{"entity": "researcher", "timestamp": "2026-06-09T02:58:20.112Z", "family": "Robinson", "given": "Jonathan L", "initials": "JL", "orcid": "0000-0001-8567-5960", "affiliations": [], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/b70b6d9b64fd45e882c4108aded013d4.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/b70b6d9b64fd45e882c4108aded013d4"}}, "publications": [{"entity": "publication", "iuid": "c173cd34f0f54b89b74230c577a2fe82", "links": {"self": {"href": "https://publications.scilifelab.se/publication/c173cd34f0f54b89b74230c577a2fe82.json"}, "display": {"href": "https://publications.scilifelab.se/publication/c173cd34f0f54b89b74230c577a2fe82"}}, "title": "Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data.", "authors": [{"family": "Gustafsson", "given": "Johan", "initials": "J", "orcid": "0000-0001-5072-2659", "researcher": {"href": "https://publications.scilifelab.se/researcher/bd5fda1ac79e49c185ba6f4dfcdff5fc.json"}}, {"family": "Anton", "given": "Mihail", "initials": "M", "orcid": "0000-0002-7753-9042", "researcher": {"href": "https://publications.scilifelab.se/researcher/4a28ecc2261e436ea5884ada5e512aed.json"}}, {"family": "Roshanzamir", "given": "Fariba", "initials": "F"}, {"family": "J\u00f6rnsten", "given": "Rebecka", "initials": "R"}, {"family": "Kerkhoven", "given": "Eduard J", "initials": "EJ", "orcid": "0000-0002-3593-5792", "researcher": {"href": "https://publications.scilifelab.se/researcher/0df361f8014144e79479631fcbffad53.json"}}, {"family": "Robinson", "given": "Jonathan L", "initials": "JL", "orcid": "0000-0001-8567-5960", "researcher": {"href": "https://publications.scilifelab.se/researcher/b70b6d9b64fd45e882c4108aded013d4.json"}}, {"family": "Nielsen", "given": "Jens", "initials": "J", "orcid": "0000-0002-9955-6003", "researcher": {"href": "https://publications.scilifelab.se/researcher/7a596e289be4438a8a2653b1f25fea8b.json"}}], "type": "journal article", "published": "2023-02-07", "journal": {"title": "Proc. Natl. Acad. Sci. U.S.A.", "issn": "1091-6490", "issn-l": "0027-8424", "volume": "120", "issue": "6", "pages": "e2217868120"}, "abstract": "Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.", "doi": "10.1073/pnas.2217868120", "pmid": "36719923", "labels": {"Systems Biology": "Collaborative", "Bioinformatics Support, Infrastructure and Training": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pmc", "key": "PMC9963017"}], "notes": [], "created": "2023-05-17T11:26:19.662Z", "modified": "2023-05-17T11:34:38.605Z"}, {"entity": "publication", "iuid": "fb677af8d3b74d6f9a0db4cc6ec6f173", "links": {"self": {"href": "https://publications.scilifelab.se/publication/fb677af8d3b74d6f9a0db4cc6ec6f173.json"}, "display": {"href": "https://publications.scilifelab.se/publication/fb677af8d3b74d6f9a0db4cc6ec6f173"}}, "title": "Enhanced metabolism and negative regulation of ER stress support higher erythropoietin production in HEK293 cells", "authors": [{"family": "Saghaleyni", "given": "Rasool", "initials": "R", "orcid": "0000-0003-0956-039X", "researcher": {"href": "https://publications.scilifelab.se/researcher/ebd08b713a894a6986d9101453f5ecd9.json"}}, {"family": "Malm", "given": "Magdalena", "initials": "M", "orcid": "0000-0003-1763-9073", "researcher": {"href": "https://publications.scilifelab.se/researcher/8c4c6c276dd64fe2abe27da888cd0925.json"}}, {"family": "Moruzzi", "given": "Noah", "initials": "N"}, {"family": "Zrimec", "given": "Jan", "initials": "J", "orcid": "0000-0002-7099-961X", "researcher": {"href": "https://publications.scilifelab.se/researcher/811851bb112d414a9bbb9d4e50add5e8.json"}}, {"family": "Razavi", "given": "Ronia", "initials": "R"}, {"family": "Wistbacka", "given": "Num", "initials": "N"}, {"family": "Thorell", "given": "Hannes", "initials": "H"}, {"family": "Pintar", "given": "Anton", "initials": "A"}, {"family": "Hober", "given": "Andreas", "initials": "A"}, {"family": "Edfors", "given": "Fredrik", "initials": "F"}, {"family": "Chotteau", "given": "Veronique", "initials": "V"}, {"family": "Berggren", "given": "Per