Computational systems and network biology perspective: Understanding Klebsiella pneumoniae infection mechanisms

Abstract

Klebsiella pneumoniae (K. pneumoniae) is a pathogen that has been identified as the leading cause of pneumonia and septicemia worldwide, compounded by its multi-drug resistant nature. Computational and bioinformatics approaches are yet understudied in terms of K. pneumoniae, and only recently systems and network biology-based approaches have gained attention for examining antimicrobial resistance. In this review, we highlight the prevalent use of computational systems and network biology methods in understanding K. pneumoniae infection mechanisms. We summarized ranges from basic methods including differential equations, network science analysis, and statistical insights into large processes, to intricate condition-specific genome-wide networks. More specifically, the availability of large-scale systematic genome-wide data, and detailed cellular and molecular information have enabled the use of mathematical modeling to study K. pneumoniae infection mechanisms. Thus, these approaches have proven to be effective in supporting academic exploration, complementing experimental studies, and deepening overall understanding in terms of K. pneumoniae. This review is essential to advance our knowledge of K. pneumoniae host-pathogen interactions and infection mechanisms. Furthermore, it serves as a valuable resource for researchers seeking guidance in selecting optimal computational systems and network biology models for K. pneumoniae-related investigations.