A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action

Cell

Volumn 177, Issue 6, 2019, Pages 1649-1661.e9

  Yang, Jason H ^a,b   Wright, Sarah N ^a,b   Hamblin, Meagan ^b   McCloskey, Douglas ^c   Alcantar, Miguel A ^a,b   Schrübbers, Lars ^c   Lopatkin, Allison J ^a,b,d   Satish, Sangeeta ^e   Nili, Amir ^e   Palsson, Bernhard O ^c,f   Walker, Graham C ^g   Collins, James J ^a,b,d  

^a Massachusetts Institute of Technology   (United States)

^b BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^c Chalmers University of Technology   (Denmark)

^d HARVARD UNIVERSITY   (United States)

^e Boston University   (United States)

^f UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^g MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

adenylate energy charge; antibiotics; ATP; biochemical screen; LC MS MS; machine learning; metabolism; NADPH:NADP+ ratio; network modeling; purine biosynthesis

Indexed keywords

ADENINE; ADENOSINE TRIPHOSPHATE; ANTIBIOTIC AGENT; CARBON; OXYGEN; PURINE; ANTIINFECTIVE AGENT; PURINE DERIVATIVE;

ANTIBACTERIAL ACTIVITY; ARTICLE; BIOCHEMICAL ANALYSIS; CARBON METABOLISM; CONTROLLED STUDY; DRUG EFFICACY; ESCHERICHIA COLI; MACHINE LEARNING; NONHUMAN; OXYGEN CONSUMPTION; PRIORITY JOURNAL; PURINE SYNTHESIS; SIMULATION; WHITE BOX MACHINE LEARNING; BIOLOGY; DRUG EFFECT; IMMUNOLOGY; METABOLISM; PRECLINICAL STUDY; PROCEDURES; THEORETICAL MODEL;

ADENINE; ANTI-BACTERIAL AGENTS; COMPUTATIONAL BIOLOGY; DRUG EVALUATION, PRECLINICAL; ESCHERICHIA COLI; MACHINE LEARNING; METABOLIC NETWORKS AND PATHWAYS; MODELS, THEORETICAL; PURINES;

EID: 85065862749     PISSN: 00928674     EISSN: 10974172     Source Type: Journal    
DOI: 10.1016/j.cell.2019.04.016     Document Type: Article

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