Olof", "initials": "PO"}, {"family": "Grassi", "given": "Luigi", "initials": "L", "orcid": "0000-0002-6308-7540", "researcher": {"href": "https://publications.scilifelab.se/researcher/c006b891dedd483e8f01756f73fd6fb0.json"}}, {"family": "Zelezniak", "given": "Aleksej", "initials": "A", "orcid": "0000-0002-3098-9441", "researcher": {"href": "https://publications.scilifelab.se/researcher/4328a7ff130a44cc90e5282e4a18a2d7.json"}}, {"family": "Svensson", "given": "Thomas", "initials": "T", "orcid": "0000-0002-9190-2979", "researcher": {"href": "https://publications.scilifelab.se/researcher/dc636683ece84dc4ac3e4d10df0c7a49.json"}}, {"family": "Hatton", "given": "Diane", "initials": "D"}, {"family": "Nielsen", "given": "Jens", "initials": "J", "orcid": "0000-0002-9955-6003", "researcher": {"href": "https://publications.scilifelab.se/researcher/7a596e289be4438a8a2653b1f25fea8b.json"}}, {"family": "Robinson", "given": "Jonathan L", "initials": "JL", "orcid": "0000-0001-8567-5960", "researcher": {"href": "https://publications.scilifelab.se/researcher/b70b6d9b64fd45e882c4108aded013d4.json"}}, {"family": "Rockberg", "given": "Johan", "initials": "J"}], "type": "journal-article", "published": "2022-06-00", "journal": {"title": "Cell Rep", "issn": "2211-1247", "issn-l": null, "volume": "39", "issue": "11", "pages": "110936"}, "abstract": null, "doi": "10.1016/j.celrep.2022.110936", "pmid": null, "labels": {"Systems Biology": "Collaborative", "Bioinformatics Support, Infrastructure and Training": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2020-12-10T11:24:40.993Z", "modified": "2023-08-03T04:21:13.841Z"}, {"entity": "publication", "iuid": "cef437ec472748d99c04192947b80bd4", "links": {"self": {"href": "https://publications.scilifelab.se/publication/cef437ec472748d99c04192947b80bd4.json"}, "display": {"href": "https://publications.scilifelab.se/publication/cef437ec472748d99c04192947b80bd4"}}, "title": "Genome-scale metabolic network reconstruction of model animals as a platform for translational research.", "authors": [{"family": "Wang", "given": "Hao", "initials": "H", "orcid": "0000-0001-7475-0136", "researcher": {"href": "https://publications.scilifelab.se/researcher/836b4fbf7ebd4f80abc84465c8f29a2e.json"}}, {"family": "Robinson", "given": "Jonathan L", "initials": "JL", "orcid": "0000-0001-8567-5960", "researcher": {"href": "https://publications.scilifelab.se/researcher/b70b6d9b64fd45e882c4108aded013d4.json"}}, {"family": "Kocabas", "given": "Pinar", "initials": "P", "orcid": "0000-0001-9788-2019", "researcher": {"href": "https://publications.scilifelab.se/researcher/c89eb03e619945a2a2058179b0d0e310.json"}}, {"family": "Gustafsson", "given": "Johan", "initials": "J", "orcid": "0000-0001-5072-2659", "researcher": {"href": "https://publications.scilifelab.se/researcher/bd5fda1ac79e49c185ba6f4dfcdff5fc.json"}}, {"family": "Anton", "given": "Mihail", "initials": "M", "orcid": "0000-0002-7753-9042", "researcher": {"href": "https://publications.scilifelab.se/researcher/4a28ecc2261e436ea5884ada5e512aed.json"}}, {"family": "Cholley", "given": "Pierre-Etienne", "initials": "PE"}, {"family": "Huang", "given": "Shan", "initials": "S"}, {"family