Decoding Adaptation of Pathogens with Systems Biology and Artificial Intelligence: towards new strategies to combat human infections

Bacterial infections with the threat of antibiotic resistance is an emerging global health problem. Chronic infections are well-recognized clinical problems that contribute to this development.

One attractive strategy to combat such infections is to target gene products that are involved on bacterial adaptations dynamically changing stressful host environments.

To reveal such gene products, I have catalogued gene expression profiles of 32 human bacterial pathogens under 11 infection relevant stress conditions for 105 088 to reveal such responses in wide variety of human bacterial pathogens.

In addition, I will now complement this dataset with in vivogene expression profiles of bacterial pathogens in different infected-human specimens.

To dissect the complexity and extract important information from the rich and solid datasets, I will apply systems biology, which integrates biological and medical sciences with mathematical and computational disciplines by employing different approaches including trained artificial intelligence (AI) algorithms.

The overall aim of this research proposal is toidentify bacterial determinants that can be explored as targets for new antimicrobials by revealing mechanisms and/ or determinants that are critical for bacteria to maintain infection in humans.

Candidate gene products will be validated with functional assays and results will be used to train reinforced AI algorithms for more precise predictions.