ArticlePLoS computational biology2012
Temporal expression-based analysis of metabolism.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 42 citations in OpenAlex.
- Flux sampling and context-specific genome-scale metabolic models for biotechnological applications.Trends in biotechnology · 2026Review
- Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.NPJ systems biology and applications · 2023Article
- Integrative Gene Expression and Metabolic Analysis ToolBiomolecules · 2022Article
- Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments.PLoS computational biology · 2020Article
- MOOMIN - Mathematical explOration of 'Omics data on a MetabolIc Network.Bioinformatics (Oxford, England) · 2020Article
- Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.NPJ systems biology and applications · 2020Article
- Dynamic metabolic modelling of overproduced protein secretion in Streptomyces lividans using adaptive DFBA.BMC microbiology · 2019Article
- iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis.Current genomics · 2019Article
- On the effects of alternative optima in context-specific metabolic model predictions.PLoS computational biology · 2017Article
- Mapping the landscape of metabolic goals of a cell.Genome biology · 2016Article
- Reconstructed Metabolic Network Models Predict Flux-Level Metabolic Reprogramming in Glioblastoma.Frontiers in neuroscience · 2016Article
- Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis.BMC systems biology · 2015Article
- Controlled Measurement and Comparative Analysis of Cellular Components in E. coli Reveals Broad Regulatory Changes in Response to Glucose Starvation.PLoS computational biology · 2015Article
- Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.PLoS computational biology · 2015Article
- Applications of genome-scale metabolic network model in metabolic engineering.Journal of industrial microbiology & biotechnology · 2015Review
- Integration of metabolomics data into metabolic networks.Frontiers in plant science · 2015Review
- Mapping the inner workings of the microbiome: genomic- and metagenomic-based study of metabolism and metabolic interactions in the human microbiome.Cell metabolism · 2014Review
- Methods for integration of transcriptomic data in genome-scale metabolic models.Computational and structural biotechnology journal · 2014Review
- Emergent biosynthetic capacity in simple microbial communities.PLoS computational biology · 2014Article
- Variability of metabolite levels is linked to differential metabolic pathways in Arabidopsis's responses to abiotic stresses.PLoS computational biology · 2014Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such "history-dependent" sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques.
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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.