": "Gobom", "given": "Johan", "initials": "J"}, {"family": "Svensson", "given": "Thomas", "initials": "T", "orcid": "0000-0002-9190-2979", "researcher": {"href": "https://publications.scilifelab.se/researcher/dc636683ece84dc4ac3e4d10df0c7a49.json"}}, {"family": "Uhlen", "given": "Mattias", "initials": "M", "orcid": "0000-0002-4858-8056", "researcher": {"href": "https://publications.scilifelab.se/researcher/ff81da3cb0cf4262873b993a1b06798c.json"}}, {"family": "Zetterberg", "given": "Henrik", "initials": "H", "orcid": "0000-0003-3930-4354", "researcher": {"href": "https://publications.scilifelab.se/researcher/85efee74eb4a4b38b63cf2823d204529.json"}}, {"family": "Nielsen", "given": "Jens", "initials": "J", "orcid": "0000-0002-9955-6003", "researcher": {"href": "https://publications.scilifelab.se/researcher/7a596e289be4438a8a2653b1f25fea8b.json"}}], "type": "journal article", "published": "2021-07-27", "journal": {"title": "Proc. Natl. Acad. Sci. U.S.A.", "issn": "1091-6490", "volume": "118", "issue": "30", "pages": "e2102344118", "issn-l": "0027-8424"}, "abstract": "Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer's disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.", "doi": "10.1073/pnas.2102344118", "pmid": "34282017", "labels": {"Bioinformatics Support, Infrastructure and Training": "Collaborative", "Systems Biology": "Technology development", "Bioinformatics Support for Computational Resources": "Service", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "2102344118"}, {"db": "pmc", "key": "PMC8325244"}], "notes": [], "created": "2021-08-19T08:56:58.316Z", "modified": "2024-01-16T13:48:39.079Z"}, {"entity": "publication", "iuid": "661c201d37c949e7a6db545f120153a2", "links": {"self": {"href": "https://publications.scilifelab.se/publication/661c201d37c949e7a6db545f120153a2.json"}, "display": {"href": "https://publications.scilifelab.se/publication/661c201d37c949e7a6db545f120153a2"}}, "title": "Single-cell BCR and transcriptome analysis after influenza infection reveals spatiotemporal dynamics of antigen-specific B cells", "authors": [{"family": "Mathew", "given": "Nimitha R", "initials": "NR"}, {"family": "Jayanthan", "given": "Jayalal K", "initials": "JK"}, {"family": "Smirnov", "given": "Ilya V", "initials": "IV"}, {"family": "Robinson", "given": "Jonathan L", "initials": "JL", "orcid": "0000-0001-8567-5960", "researcher": {"href": "https://publications.scilifelab.se/researcher/b70b6d9b64fd45e882c4108aded013d4.json"}}, {"family": "Axelsson", "given": "Hannes", "initials": "H", "orcid": "0000-0003-2365-1749", "researcher": {"href": "https://publications.scilifelab.se/researcher/63b88c4d11c443f39121c6d93fcff1f0.json"}}, {"family": "Nakka", "given": "Sravya S", "initials": "SS"}, {"family": "Emmanouilidi", "given": "Aikaterini", "initials": "A", "orcid": "0000-0001-9431-6900", "researcher": {"href": "https://publications.scilifelab.se/researcher/6c06a1851eea457199282fc3544563ef.json"}}, {"family": "Czarnewski", "given": "Paulo", "initials": "P", "orcid": "0000-0001-8150-4021", "researcher": {"href": "https://publications.scilifelab.se/researcher/b84309de4e3946159c374ffa6d977560.json"}}, {"family": "Yewdell", "given": "William T", "initials": "WT"}, {"family": "Sch\u00f6n", "given": "Karin", "initials": "K"}, {"family": "Lebrero-Fern\u00e1ndez", "given": "Cristina", "initials": "C"}, {"family": "Bernasconi", "given": "Valentina", "initials": "V"}, {"family": "Rodin", "given": "William", "initials": "W"}, {"family": "Harandi", "given": "Ali