From detection to predicting infectious disease and antibiotic resistance outcomes: employing experimental evolution, omics-stress mapping and computational biology to determine and predict what matters in antibiotic resistance.
Infectious respiratory diseases (IRDs) are a global health concern, that when left untreated can rapidly become severe and lead to death. Each year ~3 million people succumb to these infections world-wide, with the bacterial pathogen Streptococcus pneumoniae being one of the leading causes. IRD by bacteria are driven by complex host-pathogen interactions, which are characterized by a critical balance between host defense and tissue integrity to achieve bacterial control. Ultimately, if this balance is not properly maintained, the infection may escape control and progress, at which time supportive critical care becomes unavoidable. However, determining when, whether and how to interfere are not straightforward questions. Challenges faced in this space are thereby at least two-fold: 1) There is a lack of diagnostics that can inform on, and/or evaluate, ongoing treatment; and 2) The rise in antibiotic resistance is reducing treatment options, underscoring the critical need for new antimicrobial strategies. In infectious diseases, novel diagnostics are mostly focused on pathogen-identification. However, with the emergence of new experimental and computational tools, opportunities are arising to achieve a deeper, integrated understanding of how pathogens interact with drugs, their environment and host, possibly opening up pathways towards the development of on-demand, informed and tailored treatment decisions. My lab focuses on the development and application of experimental and computational Systems Biology tools to study infectious diseases as complete systems while in interaction with their environment. In this presentation I will highlight how we employ a mixture of tools and ideas from the fields of Biology, Chemistry, Physics and Computer Science, to develop approaches to predict the emergence of drug-resistance and disease progression, and develop strategies to resolve them.