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278 results for “metabolic modeling”
Proccessed data for Trend Validation of Metabolic Models Against Measurements Using Indirect Calorimetry
<p>A cleaned data set used to validate metabolism models in a muscuskeletal modeling software.<br> The dataset contains 240 rows and 18 columns. </p> <p>Labels:</p> <ul> <li>AnyMet = Metabolic output by the modelling software. Calculated as the mean energy cost per repetition [J] .</li> <li>VynMet = Metabolic output by the indirect calorimetry system (Vyntus CPX). Calculated as the mean energy cost per repetition [J].</li> <li>rest_energy = total energy cost during rest [J]. Measured with Indirect caliometry</li> <li>rest_time = total time of rest [min]</li> <li>Work = Energy cost times the displacement per rep [J].</li> <li>watt = Work divided by total duration of a repetition [J/s]</li> <li>extension time = duration of the extension part of the movement [s]</li> <li>flexion time = duration of the flexion part of the movement [s]</li> <li>bw = bodyweight [kg]</li> <li>height [m]</li> <li>CV = coefficient of variation for the measured rest_energy. </li> <li>model = model type used for AnyMet. </li> <li>Subject </li> <li>Contraction = Contraction type performed</li> <li>intensity = Intensity to overcome created by the dynamometer. </li> <li>mech_watt_kg = mechcanical watt, watt divided by bodyweight</li> <li>any_met_watt_kg = watt pr kg: (AnyMet / bw) / (extension time + flexion time)</li> <li>vyn_met_watt_kg = watt pr kg: (VynMet / bw) / (extension time + flexion time)<br> <br> There is also a zip file containing the raw data from the dynanometer and the Vyntus PGE system.</li> </ul>
single-cell RNAseq data (data set 1) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset1) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CRC samples downloaded from the GEO website (<strong>GSE81861). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
The impact of metabolic plasticity on winter energy use models
Open the record for dataset details and reuse information.
The zebra mussel (Dreissena polymorpha) as a model organism for ecotoxicological studies: a prior 1H NMR spectrum interpretation of a whole body extract for metabolism monitoring.
<p>NMR data of the zebra mussel <em>Dreissena polymorpha</em> whole body polar extract metabolome</p> <p>- 1D <sup>1</sup>H annotated spectrum - 600 MHz</p> <p>- 2D <sup>1</sup>H-<sup>1</sup>H JRES spectrum - 600 MHz</p> <p>- 2D<sup>1</sup>H-<sup>1</sup>H COSY spectrum - 600 MHz</p> <p>- 2D<sup>1</sup>H-<sup>1</sup>H TOCSY spectrum - 600 MHz</p> <p>- 2D<sup>1</sup>H-<sup>13</sup>C HSQC spectrum - 600 MHz</p> <p>- 2D<sup>1</sup>H-<sup>13</sup>C HSQC spectrum - 800 MHz</p> <p>- 2D<sup>1</sup>H-<sup>31</sup>P HSQC spectrum - 800 MHz</p> <p>- <sup>1</sup>H annotated spectrum description tables (.xlsx)</p> <p>- Instructions for data visualization in Topspin</p>
Additions to AGORA2 made for Shaaban et al, "Personalized modeling of gut microbiome metabolism throughout the first year of life"
<p>This dataset contains expansions made to AGORA2 (https://www.nature.com/articles/s41587-022-01628-0) and published in Shaaban et al, "Personalized modeling of gut microbiome metabolism throughout the first year of life", in press.</p> <p>Included are:</p> <ul> <li>289 additional genome-scale reconstructions built for genomes not included in AGORA2</li> <li>250 genome-scale reconstructions from AGORA2 endapnded with human milk oligosaccharide degradation pathways</li> </ul> <p>In this version, slight updates have been made to the 289 additional genome-scale reconstructions based on additional experimental data.</p>
Data: Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD
<p>This archive contains all scripts, resource data and results, including intermediate results to reproduce the results for "Metabolic modeling reveals a multi-level deregulation of host-microbiome metabolic networks in IBD". </p>
Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies
<p>The pcYeast7.6 model and files, required to reproduce the figures, provided in the publication "Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies", accepted in <em>Nat Commun</em>. The Zenodo upload was created by Pranas Grigaitis, p.grigaitis [at] vu.nl.</p> <p>Abstract</p> <p>When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation - known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimization, providing a framework to understand metabolic adaptation in eukaryal cells.</p>
Strain specific genome scale metabolic models for 1011 Saccharomyces cerevisiae
<p>This is generated strain-specific genome scale metabolic models for 1011 S.cerevisiae. This is linked with the paper: Lu, H. et al. <em>A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.</em> Nature Communications 10, 3586 (2019). <a href="https://doi.org/10.1038/s41467-019-11581-3">doi:10.1038/s41467-019-11581-3</a></p>
Comprehensive Context-specific Genome-scale Metabolic Models for Mus Musculus