M", "initials": "AM"}, {"family": "Lycke", "given": "Nils", "initials": "N", "orcid": "0000-0003-1155-4861", "researcher": {"href": "https://publications.scilifelab.se/researcher/cc624dbe15d24b8a90ad8d8673ffd390.json"}}, {"family": "Borcherding", "given": "Nicholas", "initials": "N"}, {"family": "Yewdell", "given": "Jonathan W", "initials": "JW"}, {"family": "Greiff", "given": "Victor", "initials": "V", "orcid": "0000-0003-2622-5032", "researcher": {"href": "https://publications.scilifelab.se/researcher/1545e9cb92db4ccc93c1b2dc398e1777.json"}}, {"family": "Bemark", "given": "Mats", "initials": "M", "orcid": "0000-0001-7416-9819", "researcher": {"href": "https://publications.scilifelab.se/researcher/7a7ee472e2e0453b98ee9fdfc02de2b3.json"}}, {"family": "Angeletti", "given": "Davide", "initials": "D", "orcid": "0000-0002-5256-1972", "researcher": {"href": "https://publications.scilifelab.se/researcher/ae59c12bf82b4ad9a8d9ad8603d03d9c.json"}}], "type": "journal-article", "published": "2021-06-00", "journal": {"title": "Cell Rep", "issn": "2211-1247", "issn-l": null, "volume": "35", "issue": "12", "pages": "109286"}, "abstract": "B cell responses are critical for antiviral immunity. However, a comprehensive picture of antigen-specific B cell differentiation, clonal proliferation, and dynamics in different organs after infection is lacking. Here, by combining single-cell RNA and B cell receptor (BCR) sequencing of antigen-specific cells in lymph nodes, spleen, and lungs after influenza infection in mice, we identify several germinal center (GC) B cell subpopulations and organ-specific differences that persist over the course of the response. We discover transcriptional differences between memory cells in lungs and lymphoid organs and organ-restricted clonal expansion. Remarkably, we find significant clonal overlap between GC-derived memory and plasma cells. By combining BCR-mutational analyses with monoclonal antibody (mAb) expression and affinity measurements, we find that memory B cells are highly diverse and can be selected from both low- and high-affinity precursors. By linking antigen recognition with transcriptional programming, clonal proliferation, and differentiation, these finding provide important advances in our understanding of antiviral immunity.", "doi": "10.1016/j.celrep.2021.109286", "pmid": "34161770", "labels": {"Bioinformatics Long-term Support WABI": "Collaborative", "Bioinformatics Support, Infrastructure and Training": "Collaborative", "Systems Biology": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "mid", "key": "EMS146188"}, {"db": "pmc", "key": "PMC7612943"}, {"db": "pii", "key": "S2211-1247(21)00657-4"}], "notes": [], "created": "2021-06-24T07:56:29.627Z", "modified": "2023-06-19T11:26:37.988Z"}, {"entity": "publication", "iuid": "ed41e1c9dfb3440fb7b5e40627afbbf6", "links": {"self": {"href": "https://publications.scilifelab.se/publication/ed41e1c9dfb3440fb7b5e40627afbbf6.json"}, "display": {"href": "https://publications.scilifelab.se/publication/ed41e1c9dfb3440fb7b5e40627afbbf6"}}, "title": "Machine learning-based investigation of the cancer protein secretory pathway.", "authors": [{"family": "Saghaleyni", "given": "Rasool", "initials": "R", "orcid": "0000-0003-0956-039X", "researcher": {"href": "https://publications.scilifelab.se/researcher/ebd08b713a894a6986d9101453f5ecd9.json"}}, {"family": "Sheikh