Data Challenge: Network Analysis of Co-resistance Patterns in Candida Species

This project will apply network theory and graph analysis techniques to the Vivli MIC data to map intricate co-resistance patterns among different antifungals (e.g., fluconazole and echinocandins) and potentially other antimicrobial classes. The primary goal is to identify common "hubs" of resistance, which would indicate strains or underlying mechanisms that confer resistance to multiple drugs simultaneously. This could reveal synergistic resistance mechanisms or shared regulatory pathways, such as cell wall stress responses that might impact both azole and echinocandin susceptibility, as hinted by physiological changes in fluconazole-resistant C. auris. Uncovering these complex co-resistance dynamics provides a holistic view of multi-drug resistance (MDR) in Candida. It can inform the development of combination therapies, guide the selection of new antifungal agents that circumvent common resistance pathways, and identify strains that pose a particularly high clinical threat due to broad-spectrum resistance.
Beyond analyzing individual drug resistance, the emergence of multi-drug resistance (MDR) represents a significant and growing concern. Network analysis is an ideal tool to identify clusters of co-resistance, thereby highlighting "super-resistant" Candida strains and the underlying pathways that confer resistance to multiple classes of drugs. This is crucial for prioritizing surveillance efforts and developing therapies that can effectively overcome broad-spectrum resistance.