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

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Science in nature

With the kind support of the Michael Bruno Memorial Award, we launched a series of scientific retreats, entitled “Science in Nature”, where scientific discussions are combined with nature hikes. We enjoy inviting colleagues locally and international to join us. These retreats serve the goal of connecting scientists across fields in a unique informal setting. We are grateful to have this opportunity and would like to thank the board of trustees of the Michael Bruno Memorial Award for making this exciting enterprise possible and real.

2018

2019

2022 - coming soon

2023 - coming later

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.

Our new projects combine novel quantitative experimental techniques with clinical studies and advanced BIG DATA analysis with opportunities for basic and translation impact.

Ideal candidates should have a strong background in Biology as well as quantitative experience in computer science, computational biology, mathematics, physics or a related field.