Abstract
Pathogens manipulate the cellular mechanisms of host organisms via pathogen–host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein–protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.
Highlights
Infectious diseases are one of the preliminary causes of death worldwide each year
The drug resistance of pathogens demands alternative solutions for infectious threats, i.e., targeting host proteins required by pathogens for replication and persistence within the host organism or targeting pathogen– host interactions (PHIs) (Murali et al, 2011; Zoraghi and Reiner, 2013)
If these host proteins are indispensable for pathogens during infections, but not essential for host cells, they may serve as antimicrobial therapeutic targets to fight drug resistance
Summary
Infectious diseases are one of the preliminary causes of death worldwide each year. Emerging and reemerging diseases and drug resistant pathogens have made the problem more serious for human beings. Different levels of omics data collected from pathogens and/or infected cells are crucial components that drive bioinformatic analyses facilitating the construction and analysis of infectionspecific gene-regulatory, metabolic, and protein–protein networks (Westermann et al, 2012; Schulze et al, 2015). Such network-based computational systems biology analyses of PHIbased omics data enable the elucidation of infection mechanisms and their dynamics, the identification of potential drug targets for the next-generation antimicrobial therapeutics, and the development of novel and personalized strategies for the prevention and treatment of infections. The available PHI databases and the current snapshot of the literature on text mining for PHI data are presented
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