Muhammad", "given": "Azam", "initials": "A", "orcid": "0000-0001-6037-7019", "researcher": {"href": "https://publications.scilifelab.se/researcher/a456bb9e432b4882814638c9019acbd2.json"}}, {"family": "Bangalore", "given": "Pramod", "initials": "P", "orcid": "0000-0002-5308-7061", "researcher": {"href": "https://publications.scilifelab.se/researcher/62e6651ffc4d4675bcec21d3754ad7b3.json"}}, {"family": "Nielsen", "given": "Jens", "initials": "J", "orcid": "0000-0002-9955-6003", "researcher": {"href": "https://publications.scilifelab.se/researcher/7a596e289be4438a8a2653b1f25fea8b.json"}}, {"family": "Robinson", "given": "Jonathan L", "initials": "JL", "orcid": "0000-0001-8567-5960", "researcher": {"href": "https://publications.scilifelab.se/researcher/b70b6d9b64fd45e882c4108aded013d4.json"}}], "type": "journal article", "published": "2021-04-00", "journal": {"title": "PLoS Comput. Biol.", "issn": "1553-7358", "volume": "17", "issue": "4", "pages": "e1008898", "issn-l": "1553-734X"}, "abstract": "Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is often studied due to its potential as a reservoir of tumor biomarkers. However, there has been less focus on the protein components of the secretory machinery itself. We therefore investigated the expression changes in secretory pathway components across many different cancer types. Specifically, we implemented a dual approach involving differential expression analysis and machine learning to identify PSP genes whose expression was associated with key tumor characteristics: mutation of p53, cancer status, and tumor stage. Eight different machine learning algorithms were included in the analysis to enable comparison between methods and to focus on signals that were robust to algorithm type. The machine learning approach was validated by identifying PSP genes known to be regulated by p53, and even outperformed the differential expression analysis approach. Among the different analysis methods and cancer types, the kinesin family members KIF20A and KIF23 were consistently among the top genes associated with malignant transformation or tumor stage. However, unlike most cancer types which exhibited elevated KIF20A expression that remained relatively constant across tumor stages, renal carcinomas displayed a more gradual increase that continued with increasing disease severity. Collectively, our study demonstrates the complementary nature of a combined differential expression and machine learning approach for analyzing gene expression data, and highlights key PSP components relevant to features of tumor pathophysiology that may constitute potential therapeutic targets.", "doi": "10.1371/journal.pcbi.1008898", "pmid": "33819271", "labels": {"Bioinformatics Support, Infrastructure and Training": "Collaborative", "Systems Biology": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "PCOMPBIOL-D-20-01453"}, {"db": "pmc", "key": "PMC8049480"}], "notes": [], "created": "2021-08-30T07:32:34.516Z", "modified": "2021-11-10T12:26:03.440Z"}, {"entity": "publication", "iuid": "36235e9dc3c244d7a5646615de05fe18", "links": {"self": {"href": "https://publications.scilifelab.se/publication/36235e9dc3c244d7a5646615de05fe18.json"}, "display": {"href": "https://publications.scilifelab.se/publication/36235e9dc3c244d7a5646615de05fe18"}}, "title": "An atlas of human metabolism.", "authors": [{"family": "Robinson", "given": "Jonathan