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European Journal of Plant Pathology

, Volume 133, Issue 1, pp 75–88 | Cite as

Information networks for disease: commonalities in human management networks and within-host signalling networks

  • K. A. Garrett
Article

Abstract

Network models of human epidemics can often be improved by including the effects of behaviour modification in response to information about the approach of epidemics. Similarly, there are opportunities to incorporate the flow of information and its effects in plant disease epidemics in network models at multiple scales. (1) In the case of human management networks for plant disease, each node of a network has four main components: plant communities, microbial communities, human information (among researchers, extension agents, farmers, and other stakeholders), and environmental conditions, along with their interactions. The links between nodes, representing the rate of movement between them, have three parts: the rates for plant materials, the rates for microbes, and the rates for information. Network resilience for information flow is an important goal for such systems. Game theory can provide insights into how human agents decide how to invest their efforts in strengthening information networks, and how policies can support more resilient networks. (2) For the case of within-plant signalling networks, each node has a comparable set of four main components: plant signals (often in the form of phytohormones) and development status, microbial communities and plant disease status, microbial signals (often in the form of quorum sensing molecules), and micro-environmental conditions, along with their interactions. In effect, human information is replaced by plant signals and microbial signals in this second model. The links between nodes have three parts: the rates for microbes, the rates for microbial signals (which may move separately from the microbes, themselves), and the rates for plant signals. Understanding how to enhance adaptive plant signalling networks and microbial signalling networks that support plant productivity, and disrupt microbial signalling networks that contribute to pathogenicity, will be an important step for improved disease management.

Keywords

Decision making Dynamic networks Host as habitat Mesoscale networks Multi-scale modelling Quorum sensing Systems biology Biofilms 

Notes

Acknowledgements

I appreciate very helpful and thorough comments from an anonymous reviewer, stimulating discussions with P. Garfinkel, S. Lindow, B. Natarajan, and C. Scoglio that contributed to the development of this paper, assistance with Fig. 3 from Mark Stadtlander, and support by US NSF Grant EF-0525712 as part of the joint NSF-NIH Ecology of Infectious Disease program, US NSF Grant DEB-0516046, USDA APHIS Grant 11-8453-1483-CA, USDA NC RIPM Grant 2010-34103-20964, Ceres Trust, and the Kansas Agricultural Experiment Station (Contribution No. 12-196-J).

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© KNPV 2012

Authors and Affiliations

  1. 1.Department of Plant PathologyKansas State UniversityManhattanUSA

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