ReviewIET systems biology2014
Dynamic flux balance analysis for synthetic microbial communities.
Review in IET systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed.
- Article
- Dynamical Systems-Constrained Metabolic Modeling Enables Forecasting of Host-Microbiome Dynamics.bioRxiv : the preprint server for biology · 2026Article
- Keystone concept revisited: insights into microbial community dynamics and control.Nature reviews. Microbiology · 2026Review
- Reconstruction of human metabolic models with large language models.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Constraint-Based Metabolic Modeling Approach for Microbial Communities.Methods in molecular biology (Clifton, N.J.) · 2026Article
- COmmunity and Single Microbe Optimisation System (COSMOS).NPJ systems biology and applications · 2025Article
- Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin-degrading consortium.Nature communications · 2025Article
- Algorithm of spatial-temporal simulation for environment-strain interactions in strain-strain consortia based on resource competition mechanism.Computational and structural biotechnology journal · 2024Article
- Article
- Predicting microbial interactions with approaches based on flux balance analysis: an evaluation.BMC bioinformatics · 2024Article
- Stochastic biological system-of-systems modelling for iPSC culture.Communications biology · 2024Article
- 'Social' versus 'asocial' cells-dynamic competition flux balance analysis.NPJ systems biology and applications · 2023Article
- Elucidating dynamic anaerobe metabolism with HRMASNature chemical biology · 2023Article
- Synthetic microbial communities (SynComs) of the human gut: design, assembly, and applications.FEMS microbiology reviews · 2023Article
- Alpha-Hemolysin from Staphylococcus aureus Obstructs Yeast-Hyphae Switching and Diminishes Pathogenicity in Candida albicans.Journal of microbiology (Seoul, Korea) · 2023Article
- Optimization and Scale-Up of Fermentation Processes Driven by Models.Bioengineering (Basel, Switzerland) · 2022Review
- Bottom-up synthetic ecology study of microbial consortia to enhance lignocellulose bioconversion.Biotechnology for biofuels and bioproducts · 2022Review
- A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS).Nature protocols · 2021Article
- An evolutionary algorithm for designing microbial communities via environmental modification.Journal of the Royal Society, Interface · 2021Article
- Microfluidic and mathematical modeling of aquatic microbial communities.Analytical and bioanalytical chemistry · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamic flux balance analysis (DFBA) is an extension of classical flux balance analysis that allows the dynamic effects of the extracellular environment on microbial metabolism to be predicted and optimised. Recently this computational framework has been extended to microbial communities for which the individual species are known and genome-scale metabolic reconstructions are available. In this review, the authors provide an overview of the emerging DFBA approach with a focus on two case studies involving the conversion of mixed hexose/pentose sugar mixtures by synthetic microbial co-culture systems. These case studies illustrate the key requirements of the DFBA approach, including the incorporation of individual species metabolic reconstructions, formulation of extracellular mass balances, identification of substrate uptake kinetics, numerical solution of the coupled linear program/differential equations and model adaptation for common, suboptimal growth conditions and identified species interactions. The review concludes with a summary of progress to date and possible directions for future research.
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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.