ArticlePLoS computational biology2020
StanDep: Capturing transcriptomic variability improves context-specific metabolic models.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- An anaerobic pathogen rewires host metabolism to fuel oxidative growth in the inflamed gut.Cell · 2026Article
- A community reconstruction of Chinese hamster metabolism and structural systems biology elucidate metabolic rewiring in lactate-free CHO cells.Cell systems · 2026Article
- Rational engineering of facultative anaerobiosis enables commensal survival in the oxygenated gut.bioRxiv : the preprint server for biology · 2026Article
- Systematic data-driven genome-scale metabolic model reduction for bioprocess modeling: CHO culture case study.NPJ systems biology and applications · 2026Article
- Evaluating transcriptomic integration for cyanobacterial constraint-based metabolic modelling.Frontiers in bioinformatics · 2026Article
- Modelling reliable metabolic phenotypes by analysing the context-specific transcriptomics data.NPJ systems biology and applications · 2025Article
- Metatranscriptomics-based metabolic modeling of patient-specific urinary microbiome during infection.NPJ biofilms and microbiomes · 2025Article
- ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions.iScience · 2025Article
- Metabolic modeling elucidates phenformin and atpenin A5 as broad-spectrum antiviral drugs against RNA viruses.Communications biology · 2025Article
- Metabolic modelling reveals the aging-associated decline of host-microbiome metabolic interactions in mice.Nature microbiology · 2025Article
- Investigating intestinal epithelium metabolic dysfunction in celiac disease using personalized genome-scale models.BMC medicine · 2025Article
- SL-scan identifies synthetic lethal interactions in cancer using metabolic networks.Scientific reports · 2023Article
- Extracting functionally accurate context-specific models of Atlantic salmon metabolism.NPJ systems biology and applications · 2023Article
- Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.NPJ systems biology and applications · 2023Article
- Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures.Metabolites · 2023Review
- Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data.Metabolic engineering · 2023Article
- A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale.PLoS computational biology · 2022Article
- Correction: StanDep: Capturing transcriptomic variability improves context-specific metabolic models.PLoS computational biology · 2022Article
- Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models.Computational and structural biotechnology journal · 2022Article
- Path to improving the life cycle and quality of genome-scale models of metabolism.Cell systems · 2021Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at https://github.com/LewisLabUCSD/StanDep.
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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.