Abstract
BackgroundPhytophthora infestans is a devastating oomycete pathogen of potato production worldwide. This review explores the use of computational models for studying the molecular interactions between P. infestans and one of its hosts, Solanum tuberosum.Modeling and conclusionDeterministic logistics models have been widely used to study pathogenicity mechanisms since the early 1950s, and have focused on processes at higher biological resolution levels. In recent years, owing to the availability of high throughput biological data and computational resources, interest in stochastic modeling of plant-pathogen interactions has grown. Stochastic models better reflect the behavior of biological systems. Most modern approaches to plant pathology modeling require molecular kinetics information. Unfortunately, this information is not available for many plant pathogens, including P. infestans. Boolean formalism has compensated for the lack of kinetics; this is especially the case where comparative genomics, protein-protein interactions and differential gene expression are the most common data resources.
Highlights
Phytophthora infestans is a devastating oomycete pathogen of potato production worldwide
Control and management of plant diseases and the identification of factors that contribute to the spread a given plant pathogen attack are at the basis of phytopathology
Most of the review will focus on the Phytophthora infestans - Solanum tuberosum pathosystem, but its discussion will be general enough as to be applicable to any other plant pathogen system
Summary
Control and management of plant diseases and the identification of factors that contribute to the spread a given plant pathogen attack are at the basis of phytopathology. This model was based on the assumption that disease can be significantly higher in monocultures than in cultivar mixtures and it only considered stochastic variation of spore dispersal at constant sporulation rate, there exist many other sources of stochastic variation (such as genotypic variation) [2] No matter whether they are stochastic or deterministic, the models described above have been focused on higher http://www.tbiomed.com/content/6/1/24 scales of plant pathogen interactions, such as the population, organ or ecological level. It was shown that it could operate as an "onoff" gene expression switch that is sensitive to the environment, allowing the question about how bacteria really behave or respond to be answered in QS This topic is absent in some plant-pathogen organisms, such as P. infestans, the characteristics of quantitative modeling of molecular mechanisms could elucidate several questions in phytopathology. This possibility opens the door to implementation in other species of Oomycetes where lack of information is typical
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