Plants use various strategies to resist infection by a particular pathogen 4. These strategies are part of the plant's innate immune system and can be grouped into two broad categories 5. The first recognizes common pathogen-associated molecular patterns (PAMPs), and acts as an early plant warning to potential infection 6. This recognition leads to the induction of a basal plant defense, which in some cases includes a hypersensitive response (HR). The HR is characterized by the rapid death of cells surrounding the infected region and commonly leads to a broad spectrum plant response, the Systemic Acquired Resistance 7.

A second defense system in plants involves pairs of gene products, an effector molecule from the pathogen and an associated resistance protein (R) from the host, which recognizes it. This defense mechanism is highly specific and is triggered once a given effector is recognized by its associated R defense protein 5.

Plants with the capacity for protection from a pathogen attack are considered as resistants and a pathogen that lacks the ability to infect it is referred to as avirulent on that plant 4. In this case, the host-pathogen interaction is considered incompatible. On the other hand, when a compatible interaction occurs, the pathogen becomes virulent and a plant that is incapable of resisting the attack is considered non-resistant.

Plant pathogens have developed several strategies to evade such plant defense responses and to become virulent. For some of these pathogens the evasion mechanisms are at least partially known, as in the case of bacteria such as Pseudomonas syringae. However, for most plant pathogen species, these evasion mechanisms are almost completely unknown. This is the case for P. infestans, the causal agent of late blight of potato, a disease that affects S. tuberosum and some other species in the Solanaceae family 8. Oomycetes from the genus Phytophthora are plant pathogens devastating for agriculture and natural ecosystems 9. For instance, in the United States alone, P. infestans causes estimated losses that exceed $US 5 billion annually 10.

Despite its economic importance, the fundamental molecular mechanisms underlying the pathogenicity of P. infestans are poorly understood. It was not until recent years that information crucial to the understanding of its genomics and infectious mechanisms was accessible to the research community 11. For example, in 2006, the first effort to classify the secretome of plant pathogenic Oomycetes was carried out by Kamoun et al. Furthermore, although the general molecular events associated with the interaction between P. infestans and S. tuberosum were already known in 1991 12, it was not until last year (2008) that all the known molecular and cytological processes underlying plant-pathogen interactions in various Phytophthora species were revised 9.

From the biological strategies used so far to study the processes underlying plant-pathogen interactions, three are most suitable as basis for a computational systems biology approach: (a) gene expression, (b) structural and comparative genomics and (c) protein-protein interactions.

Gene expression approaches constitute a starting point from which to determine the best strategy for building a computational model of a plant disease. Host-expressed molecules give insights into the underlying defense mechanisms, whereas identification of the pathogen counterparts allows us to ascertain possible mechanisms of attack and/or avoidance mechanisms used to establish a disease.

Along with differential gene expression analysis, this is the most common modern approach to studying plant pathogen interactions, mostly due to the proteomic techniques as well as data mining and functional genomics tools available nowadays.

To date, one nuclear and six chloroplast genomes have been sequenced and two more nuclear genome sequencing projects are in progress in Solanaceous species (Additional file 1). On the pathogen side, five Oomycete genomes have been sequenced 11 and several studies at the genome scale have been carried out thanks to the availability of genomic information on these Oomycetes 383940 and their hosts.

Therefore, the possibility of performing comparisons between different organisms at the sequence level 40 has allowed agronomically important resistance genes in potato to be isolated 41, pathogen avirulence genes 42 and gene families 10 to be identified, and novel proteins implicated in a given interaction to be identified 43. For example, in the case of S. tuberosum, comparative analysis has revealed a physical co-localization between resistance loci in tomato, tobacco and pepper 44.

This approach has also revealed how two widely divergent microorganisms, P. infestans and the human malaria parasite Plasmodium falciparum, use equivalent host-targeting signals to deliver virulence and avirulence gene products into their hosts 45. These products have been characterized by a particular protein motif, leading to the hypothesis of pathogenicity mechanisms conserved between both organisms 46. This motif is the host-targeting (HT) signal of P. falciparum, centered on an RxLx core, revealed after the discovery of the RxLR host translocation motif of Oomycete effectors 474849. Owing to the availability of such data, it has been shown that although Plasmodium and Phytophthora are divergent eukaryotes, they share leader sequences, which suggests a conserved machinery for transport of effector proteins, a finding otherwise hard to achieve.

One approach to study protein-protein interactions is by using yeast two hybrid screening, co-immunoprecipitation 50 or surface plasmon resonance. This is arguably the most important approach towards a broad understanding of any plant pathogen interaction. It enables some mechanisms for the suppression of host defense in several organisms, such as the fungal pathogen Septoria lycopersici 51 or the Oomycete Phytophthora sojae 52, to be revealed.

In the case of P. infestans, relevant host defense suppression molecules have been also identified by this approach, such as the extracellular protease inhibitors EPI1 53, EPI10 - the first protease inhibitor reported in any plant-associated pathogen, which suppresses tomato defense by targeting - the P69B subtilisin-like serine protease 54, and the EPIC family of secreted proteins that target the extracellular cysteine protease PIP1 (Phytophthora Inhibited Protease 1) 55.

