{"dcterms:modified":"2022-03-17","dcterms:creator":"A Dataverse Instance","@type":"ore:ResourceMap","@id":"https://data.fz-juelich.de/api/datasets/export?exporter=OAI_ORE&persistentId=doi:10.26165/JUELICH-DATA/YXLFKJ","ore:describes":{"fzj:Institute":"IBG-1","Title":"Replication Data for: HOPS: high-performance library for (non-)uniform sampling of convex-constrained models","fzj:PoF III topic":"Innovative Synergisms (POF3-583)","fzj:PoF IV topic":"Acquisition before PoF IV (2021-2026) (POF4-898)","Author":[{"author:Name":"Johann F. Jadebeck","author:Affiliation":"Forschungszentrum Jülich GmbH","Identifier Scheme":"ORCID","Identifier":"0000-0002-5026-1546"},{"author:Name":"Axel Theorell","author:Affiliation":"Forschungszentrum Jülich GmbH"},{"author:Name":"Samuel Leweke","author:Affiliation":"Forschungszentrum Jülich GmbH"},{"author:Name":"Katharina Nöh","author:Affiliation":"Forschungszentrum Jülich GmbH","Identifier Scheme":"ORCID","Identifier":"0000-0002-5407-2275"}],"citation:Contact":[{"datasetContact:Name":"Johann F. Johann","datasetContact:Affiliation":"Forschungszentrum Jülich GmbH","datasetContact:E-mail":"j.jadebeck@fz-juelich.de"},{"datasetContact:Name":"Katharina Nöh","datasetContact:Affiliation":"Forschungszentrum Jülich GmbH","datasetContact:E-mail":"k.noeh@fz-juelich.de"}],"citation:Description":{"dsDescription:Text":"This collection contains showcased models and pre-processing results (determining an independent flux space and rounding) that are used as basis for benchmarking the uniform sampling performance of the CHHR implementations of the HOPS library [10.1093/bioinformatics/btaa872] (https://github.com/modsim/hops) and the COBRA toolbox [doi.org/10.1038/s41596-018-0098-2]. Here, the pre-processing was performed with the COBRA toolbox for all benchmarks to guarantee fair comparability. The models are split in two classes: 1) simplices with 64, 256, 512, 1024, and 2048 dimensions; 2) metabolic network models (e_coli_core, iAT_PLT_256, iJO1366, RECON1, Recon2, Recon3D_301) [http://bigg.ucsd.edu/], [doi.org/10.1038/nbt.2488] formulated in SBML format [doi.org/10.1093/bioinformatics/btg015]. \r\nFor each of these models, the left hand side of the constraint system (called <b>A</b>), the right hand side (called <b>b</b>), a shift of the transformation from sampling space, meaning the null space, to parameter space, meaning the full space of the model (called <b>p_shift</b>), the linear transformation from sampling space to parameter space (called <b>N</b>) and the Chebyshev center (called <b>start</b>) are provided.  These files come in <b> rounded </b> and <b> unrounded </b> types, indicating if the rounding algorithm has been applied.\r\nFor convenience, sometimes the rounding transformation is also given (indicated by  <b>T</b>). The rounding transformation is only given in the <b>rounded</b> form, because it is identity otherwise.\r\nAdditionally, the dataset contains an  <i>E. coli </i> WT model with carbon atom transitions and isotope labelling measurements [10.1038/nprot.2009.58] used in the Bayesian inference non-uniform sampling example. The <i>E.  coli</i> WT model is contained in a single FluxML file [doi.org/10.3389/fmicb.2019.01022]. To reproduce the data generated with the <i> E. coli WT </i> model, the rounding was calculated using the HOPS library and independent fluxes were provided by the high-performance simulator 13CFLUX2 [doi.org/10.1093/bioinformatics/bts646]."},"Subject":"Medicine, Health and Life Sciences","citation:Keyword":{"keyword:Term":"Metabolic Network Model HOPS Sampling MCMC Rounding"},"Related Publication":{"Citation":"Johann F Jadebeck, Axel Theorell, Samuel Leweke, Katharina Nöh, HOPS: high-performance library for (non-)uniform sampling of convex-constrained models, Bioinformatics, 2020, btaa872","ID Type":"doi","ID Number":"10.1093/bioinformatics/btaa872","URL":"https://doi.org/10.1093/bioinformatics/btaa872"},"citation:Depositor":"Jadebeck, Johann Fredrik","Deposit Date":"2020-10-27","@id":"doi:10.26165/JUELICH-DATA/YXLFKJ","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"1.2","schema:datePublished":"2020-12-18","schema:name":"Replication Data for: HOPS: high-performance 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