My current focus is on using statistical, bioinformatics and mathematical modelling tools to investigate the dynamics of Pseudomonas aeruginosa in relation to antibiotic treatment. For this purpose, I am analysing a large collection of P. aeruginosa genomes and isolates sampled longitudinally from 46 cystic fibrosis (CF) patients with chronic infections. Sputum samples were collected every day for six months from these patients and using a high throughput workflow developed in the Bryant lab we are generating deep sequencing data for the P. aeruginosa in these samples. The study includes samples over the course of both stable periods and during intensive treatment, providing an unprecedented opportunity to capture the dynamics leading to antimicrobial resistance. I am combining this genomic dataset with other daily datapoints for these patients, such as, in-depth clinical measurements and treatment history to address the following questions:
- What mutations and gene loss/gain events in P. aeruginosa are associated with antibiotic therapy?
- What are the evolutionary dynamics of these genomic changes? How long does it take for resistance to arise and degrade for different antibiotics? Is this affected by other drugs the patient is taking?
- What is the effect of antibiotic resistance on clinical outcome?
I am interested in the lung microbiome, high-dimensional statistics, causal inference and large-scale data.