Mathematics of drug resistance

Antimicrobial resistance of infectious agents is a growing global problem. To prevent the further evolution and spread of drug resistance, rational design of antibiotic treatment across scales is needed. Immunity is an important, but often overlooked factor in the clearance of resistant infections. In my work, I use mathematical modeling of within host pathogen dynamics to study the implications of the interplay between host immune responses and antibiotic treatment. My aim is to understand how within-host infection processes are modulated by drugs, and how treatment parameters can be optimized for successful therapies in synergy with host’s natural defenses (Gjini and Brito, 2016).

Another direction of this work relates to the consequences of antibiotic resistance at the population level. I develop mathematical models to capture the epidemiological dynamics of antibiotic resistant pathogen strains in response to antibiotic treatment and other interventions. Such models have threefold applications: i) to generate hypotheses about the magnitude, direction, and implications of resistance selection; ii) to uncover optimal population treatment practices in particular ecological scenarios; iii) and to interpret mechanistically existing patterns in host communities.


  • Dr. Vitaly V. Ganusov, University of Tennessee, Knoxville, USA
  • Dr. Kevin Wood, University of Michigan, Ann Arbor, USA
  • Dr. Patricia H. Brito, Instituto Gulbenkian de Ciência, Oeiras, Portugal