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Abstract Purpose of ReviewThis review explores the application of classical ecological theory to host-associated microbiomes during initial colonization, maintenance, and recovery. We discuss unique challenges of applying these theories to host-associated microbiomes and host factors to consider going forward. Recent FindingsRecent studies applying community ecology principles to host microbiomes continue to demonstrate a role for both selective and stochastic processes in shaping host-associated microbiomes. However, ecological frameworks developed to describe dynamics during homeostasis do not necessarily apply during diseased or highly perturbed states, where large variations can potentially lead to alternate stable states. SummaryDespite providing valuable insights, the application of ecological theories to host-associated microbiomes has some unique challenges. The integration of host-specific factors, such as genotype or immune dynamics in ecological models or frameworks is crucial for understanding host microbiome assembly and stability, which could improve our ability to predict microbiome outcomes and improve host health.
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Understanding ecological systems using knowledge graphs: an application to highly pathogenic avian influenza
Abstract MotivationEcological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of interactions underpinning system dynamics. Knowledge graphs (KGs) have been successfully applied to organize heterogeneous data and to predict new linkages in complex systems. Though not previously applied broadly in ecology, KGs have much to offer in an era when system dynamics are responding to rapid changes across multiple scales. ResultsWe developed a KG to demonstrate the method’s utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include data related to HPAI including pathogen–host associations, species distributions, and population demographics, using a semantic ontology that defines relationships within and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying patterns of HPAI ecology. We underscore the generalizable value of KGs to ecology including ability to reveal previously known relationships and testable hypotheses in support of a deeper mechanistic understanding of ecological systems. Availability and implementationThe data and code are available under the MIT License on GitHub at https://github.com/cghss-data-lab/uga-pipp.
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- Award ID(s):
- 2200158
- PAR ID:
- 10575243
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics Advances
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2635-0041
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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