A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we want to bring together studies on the below listed sections, to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions:
- Computational Prediction of Pathogen-Host Interaction Data
- Text Mining of Pathogen-Host Interaction Data from Literature
- Bioinformatics Analysis of Infection Mechanisms through Pathogen-Host Interactions
- Structural Biology of Pathogen-Host Interactions
Computational Prediction of Pathogen-Host Interaction Data
Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data is far from complete for a system view of infection mechanisms through PHIs. Therefore, the available computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest.
Text Mining of Pathogen-Host Interaction Data from Literature
The emergence of large-scale PHI data has led to the development of PHI specific databases such as PHIbase, PHIDIAS, VirusMINT, VirhostNet, PATRIC, and PHISTO. However, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature.
Bioinformatics Analysis of Infection Mechanisms through Pathogen-Host Interactions
Bioinformatics analysis of PHIs is crucial to enlighten the infection mechanisms. The general focus of computational analyses of PHI data has been on investigating the common and specific behaviors of different types of pathogens during infections, by comparing their PHI data. Developing novel bioinformatics methods to analyze the increasing amount of experimentally-found and computationally-predicted PHI data is required to capture a system view of infection mechanisms.
Structural Biology of Pathogen-Host Interactions
The development of new and more efficient antimicrobial therapeutics calls for detailed information on protein structures, in particular quaternary structure for PHIs. Only few studies have been published on structural systems biology of PHIs. Once the infection mechanisms are known, the importance of structural biology perspective on PHI data will be even more pronounced, especially for drug design purposes.
A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we want to bring together studies on the below listed sections, to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions:
- Computational Prediction of Pathogen-Host Interaction Data
- Text Mining of Pathogen-Host Interaction Data from Literature
- Bioinformatics Analysis of Infection Mechanisms through Pathogen-Host Interactions
- Structural Biology of Pathogen-Host Interactions
Computational Prediction of Pathogen-Host Interaction Data
Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data is far from complete for a system view of infection mechanisms through PHIs. Therefore, the available computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest.
Text Mining of Pathogen-Host Interaction Data from Literature
The emergence of large-scale PHI data has led to the development of PHI specific databases such as PHIbase, PHIDIAS, VirusMINT, VirhostNet, PATRIC, and PHISTO. However, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature.
Bioinformatics Analysis of Infection Mechanisms through Pathogen-Host Interactions
Bioinformatics analysis of PHIs is crucial to enlighten the infection mechanisms. The general focus of computational analyses of PHI data has been on investigating the common and specific behaviors of different types of pathogens during infections, by comparing their PHI data. Developing novel bioinformatics methods to analyze the increasing amount of experimentally-found and computationally-predicted PHI data is required to capture a system view of infection mechanisms.
Structural Biology of Pathogen-Host Interactions
The development of new and more efficient antimicrobial therapeutics calls for detailed information on protein structures, in particular quaternary structure for PHIs. Only few studies have been published on structural systems biology of PHIs. Once the infection mechanisms are known, the importance of structural biology perspective on PHI data will be even more pronounced, especially for drug design purposes.