Protein-protein interactions play an important role in recognition between plant pathogens and their hosts. This recognition has been studied at two levels: recognition of the host by the pathogen and recognition of the pathogen by the host 5657. During an interaction, host resistance (R) and pathogen avirulence (Avr) proteins interact in a gene-for-gene manner. Proteins encoded by R alleles recognize the products of corresponding Avr alleles, thus triggering disease resistance. Using an association genetics approach 58, the P. infestans Avr3a effector was shown to be recognized in tomato cytoplasm by R3a (a member of the R3 complex locus on chromosome 11). R3a was isolated by positional cloning the same year 41.

Together, these and other studies 5923, along with computational chemistry and/or computational modeling and prediction of protein-protein interactions 60, provide valuable information about the recognition mechanisms in S. tuberosum - P. infestans R-Avr interactions and could lead to the identification of metabolic and/or signaling pathways underlying incompatible interactions.

In cases where experimental data for a biological system start to accumulate, it is feasible and convenient to integrate all the information gathered into a quantitative model. This approach allows us to obtain a mathematical and networked framework for a descriptive model of the biological phenomenon 61. This type of model strengthens the predictive capacity of future responses, for instance under different conditions, and it also helps to broaden our view of the potential interactions that could take place in any molecular reaction 62.

In order to capture time-dependent dynamic phenomena, a systems biology approach should allow us to integrate various ranges of spatial and temporal biological scales, as well as processing of different signals, genotypic variation and responses to external perturbations. As seen in the previous section, typical experiments describing the interaction between P. infestans and its hosts are clearly related to each of these characteristics.

Functional genomics and proteomic approaches produce the most suitable data for the development of a theoretical model 61. For instance, microarray-based differential expression analysis evaluates expression patterns at different times 30, under different conditions 2133 with host and pathogen genotypic variation. On the other hand, gene expression and host targeting of protease inhibitors work at different levels of signaling and at different spatial and temporal scales 5453.

Data gathered from such plant-pathogen interaction approaches, along with the development of interaction, pathways and metabolism databases 6364, as well as standardized systems biology languages 6566 and in silico research platforms 6768, have opened the door to modern computational model approaches at the molecular level in several organisms, including Oomycetes.

Predominantly, phytopathologists have used computational and quantitative modeling approaches to describe the temporal dynamics of plant diseases. Consistently, the bulk of the literature written in this field has been focused on the epidemiology of the disease, so research on the modeling of plant-pathogen molecular interactions is under-represented.

Plants resist pathogen attacks by shifting their defense mechanisms, as reflected in quantitative and kinetics enhancements 62. The mechanism that controls host defense activation consists of a highly interconnected network, in which host defense genes interact with each other as well as with effector proteins present in the cell 909192. The availability of high-throughput gene expression and proteomics data has generated an unprecedented opportunity for comprehensive study of these types of biological networks 8993.

Since an important phase in host-pathogen interactions involves protein-protein recognition 9491, efforts to elucidate networks of such interactions are of special interest in phytopathology. For example, a whole-genome computational strategy to infer protein interactions was applied to ten pathogens, including species of Mycobacterium, Apicomplexa and Kinetoplastida 91. This work started with the identification of pairs of matching proteins known to interact between the host and the pathogen, and by assessing the likelihood of this interaction by means of structural modeling, expression properties and subcellular location. As a result, an enriched candidate set of proteins is obtained, suitable for experimental study.

With the current genome sequence information for several Phytophthora genomes (Additional file 1) and those under sequencing 1195, this approach could be applicable to a Phytophthora-Solanaceae model and thus enhance our limited knowledge about the molecular interactions in these genera.

Another good theoretical framework to start working with is a space of interconnected operators such as a boolean network (Figure 1). Boolean networks present some advantages when compared to similar strategies such as hidden Markov models 9697. For instance, it is possible to perform a simulation while "avoiding the statistical basis around them, provides the option to perform simpler computational simulations, insert additional regulators or quantitative and biochemical data parameters into the model when available" 98.

Although clustering analysis can be used to infer gene function from expression data, the detailed interaction between genes within or between clusters cannot be deduced by this approach 99. In order to deduce such interactions, data from differential expression analysis can be represented in a boolean formalism. This representation can be achieved in a typical boolean binary form, where repression and/or induction of a given gene can be expressed by an on or off switch and thus translated into a network structure and simulated by computational analysis. This approach has been successfully implemented in the simulation of plant defense signaling networks in Arabidopsis thaliana in response to different treatments with salicylic acid, jasmonic acidand ethylene 98 (figure 2). For example, genes up-regulated to the same level in both treatments can be expressed by an OR operator, as in the case of phyA and PhyB in figure 2, thus leading to different possible initial states (input domains) as represented by panels A and B in the same figure. Data from differential expression analysis can also be represented by three possible boolean states (Table 2). This approach has been successfully used in the inference of gene regulatory networks 100 where "-1" was also introduced to address the negative interaction between components in the network. Experimental information on the compatible interaction between P. infestans and S. tuberosum is being approached by our laboratory using a similar strategy, in order to hypothesize the network space of carbonic anhydrase in this interaction.

This approach can also be used in systems lacking biological information, by gathering data common to other organisms or from related species. This possibility opens the door to implementation in other species of Oomycetes where lack of information is typical.