ArticlePLoS computational biology2015
A Method to Constrain Genome-Scale Models with 13C Labeling Data.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Lipid accumulation in nitrogen and phosphorus-limited yeast is caused by less growth-related dilution.Metabolic engineering · 2026Article
- Hepatic glucose metabolism in the steatotic liver.Nature reviews. Gastroenterology & hepatology · 2024Review
- BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale.PLoS computational biology · 2023Article
- Tuning a high performing multiplexed-CRISPRiMetabolic engineering communications · 2022Article
- Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease.Metabolites · 2022Review
- Review
- In silico co-factor balance estimation using constraint-based modelling informs metabolic engineering in Escherichia coli.PLoS computational biology · 2020Article
- Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis.Metabolites · 2020Review
- Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective.NPJ systems biology and applications · 2020Review
- High-resolutionNature protocols · 2019Article
- Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches.BMC biology · 2019Review
- Article
- Predicting the metabolic capabilities of Synechococcus elongatus PCC 7942 adapted to different light regimes.Metabolic engineering · 2019Article
- Approaches to Computational Strain Design in the Multiomics Era.Frontiers in microbiology · 2019Article
- Advances in metabolic flux analysis toward genome-scale profiling of higher organisms.Bioscience reports · 2018Review
- Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism forMetabolites · 2018Article
- A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data.NPJ systems biology and applications · 2018Article
- A comprehensive evaluation of constraining amino acid biosynthesis in compartmented models for metabolic flux analysis.Metabolic engineering communications · 2017Article
- Understanding metabolism with flux analysis: From theory to application.Metabolic engineering · 2017Review
- The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism.BMC bioinformatics · 2017Article
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8 authors.
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Abstract
Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.
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