Simulating Serial-Target Antibacterial Drug Synergies Using Flux Balance Analysis

PloS One
Andrew S KruegerMorten O A Sommer

Abstract

Flux balance analysis (FBA) is an increasingly useful approach for modeling the behavior of metabolic systems. However, standard FBA modeling of genetic knockouts cannot predict drug combination synergies observed between serial metabolic targets, even though such synergies give rise to some of the most widely used antibiotic treatments. Here we extend FBA modeling to simulate responses to chemical inhibitors at varying concentrations, by diverting enzymatic flux to a waste reaction. This flux diversion yields very similar qualitative predictions to prior methods for single target activity. However, we find very different predictions for combinations, where flux diversion, which mimics the kinetics of competitive metabolic inhibitors, can explain serial target synergies between metabolic enzyme inhibitors that we confirmed in Escherichia coli cultures. FBA flux diversion opens the possibility for more accurate genome-scale predictions of drug synergies, which can be used to suggest treatments for infections and other diseases.

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Citations

Oct 27, 2017·FEMS Microbiology Reviews·Kristina Klobucar, Eric D Brown
Feb 8, 2019·Nature Communications·Carola E H RosenkildeMorten O A Sommer
Feb 12, 2021·Frontiers in Bioengineering and Biotechnology·Varshit DusadDiego A Oyarzún
Oct 20, 2017·Current Opinion in Microbiology·Jason H YangJames J Collins
Jun 19, 2021·Computational and Structural Biotechnology Journal·Teresa Gil-GilJosé Luis Martínez

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Methods Mentioned

BETA
gene knockouts
ChIP-Seq

Software Mentioned

R
MOMA
res
FBA
Metacyc
KEGG
optimizeProb
R package Sybil ( Systems Biology Library for R )
Flux Balance Analysis ( FBA )
ko

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