Predictive biology: understanding and reversing the evolution of antibiotic resistance

January 28, 2021

Location

Online

Presenters

Allison J. Lopatkin, Assistant Professor, Computational Biology, Barnard College, Columbia University, USA

Outline

Predictive biology is the next frontier in systems and synthetic microbiology. Experimental data, quantitative modeling, and machine learning can now be readily integrated to provide new and exciting insights into microbial community structure and dynamics. One particularly useful application of this interdisciplinary approach is in better understanding the evolution of antibiotic resistance, which is a growing global threat for human and agricultural health.
This webinar will discuss predictive microbiology in the context of antibiotic resistance with a specific focus on combining computational approaches and experimental measurements to better understand how resistance emerges. Ultimately, this information can be used to guide the development of novel strategies that curtail, or even reverse, the evolution of resistance.

References:

Lopatkin AJ, Collins JJ. Predictive biology: modelling, understanding and harnessing microbial complexity. Nat Rev Microbiol. 2020;18(9):507-520. doi:10.1038/s41579-020-0372-5
Lopatkin AJ, Bening SC, Manson AL, Stokes JM, Kohanski MA, Badran AH, Earl AM, Cheney NJ, Yang JH, Collins JJ. Clinically relevant mutations in core metabolic genes confer antibiotic resistance. Science. 2021 Feb 19;371(6531):eaba0862. doi: 10.1126/science.aba0862