ArticleNature communications2016
A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
93 citing papers in PubMed.
- COCOA.jl-a Julia package for high-performance analysis of concordance and kinetic modules in biochemical networks.Bioinformatics (Oxford, England) · 2026Article
- Article
- Article
- A logistic model for glucose‑driven asymmetry in multi‑phase E. coli growth kinetics.Scientific reports · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- Complex Kinetic Models Predict β-Carotene Production and Reveal Flux Limitations in RecombinantACS synthetic biology · 2025Article
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Redox poise in R. rubrum phototrophic growth drives large-scale changes in macromolecular pathways.PLoS computational biology · 2025Article
- Kinetic modules are sources of concentration robustness in biochemical networks.Science advances · 2025Article
- Computational biology predicts metabolic engineering targets for increased production of 103 valuable chemicals in yeast.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks.Briefings in bioinformatics · 2025Article
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network.mSystems · 2024Article
- Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data.Metabolic engineering communications · 2024Article
- Engineering Crassulacean Acid Metabolism in CCold Spring Harbor perspectives in biology · 2024Review
- Rational strain design with minimal phenotype perturbation.Nature communications · 2024Article
- Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches.Frontiers in immunology · 2024Review
- Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost.Frontiers in artificial intelligence · 2024Article
- Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states.Nature catalysis · 2024Article
33 more citing papers are in PubMed but not listed here.
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
Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). The Pearson correlation coefficient between experimental data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47 (k-ecoli457 is available for download at http://www.maranasgroup.com).
Indexed as
Identifiers
What OpenQuestion holds
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.