Simulating Serial-Target Antibacterial Drug Synergies Using Flux Balance Analysis

• Published in: PloS one Vol. 11; no. 1; p. e0147651
• Main Authors: Krueger, Andrew S, Munck, Christian, Dantas, Gautam, Church, George M, Galagan, James, Lehár, Joseph, Sommer, Morten O A
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.01.2016; Public Library of Science (PLoS)
• Subjects: Anti-Bacterial Agents - pharmacology; Antibacterial agents; Antibiotics; Bacterial infections; Biology; Biology and Life Sciences; Biomass; Complications and side effects; Computer simulation; Dosage and administration; Drug resistance; Drug Synergism; Drug therapy; E coli; Enzyme inhibitors; Enzymes; Epistasis, Genetic; Escherichia; Escherichia coli; Escherichia coli - drug effects; Escherichia coli - metabolism; Fluctuations; Flux; Gene expression; Genes, Bacterial; Genomes; Inhibitors; Inhibitory Concentration 50; Kinetics; Mathematical models; Medical treatment; Medicine and Health Sciences; Metabolic Engineering; Metabolic Flux Analysis; Metabolic Networks and Pathways; Metabolism; Metabolites; Microbial Sensitivity Tests; Microbial Viability; Modelling; Reaction kinetics; Research and Analysis Methods; United States; Denmark; United States > US; Massachusetts; Missouri
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0147651

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.