Antimicrobial Resistance: Factors to Findings : Omics and Systems Biology Approaches

It addresses environmental reservoirs and mobile genetic agents in AMR, host-pathogen interactions and physiology in the development of resistance, drug repurposing and development, and cutting-edge tools such as machine learning, AI for big data analysis, and genomic surveillance.

Antimicrobial resistance (AMR) is increasing globally at an incredible rate, and many infectious diseases have already reached an alarming stage of resistance to existing treatments. WHO reports that nearly1.27 million people currently die each year due to resistant infections, and AMR is projected to account for 10 million annual deaths globally by 2050. There is an urgent need for novel approaches to address this issue. Omics technologies are powerful research tools used extensively to study pathogen biology and the activity of microbial agents. These tools, paired with systems biology approaches, can provide novel insights into antimicrobial susceptibility and resistance, and aid in the development of new, more effective measures to combat resistant pathogens. This book provides a comprehensive overview of omics technologies to study pathogen biology, including proteomics, genomics, transcriptomics, metabolomics, and microbiome analysis, and the role of systems biology in developing strategies to combat resistant pathogens. It addresses environmental reservoirs and mobile genetic agents in AMR, host-pathogen interactions and physiology in the development of resistance, drug repurposing and development, and cutting-edge tools such as machine learning, AI for big data analysis, and genomic surveillance. The final section discusses future perspectives on omics-systems biology in AMR, and identifies opportunities for scientific collaboration in the global fight against antimicrobial resistance. This book serves as a comprehensive and accessible resource for researchers in academia and industry focused on immunology, drug development, biotechnology, and systems biology.