ArticleBMC systems biology2013
An objective function exploiting suboptimal solutions in metabolic networks.
Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- MCC: automated mass and charge curation at the genome scale applied toMicrobiology spectrum · 2026Article
- Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux.Nature communications · 2023Article
- A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon onMetabolites · 2023Article
- Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.Computational and structural biotechnology journal · 2023Article
- Predicting stress response and improved protein overproduction in Bacillus subtilis.NPJ systems biology and applications · 2022Article
- A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS).Nature protocols · 2021Article
- Addressing uncertainty in genome-scale metabolic model reconstruction and analysis.Genome biology · 2021Review
- Dynamic Allocation of Carbon Storage and Nutrient-Dependent Exudation in a Revised Genome-Scale Model ofFrontiers in genetics · 2021Article
- Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine.BioMed research international · 2019Review
- Mathematical modelling of clostridial acetone-butanol-ethanol fermentation.Applied microbiology and biotechnology · 2017Review
- Predicting internal cell fluxes at sub-optimal growth.BMC systems biology · 2015Article
- Validation of an FBA model for Pichia pastoris in chemostat cultures.BMC systems biology · 2014Article
- Promise and reality in the expanding field of network interaction analysis: metabolic networks.Bioinformatics and biology insights · 2014Article
- Predictive sulfur metabolism - a field in flux.Frontiers in plant science · 2014Review
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3 authors.
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Abstract
backgroundFlux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power.
resultsWe propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation.
conclusionAccounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network.
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