L", "initials": "JL", "orcid": "0000-0001-8567-5960", "researcher": {"href": "https://publications.scilifelab.se/researcher/b70b6d9b64fd45e882c4108aded013d4.json"}}, {"family": "Kocaba\u015f", "given": "P\u0131nar", "initials": "P", "orcid": "0000-0001-9788-2019", "researcher": {"href": "https://publications.scilifelab.se/researcher/c89eb03e619945a2a2058179b0d0e310.json"}}, {"family": "Wang", "given": "Hao", "initials": "H", "orcid": "0000-0001-7475-0136", "researcher": {"href": "https://publications.scilifelab.se/researcher/836b4fbf7ebd4f80abc84465c8f29a2e.json"}}, {"family": "Cholley", "given": "Pierre-Etienne", "initials": "PE"}, {"family": "Cook", "given": "Daniel", "initials": "D", "orcid": "0000-0001-5534-8600", "researcher": {"href": "https://publications.scilifelab.se/researcher/ad0b42774a1e4e0580dde05e95fcb1fc.json"}}, {"family": "Nilsson", "given": "Avlant", "initials": "A", "orcid": "0000-0002-9476-4516", "researcher": {"href": "https://publications.scilifelab.se/researcher/44da161dba604c9e803a4af303277083.json"}}, {"family": "Anton", "given": "Mihail", "initials": "M", "orcid": "0000-0002-7753-9042", "researcher": {"href": "https://publications.scilifelab.se/researcher/4a28ecc2261e436ea5884ada5e512aed.json"}}, {"family": "Ferreira", "given": "Raphael", "initials": "R", "orcid": "0000-0001-9881-6232", "researcher": {"href": "https://publications.scilifelab.se/researcher/5e97f22759cd4e008d9b6473f52865e5.json"}}, {"family": "Domenzain", "given": "Iv\u00e1n", "initials": "I", "orcid": "0000-0002-5322-2040", "researcher": {"href": "https://publications.scilifelab.se/researcher/3793e87625584ee2a31301297263a12a.json"}}, {"family": "Billa", "given": "Virinchi", "initials": "V"}, {"family": "Limeta", "given": "Angelo", "initials": "A"}, {"family": "Hedin", "given": "Alex", "initials": "A", "orcid": "0000-0002-0829-2496", "researcher": {"href": "https://publications.scilifelab.se/researcher/88756d4d3b894ab288141af7b9c9b052.json"}}, {"family": "Gustafsson", "given": "Johan", "initials": "J", "orcid": "0000-0001-5072-2659", "researcher": {"href": "https://publications.scilifelab.se/researcher/bd5fda1ac79e49c185ba6f4dfcdff5fc.json"}}, {"family": "Kerkhoven", "given": "Eduard J", "initials": "EJ", "orcid": "0000-0002-3593-5792", "researcher": {"href": "https://publications.scilifelab.se/researcher/0df361f8014144e79479631fcbffad53.json"}}, {"family": "Svensson", "given": "L Thomas", "initials": "LT", "orcid": "0000-0002-9190-2979", "researcher": {"href": "https://publications.scilifelab.se/researcher/dc636683ece84dc4ac3e4d10df0c7a49.json"}}, {"family": "Palsson", "given": "Bernhard O", "initials": "BO", "orcid": "0000-0003-2357-6785", "researcher": {"href": "https://publications.scilifelab.se/researcher/b72eed29485a433cb85e260ee38dc894.json"}}, {"family": "Mardinoglu", "given": "Adil", "initials": "A", "orcid": "0000-0002-4254-6090", "researcher": {"href": "https://publications.scilifelab.se/researcher/da756265658c4ed2a8911644583e07a3.json"}}, {"family": "Hansson", "given": "Lena", "initials": "L"}, {"family": "Uhl\u00e9n", "given": "Mathias", "initials": "M", "orcid": "0000-0002-4858-8056", "researcher": {"href": "https://publications.scilifelab.se/researcher/ff81da3cb0cf4262873b993a1b06798c.json"}}, {"family": "Nielsen", "given": "Jens", "initials": "J", "orcid": "0000-0002-9955-6003", "researcher": {"href": "https://publications.scilifelab.se/researcher/7a596e289be4438a8a2653b1f25fea8b.json"}}], "type": "journal article", "published": "2020-03-24", "journal": {"title": "Sci Signal", "issn": "1937-9145", "issn-l": "1945-0877", "volume": "13", "issue": "624", "pages": null}, "abstract": "Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.", "doi": "10.1126/scisignal.aaz1482", "pmid": "32209698", "labels": {"Systems Biology": "Technology development", "Bioinformatics Support, Infrastructure and Training": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pii", "key": "13/624/eaaz1482"}, {"db": "pmc", "key": "PMC7331181"}, {"db": "mid", "key": "NIHMS1590510"}], "notes": [], "created": "2020-12-10T11:11:44.807Z", "modified": "2021-11-10T12:52:52.282Z"}]}