ArticlePloS one2016
Simulating Serial-Target Antibacterial Drug Synergies Using Flux Balance Analysis.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Study on collateral sensitivity of tigecycline to colistin-resistantMicrobiology spectrum · 2025Article
- Perturbation ofmBio · 2025Article
- Navigating collateral sensitivity: insights into the mechanisms and applications of antibiotic resistance trade-offs.Frontiers in microbiology · 2024Review
- Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling.Antibiotics (Basel, Switzerland) · 2023Review
- A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions.Frontiers in microbiology · 2021Review
- Antibiotic resistance: Time of synthesis in a post-genomic age.Computational and structural biotechnology journal · 2021Review
- Opportunities at the Interface of Network Science and Metabolic Modeling.Frontiers in bioengineering and biotechnology · 2020Review
- Collateral sensitivity constrains resistance evolution of the CTX-M-15 β-lactamase.Nature communications · 2019Article
- Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa.GigaScience · 2018Article
- Antibiotic efficacy-context matters.Current opinion in microbiology · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.