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150 results for “metabolic networks”
Metabolism dataset: one year of high-frequency temperature, dissolved oxygen, wind, photosynthetically active radiation observations and low-frequency nutrient data for 58 lakes in the Global Lake Ecological Observatory Network
Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We compiled data from 58 diverse and globally distributed and mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP and ER derived from those data. In addition, the data package includes median in-lake and stream concentrations of dissolved organic carbon and total phosphorus for a subset of 18 of those lakes.
Compartment and Hub Definitions Tune Metabolic Networks for Metabolomic Interpretations
<p>This archive contains data for a report by the same title.<br> Data relate to software projects MetaboNet and DyMetaboNet.<br> MetaboNet: https://github.com/tcameronwaller/metabonet<br> DyMetaboNet: https://github.com/tcameronwaller/dymetabonet</p> <p>File descriptions</p> <p>dymetabonet_2019-08-29.mp4 ... raw screen capture video of DyMetaboNet<br> dock_metabonet_2019-08-18.zip ... complete MetaboNet export<br> model_* ... curation of human metabolic model by MetaboNet<br> model_dymetabonet.zip ... format for DyMetaboNet<br> model_compartments* ... compartments<br> model_processes* ... processes<br> model_reactions* ... reactions<br> model_metabolites* ... metabolites<br> measurement_* ... curation of metabolomic measurements by MetaboNet<br> measurement_study_*_report.tsv ... summary of match measurements to metabolites<br> measurement_study_*.tsv ... metabolites' fold changes and probabilities between groups<br> measurement_study_*_metaboanalyst.txt ... format for MetaboAnalyst<br> measurement_study_*_metaboanalyst_pair.txt ... format for MetaboAnalyst with sample pairs<br> network_* ... multiple definitions of metabolic networks<br> network_compartments-true_hubs-true.zip ... compartmental network with hubs<br> network_compartments-true_hubs-false.zip ... compartmental network without hubs<br> network_compartments-false_hubs-true.zip ... noncompartmental network with hubs<br> network_compartments-false_hubs-false.zip ... noncompartmental network without hubs<br> network_compartments-*_hubs_*/network_cytoscape.json ... format for Cytoscape<br> network_compartments-*_hubs_*/network_networkx.pickle ... format for NetworkX<br> network_compartments-*_hubs_*/nodes_reactions.pickle ... network's nodes for reactions<br> network_compartments-*_hubs_*/nodes_metabolites.pickle ... network's nodes for metabolites<br> network_compartments-*_hubs_*/links.pickle ... network's links<br> network_compartments-*_hubs_*/analysis/nodes_reactions.tsv ... nodes' metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/nodes_metabolites.tsv ... nodes' metrics relative to metabolites<br> network_compartments-*_hubs_*/analysis/network_reactions.tsv ... network's metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/network_metabolites.tsv ... network's metrics relative to metabolites<br> network_compartments-*_hubs_*/measurement/metabolites.tsv ... measurements on nodes for metabolites</p>
Supplementary material for "Learning Boolean controls in regulated metabolic networks: a case-study"
<p>This record contains notebooks and Docker image for reproducing the learning of Boolean controls in regulated metabolic networks, and the case study presented in the CMSB 2021 conference proceeding article "Learning Boolean controls in regulated metabolic networks: a case-study".</p> <p>Visualize notebooks online:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-Simulations.ipynb">CaseStudy-Simulations.ipynb</a></li> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-SearchSpace.ipynb">CaseStudy-SearchSpace.ipynb</a></li> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-Inference.ipynb">CaseStudy-Inference.ipynb</a></li> </ul> <p>Notebooks can be executed interactively within the Docker image <code>bioasp/boolean-caspo-flux:cmsb2021 </code>which extends the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> version <code>2021-02-01.</code></p> <p>Alternatively, they can be executed without any installation at <a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.5070151/">https://mybinder.org/v2/zenodo/10.5281/zenodo.5070151/</a>.</p> <p>The Docker image can be executed as follows:</p> <pre><code class="language-bash">docker pull bioasp/boolean-caspo-flux:cmsb2021 docker run -it --rm -p 8888:8888 bioasp/boolean-caspo-flux:cmsb2021 </code></pre> <p>then point your browser to <a href="http://127.0.0.1:8888">http://127.0.0.1:8888</a>.</p> <p>The image can be imported using the command <code>docker load</code> with the image file provided in this record:</p> <pre><code>docker load -i image.tar.gz</code></pre> <p>or with the <code>donodo</code> command available at <a href="https://github.com/pauleve/donodo">https://github.com/pauleve/donodo</a>:</p> <pre><code>pip install -U donodo donodo pull 10.5281/zenodo.5070151</code></pre>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, main part