<p>Comprehensive Context-specific Genome-scale Metabolic Models for Mus Musculus. The data consists of 28 models for the combination 2 mouse strains (WT and Ob/Ob), 2 diets (WT and HFD) and 7 tissues (Aorta, Heart, Liver, Skeletal Muscle, Hippocampus, Hypothalamus and Epididymal fat).</p>
Dataset related to article "'Understanding fibrosis pathogenesis via modelling macrophage-fibroblast interplay in immune-metabolic context"
<p>This record contains raw data related to article “ 'Understanding fibrosis pathogenesis via modelling macrophage-fibroblast interplay in immune-metabolic context"</p> <p>Abstract not avaible at the moment</p>
Dataset - Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models
<p><strong>Simulation data needed to reproduce the results from the manuscript “Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models”</strong><br> by V. Pandey, N. Hadadi and V. Hatzimanikatis</p> <p>"REMI manuscript - simData" folder contains all simulation data which can be used to generate results of the paper: <br> • Expression_data: This folder contains Transcriptomics data from both studies: Ishii et al (see test_expr.mat) and Holm et al.<br> • Fluxdata: Fluxomics data can be found form the studies Ishii et al and Holm et al.<br> • Metabolomics: This contains metabolomics data of aforementioned both studies.<br> • ModelsSolutions: We generated different models using with thermodynamics (TGex, TGexM, TM) and without thermodynamics models (Gex, GexM, M). Gex indicates integration with only gene expression, GexM indicates gene expression and metabolite, and M indicates only metabolites. ‘T’ is used for thermodynamic models. Models for different mutants and conditions (e.g. pgm, pgi) can be found in the corresponding folders (TGex, TGexM, TM, Gex, GexM, and M). Variables with the ‘store’ tag comprises flux solutions, correlation values and percentage error between simulation and experiment fluxes.<br> • AlternativeMCS: We generated alternative states for MCS and saved results.<br> • FVAMM: This is the result flux variability analysis can be found in this folder.<br> • Scatter_plot: Scatter plots indicates correlation between measured and model predicted fluxes.</p> <p> </p>
Metabolic Derangement in Polycystic Kidney Disease Mouse Models Is Ameliorated by Mitochondrial-Targeted Antioxidants
<p>Autosomal dominant polycystic kidney disease (ADPKD) is characterized by progressively enlarging cysts. Here we elucidate the interplay between oxidative stress, mitochondrial dysfunction, and metabolic derangement using two mouse models of PKD1 mutation, PKD1RC/null and PKD1RC/RC. Mouse kidneys with PKD1 mutation have decreased mitochondrial complexes activity. Targeted proteomics analysis shows a significant decrease in proteins involved in the TCA cycle, fatty acid oxidation (FAO), respiratory complexes, and endogenous antioxidants. Overexpressing mitochondrial-targeted catalase (mCAT) using adeno-associated virus reduces mitochondrial ROS, oxidative damage, ameliorates the progression of PKD and partially restores expression of proteins involved in FAO and the TCA cycle. In human ADPKD cells, inducing mitochondrial ROS increased ERK1/2 phosphorylation and decreased AMPK phosphorylation, whereas the converse was observed with increased scavenging of ROS in the mitochondria. Treatment with the mitochondrial protective peptide, SS31, recapitulates the beneficial effects of mCAT, supporting its potential application as a novel therapeutic for ADPKD.</p>
single-cell RNAseq data (data set 20) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset20) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from breast cancer samples downloaded from the GEO website (GSE180286)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </p>
single-cell RNAseq data (data set 18) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset18) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from Liver cancer set 1 samples downloaded from the GEO website (GSE125449)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 17) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset17 was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from PBMC metastatic MCC samples downloaded from the GEO website (GSE117988)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 12) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset12) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor10 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 16) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset16) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CD4 T-cells in PACA samples downloaded from the GEO website (GSE156728)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </p>
single-cell RNAseq data (data set 11) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset11) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor9 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 14) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset14) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor12 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 9) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset9) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor7 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.