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.

Our research

<p>From pathogen genomics to resistance-proof drug regimes</p>

Pathogen Genomics

From pathogen genomics to resistance-proof drug regimes

<p>From big-data and machine-learning to personalized antibiotic treatment</p>

Digital Health

From big-data and machine-learning to personalized antibiotic treatment

<p>From species evolution and species interactions to whole community dynamics and stability</p>

Ecology and Evolution

From species evolution and species interactions to whole community dynamics and stability

Featured publications

Systematic identification of gene-altering programmed inversions across the bacterial domain

Nucleic Acids Research (2023)

Antibiotic combinations reduce Staphylococcus aureus clearance

Nature (2022)

Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections

Science (2022)

Contact info

Principal Investigator

Prof. Roy Kishony
rkishony[at]technion.ac.il

Administration Manager

Sivan Geisler – Edelbaum
sivangei[at]technion.ac.il
Phone: +972 (0) 77-887-1529

We are recruiting outstanding Postdoctoral fellows, Ph.D. and M.Sc students.