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <p><strong>Datasets:</strong></p> <ul> <li> <strong>models.zip </strong>- Datasets parameterizing kinetic nonlinear models of a wild-type <em>E. coli </em>strain used for training generative adversarial networks <ul> <li>subfolder 1: kinetic - contains the kinetic model (kin_varma_curated.yml)</li> <li>subfolder 2: thermo - contains the thermodynamic model for all the four physiologies (varma_fdp1, varma_fdp2, varma_fdp3, varma_fdp4)</li> <li>subfolder 3: steady_state_samples: contains the TFA steady state profiles for all four physiologies (samples_fdp1, sample_fdp2, samples_fdp3, samples_fdp4)</li> <li>subfolder 4: parameters - contains the kinetic parameter training dataset for each physiology (.hdf5 files), maximal eigenvalues (training labels) (maximal_eigenvalues.csv) and the minimum eigenvalues (minimal_eigenvalues.csv)</li> </ul> </li> <li><strong>vanilla_learning_training.zip:</strong> contains 4 folders for each of the 4 physiologies. <ul> <li>each of these folders contains 6 subsubfolders in the format N-<em>{n} </em>( N-10, N-50, N-100, N-500, N-1000, N-72000), where <em>{n} </em>represents the number of used training data samples.</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy (Note: eigenvalues were not calculated for N=10, 50, 100 as traning failed)/</li> </ul> </li> </ul> </li> <li><strong>transfer_learning_training.zip</strong> - contains 12 subfolders "tl_fdpi_fdpj" where i,j ={1,2,3,4} for each of the 12 transfer learning case <ul> <li>each of these folders contains 5 subsubfolders N-10, N-50, N-100, N-500, N-1000</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy </li> </ul> </li> </ul> </li> </ul> <ul> <li><strong>best_generators.zip</strong> <ul> <li>The best generators (with the highest incidence of relevant models) for each physiology (generator1- 4.h5)</li> <li>The normalizing scaling parameters for each generator (d_scaling.pkl).</li> <li> </li> </ul> </li> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>vanilla_ODE_sample_parameters.zip</strong> - contains (i) 1000 REKINDLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total) (ii) 1000 ORACLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total). These parameter sets parameterize the ODEs which are integrated.</li> <li><strong>ode_solutions_physiology1.zip (available at </strong><a href="https://zenodo.org/record/5818192">https://zenodo.org/record/5818192</a><strong>) - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li><strong>ode_solutions_physiology1_ORACLE.zip (available at </strong><a href="https://zenodo.org/record/5819669">https://zenodo.org/record/5819669</a><strong>) - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li><strong>ode_solutions_physiologies2-4.zip -</strong> contains 6 subfolders (physiology_2-4, physiology_2-4_ORACLE), with each subfolder containing 10 sub subfolders. Each sub subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE / ORACLE generated parameter sets for physiology 2-4, each of the 1000 models having a random perturbation.</li> <li><strong>transfer_learning_ODE_solutions.zip - </strong>contains two subfolders N_10, N_50, each subfolder contains 12 subsubfolders titled i_j (where i = {1,2,3,4} and j = {1,2,3,4} where 1_2 represent the transfer learning case from physiology 2 to physiology 1 and when using <em>{n}</em> samples from physiology 2 and so on (where <em>{n}</em>=10 and 50 respectively). Each subsubfolders contain <ul> <li>i_j.hdf5: contains 300 kinetic parameter sets generated using (i) REKINDLE for this transfer learning case</li> <li>i_j.csv: the maximal eigenvalues of the parameter sets</li> <li>solutions.csv: ODE integrated time series data for the relevant kinetic parameters out of the 300 generated.</li> </ul> </li> </ul> </li> </ul> <p> </p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 2
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <ul> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>ode_solutions_physiology1_ORACLE.zip</strong> - contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> </ul> </li> </ul> <p>The detailed instructions and the main body of the dataset is available here: <a href="https://zenodo.org/record/5803120">https://zenodo.org/record/5803120</a></p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 1
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <ul> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>ode_solutions_physiology1.zip - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> </ul> </li> </ul> <p>The detailed instructions and the main body of the dataset is available here: <a href="https://zenodo.org/record/5803120">https://zenodo.org/record/5803120</a></p>
Data from: Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations
Open the record for dataset details and reuse information.
