Predicting and
preventing the
evolution of antibiotic resistance
Can the future of antibiotic resistance be predicted?
How can Computational Biology change the foundations of infectious disease diagnostics?
Can antibiotic resistance be reversed?
Combining novel quantitative experimental techniques and clinical studies with mathematical modeling and advanced data analysis, we are studying microbial evolution with a specific focus on antibiotic resistance. We aim at understanding how bacterial pathogens evolve resistance to antibiotics within the human body during infection and how combinations of drugs can be used to slow down and perhaps even reverse this process.
From species evolution and species interactions to whole community dynamics and stability
Autonomous LLM-Driven Research — from Data to Human-Verifiable Research Papers
T. Ifargan*, L. Hafner*, M. Kern, O. Alcalay, R. Kishony
NEJM AIÂ (2024)
Antibiotic combinations reduce Staphylococcus aureus clearance
V. Lázár, O. Snitser, D. Barkan, R. Kishony
Nature (2022), 610(7932), pp.540-546.
Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections
M. Stracy, O. Snitser, I. Yelin, Y. Amer, M. Parizade, R. Katz, G. Rimler, T. Wolf, E. Herzel, G. Koren, J. Kuint, B. Foxman, G. Chodick, V. Shalev, R. Kishony
Science (2022), 375(6583), pp.889-894.