A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action
Name
nihms-1526839.pdf
Description
Accepted version
Size
2.27 MB
Format
Adobe PDF
Checksum (MD5)
dc2c45c10a38082332ff9f0ad50cfe2b
Author(s)
Yang, Jason H
Wright, Sarah N
Hamblin, Meagan
McCloskey, Douglas
Alcantar, Miguel A
Schrübbers, Lars
Lopatkin, Allison J
Satish, Sangeeta
Nili, Amir
Palsson, Bernhard O
Date Issued
2019
Journal
Cell
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
© 2019 Elsevier Inc. Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated “white-box” biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy. Causal metabolic pathways underlying antibiotic lethality in bacteria are illuminated by a network model-driven machine learning approach, overcoming limitations of existing “black-box” approaches that cannot reveal causal relationships from large biological datasets.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biology
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
10.1016/J.CELL.2019.04.016
https://doi.org/10.1016/J.CELL.2019.04.016
