Advances and Challenges in Understanding Microbial Pathogenesis through Systems Biology and Artificial Intelligence

Dear Colleagues,

Systems biology and artificial intelligence are emerging fields with tremendous potential to understand biological phenomena including the interactions between different biological components. The high-throughput approaches for the generation of biological data have greatly aided the advancement of these fields and paved the way to understanding microbial pathogenesis and associated therapeutic targets.

The ability of systems biological investigations to integrate data from various sources provides the major advantage of using these approaches for understanding microbial pathogenesis. In addition, artificial-intelligence-based approaches are enabling machines to learn from the data and to infer meaningful information. Incremental biological data generated through genomics-, proteomics-, transcriptomics-, and metabolomics-related studies are opening new avenues for the use of systems biology and artificial-intelligence-based approaches to identify key host–microbe factors and their mechanisms in microbial pathogenesis.

While the potential of these approaches seems limitless, several factors are posing challenges to the use of these approaches for understanding microbial pathogenesis. For instance, the complexity of host–pathogen interactions and their involvement at certain time points makes it difficult to identify key players and potential therapeutic targets against different organisms using systems biology. In addition, the high-throughput data generated using diverse techniques require several time-consuming and labor-intensive processing steps before these datasets can be integrated to understand microbial pathogenesis, which poses an additional challenge. The ability of artificial intelligence to mimic human reasoning may enable the inference of meaningful information from complex datasets, and may help to overcome these limitations. 

In conclusion, systems biology and artificial intelligence have great potential to understand microbial pathogenesis, and have ushered in a new era for the development of innovative therapeutic strategies. However, the challenges in the effective utilization of these approaches need to be addressed. This Special Issue will encompass different types of articles, including original research papers, reviews, case studies, and short communications related to the use of systems biology and artificial intelligence in microbial-pathogenesis-related studies. It will also address the challenges faced by the scientific community in using these approaches and discuss their potential solutions.

This Issue aims to provide a comprehensive overview on the use of systems biology and artificial intelligence to study microbial pathogenesis and develop innovative diagnostic and therapeutic approaches.

Dr. Abdul Arif Khan
Guest Editor

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• microbial pathogenesis
• systems biology
• systems biology advances
• systems medicine
• host–pathogen interactions
• metagenomics
• pathogenomics
• metabolomics
• proteomics
• transcriptomics
• microbiome
• systems medicine
• infectious disease
• synthetic biology
• pathogenesis-related proteins
• host–microbe symbiosis
• multi-omics data integration
• immunometabolism
• microbial ecology
• biochemical pathways
• gene regulation
• microbial evolution
• infectious disease research progress
• machine learning in medicine
• artificial intelligence
• artificial intelligence in biology
• machine learning in microbiology