Review

Computational models in plant-pathogen interactions: the case of Phytophthora infestans

Andrés Pinzón^1,2 , Emiliano Barreto² , Adriana Bernal¹ , Luke Achenie³ , Andres F González Barrios⁴ , Raúl Isea⁵ and Silvia Restrepo¹

¹  Mycology and Phytopathology Laboratory, Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia

²  Bioinformatics center, Colombian EMBnet node, Biotechnology Institute, National University of Colombia, Bogotá, Colombia

³  Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg Virginia, USA

⁴  Grupo de Diseño de Productos y Procesos, Department of Chemical Engineering, Los Andes University, Bogotá, Colombia

⁵  Fundación IDEA, Centro de Biociencias, Hoyo de la puerta, Baruta 1080, Venezuela

author email corresponding author email

Theoretical Biology and Medical Modelling 2009, 6:24doi:10.1186/1742-4682-6-24

Published:	12 November 2009

Abstract

Background

Phytophthora 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 conclusion

Deterministic 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.