Data from: Reconfiguration of functional brain networks and metabolic cost converge during task performance
<p>The ability to solve cognitive tasks depends upon adaptive changes in the organization of whole-brain functional networks. However, the link between task-induced network reconfigurations and their underlying energy demands is poorly understood. We address this by multimodal network analyses integrating functional and molecular neuroimaging acquired concurrently during a complex cognitive task. Task engagement elicited a marked increase in the association between glucose consumption and functional brain network reorganization. This convergence between metabolic and neural processes was specific to feedforward connections linking the visual and dorsal attention networks, in accordance with task requirements of visuo-spatial reasoning. Further increases in cognitive load above initial task engagement did not affect the relationship between metabolism and network reorganization but only modulated existing interactions. Our findings show how the upregulation of key computational mechanisms to support cognitive performance unveils the complex, interdependent changes in neural metabolism and neuro-vascular responses.</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>
Data from: Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks
<p><span>External tasks evoke characteristic fMRI BOLD signal deactivations in the default mode network (DMN). However, for the corresponding metabolic glucose demands both decreases and increases have been reported. To resolve this discrepancy, functional PET/MRI data from 50 healthy subjects performing Tetris® were combined with previously published data sets of working memory, visual and motor stimulation. We show that the glucose metabolism of the posteromedial DMN is dependent on the metabolic demands of the correspondingly engaged task-positive networks. Specifically, the dorsal attention and frontoparietal network shape the glucose metabolism of the posteromedial DMN in opposing directions. While tasks that mainly require an external focus of attention lead to a consistent downregulation of both metabolism and the BOLD signal in the posteromedial DMN, cognitive control during working memory requires a metabolically expensive BOLD suppression. This indicates that two types of BOLD deactivations with different oxygen-to-glucose index may occur in this region. We further speculate that consistent downregulation of the two signals is mediated by decreased glutamate signaling, while divergence may be subject to active GABAergic inhibition. The results demonstrate that the DMN relates to cognitive processing in a flexible manner and does not always act as a cohesive task-negative network in isolation.</span></p>
Data from Genome scale metabolic network modelling for metabolic profile predictions
<p>Data used to produce figures 4, 5 and 6 in the paper Genome scale metabolic network modelling for metabolic profile predictions.</p>
The Effect of Pramipexole on Metabolic Network Activity Compared With Levodopa in Early Parkinson's Disease
ClinicalTrials.gov study NCT01470859. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Data from: Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks
Open the record for dataset details and reuse information.
Data from: Reconfiguration of functional brain networks and metabolic cost converge during task performance
Open the record for dataset details and reuse information.
Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction
<p>We include samples of various data types used in the work "Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction"</p> <p>More information about the software package and instructions are provided in <a href="https://github.com/hallamlab/pathway2vec">hallamlab/pathway2vec</a></p>
Large-scale metabolic interaction network of the mouse and human gut microbiota
<p>The role of our gut microbiota in health and disease is largely attributed to the collective metabolic activities of the inhabitant microbes. A system-level framework of the microbial community structure, mediated through metabolite transport, would provide important insights into the complex microbe-microbe and host-microbe chemical interactions. This framework, if adaptable to both mouse and human systems, would be useful for mechanistic interpretations of the vast amounts of experimental data from gut microbiomes in murine animal models, whether humanized or not. Here, we constructed a literature-curated, interspecies network of the mammalian gut microbiota for mouse and human hosts, called NJC19. This network is an extensive data resource, encompassing 838 microbial species (766 bacteria, 53 archaea, and 19 eukaryotes) and 6 host cell types, interacting through 8,224 small-molecule transport and macromolecule degradation events. Moreover, we compiled 912 negative associations between organisms and metabolic compounds that are not transportable or degradable by those organisms. Our network may facilitate experimental and computational endeavors for the mechanistic investigations of host-associated microbial communities.</p>
DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks
<p>Matlab output files and raw data used for the evaluation presented in "DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks" <a href="https://github.com/MetExplore/dexom">https://github.com/MetExplore/dexom</a></p>
Fig. 3. Network modules for suberin-associated metabolism genes. Expression profiles for 317 in Transcriptomic analysis of wound-healing in Solanum tuberosum (potato) tubers: Evidence for a stepwise induction of suberin-associated genes
Fig. 3. Network modules for suberin-associated metabolism genes. Expression profiles for 317 wound-induced and suberin-associated genes encompassing primary carbohydrate metabolism and the formation of suberin phenolic and aliphatic monomers were subjected to WGCNA. Genes belonging to carbohydrate (C), tricarboxylic acid cycle (TCA), shikimate pathway (S), phenolic metabolism (P), phenolic assembly (PA), fatty acid biosynthesis (FAB), fatty acid modification (FAM) and aliphatic assembly (AA) are colour-coded (see legend). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Metabolic networks paper CoCoMiCo
<p>root.tgz contains all metabolic network from root microbiota (10.1038/nature16192)</p> <p>leaf.tgz contains all metabolic network from the leaf microbiota (10.1038/nature16192)</p> <p>soil.tgz contains all metabolic network from the RefSoil database (10.1038/ismej.2016.168)</p> <p>gut.tgz contains all metabolic network from the gut microbiota (10.1038/s41587-018-0008-8)</p>
Fig. 1 in Establishing the metabolic network of isoquinoline alkaloids from the Macleaya genus
Fig. 1. Diagram of systematic screening of isoquinoline alkaloids by HPLC-Q-TOF-MS combined with a screening method. (a) X, Y, Z, M, and N represent the number of OCH2O, OCH3, OH, glucose, and H groups, respectively. (b) The number of substituent groups connected to the target skeleton is no more than four.
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.