Dr. Tianyu Zhang (Dept. of Mathematical Sciences, MSU) 

03/13/23  3:10pm

Abstract: 

Antibiotics are our primary approach to treating complex infections, yet we have a poor understanding of how these drugs affect microbial communities.  Antibiotic therapy against bacteria infections is frequently predicated on antimicrobial resistance profiles of isolates of classic pathogens. Given the diversity of the polymicrobial community, these susceptibility profiles are often unable to predict therapeutic outcomes. Here we develop a mathematical model for antibiotic treatment of a particular polymicrobial community, namely, chronic lung disease cystic fibrosis (CF), which reflects the physiology of different species present in the community and their interactions. Comparison between model predictions and experimental results will be discussed. In particular, our results indicate that it is paramount to understand the complex species-species and guild-guild interdependencies and competitive interactions in order to enable better therapeutic outcomes of antibiotic therapy.