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Network Analysis: A Systems Framework to Address Grand Challenges in Plant Pathology
K A Garrett1,2,3, R I Alcalá-Briseño1,2,3, K F Andersen1,2,3
1Plant Pathology Department, University of Florida, Gainesville, Florida 32611, USA;
Annual Review of Phytopathology
|July 7, 2018
Summary
Network analysis offers powerful tools for understanding complex plant disease systems. Integrating network approaches can optimize disease management and support sustainable agriculture.
Area of Science:
- Plant Pathology
- Systems Biology
- Agricultural Science
Background:
- Plant pathology faces complex challenges requiring advanced analytical tools.
- Network analysis provides a framework for synthesizing information within plant pathology and related fields.
- Existing methods often lack the capacity to address the intricate interactions within plant disease systems.
Purpose of the Study:
- To explore the application of network analysis in plant pathology.
- To demonstrate how network analysis can synthesize complex plant-pathogen interactions.
- To identify research priorities for network analysis in optimizing plant disease management.
Main Methods:
- Discussion of network analysis applications in plant pathology.
- Review of microbiome and phytobiome network analysis.
- Integration of epidemic and social network analysis.
- Consideration of statistical tools like Bayesian network analysis and exponential random graph models.
Main Results:
- Network analysis offers a synthetic approach to complex plant pathology challenges.
- Microbiome and phytobiome network analysis can identify biocontrol strategies and predict disease emergence.
- Linking epidemic and social network analysis aids sustainable agriculture and scalable disease management solutions.
- Underutilized statistical network tools show promise for informing plant pathology strategies.
Conclusions:
- Network analysis is crucial for addressing complexity in plant pathology.
- Integrating diverse network analyses can optimize plant disease management systems.
- Further research into statistical network models is recommended for advancing the field.
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