Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models.

Molecular systems biology - 1 Jul 2010

Lewis Nathan E, Hixson Kim K, Conrad Tom M, Lerman Joshua A, Charusanti Pep, Polpitiya Ashoka D, Adkins Joshua N, Schramm Gunnar, Purvine Samuel O, Lopez-Ferrer Daniel, Weitz Karl K, Eils Roland, König Rainer, Smith Richard D, Palsson Bernhard Ø

Abstract excerpt

After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% of active reactions from FBA optimal...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Adaptation, Physiological
Share to Computer Simulation

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Gene Expression Regulation, Bacterial

Share to Gene Regulatory Networks

Share to Metabolomics

Share to Proteomics

Share to Reproducibility of Results

Share to Systems Biology

Source

Open in PubMed Open full text

Identifiers

PMID: 20664636
DOI: 10.1038/msb.2010.47
PMCID: PMC2925526

Genes

GEM

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models.PMID 20664636Read ↗

Identification of genome-scale metabolic network models using experimentally measured flux profiles.Semantic proximity Read ↗

Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction.Semantic proximity Read ↗

Experimental Evolution Reveals Unifying Systems-Level Adaptations but Diversity in Driving Genotypes.Semantic proximity Read ↗

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.Semantic proximity Read ↗

Multiple Optimal Phenotypes Overcome Redox and Glycolytic Intermediate Metabolite Imbalances in Escherichia coli pgi Knockout Evolutions.Semantic proximity Read ↗

Reconstructing organisms in silico: genome-scale models and their emerging applications.Semantic proximity Read ↗

An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models.Semantic proximity Read ↗

Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model.Semantic proximity Read ↗

Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes.Semantic proximity Read ↗

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0.Semantic proximity Read ↗

Stoichiometric Representation of Gene-Protein-Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction.Semantic proximity Read ↗

Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.