine learning, to infer interactions and ecological strategies from empirical data. In this review, we synthesize the foundations of theoretical and mathematical modeling of phage–host systems and discuss emerging approaches for integrating omics data with these frameworks. By linking metagenomic observations with mechanistic and ecological models, we highlight pathways toward moving beyond descriptive viromics and toward predictive, data-informed understanding of phage ecology and evolution in natural ecosystems.
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This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
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This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.
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Viruses, particularly bacteriophages, are the most abundant biological entities across nearly all ecosystems and play a central role in shaping microbial community structure, ecosystem function, and evolution. Consequently, there has been growing interest in studying phages and their interactions within microbiomes. Mathematical modeling has long provided a foundation for investigating phage–host interactions, formalizing diverse infection strategies (lytic, lysogenic and chronic infection) and their governing parameters. These approaches include resource-based trophic models that describe the flow of resources from microbes to viruses, epidemiological models that focus on the transmission of infection, stochastic models that capture environmental and demographic randomness, metacommunity models that account for spatial organization, and network-based models that characterize the topology of phage–host interactions. Together, these frameworks have given rise to influential ecological theories, such as Kill-the-Winner and Piggyback-the-Winner, which offer mechanistic explanations for phage–host dynamics. Despite these advances, theoretical insights have remained only loosely connected to environmental observations, and studies of phage communities have often been limited to descriptive characterization. Metagenomics now provides a unique opportunity to directly observe phage–host dynamics across diverse environments and timescales. The increasing availability of large-scale and longitudinal metagenomic datasets enables the application of statistical and data-driven approaches, including co-occurrence and correlation analyses, network inference, dynamic modeling, and machine learning, to infer interactions and ecological strategies from empirical data. In this review, we synthesize the foundations of theoretical and mathematical modeling of phage–host systems and discuss emerging approaches for integrating omics data with these frameworks. By linking metagenomic observations with mechanistic and ecological models, we highlight pathways toward moving beyond descriptive viromics and toward predictive, data-informed understanding of phage ecology and evolution in natural ecosystems.
https://doi.org/10.32942/X2P07G
Life Sciences
Phage ecology, mathematical models, metagenomics, time-series metagenomics, data-driven, host-virus interaction, viral lifestyle, environmental viruses, ecological modeling
Published: 2026-02-26 20:24
Last Updated: 2026-02-26 20:24
CC-BY Attribution-NonCommercial 4.0 International
Language:
English
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