{"entity": "researcher", "timestamp": "2026-05-09T16:48:50.228Z", "family": "Feltus", "given": "F Alex", "initials": "FA", "orcid": "0000-0002-2123-6114", "affiliations": ["Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.", "Biomedical Data Science and Informatics Program, Clemson University, Clemson, SC, USA.", "Center for Human Genetics, Clemson University, Clemson, SC, USA."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/bf88745ea761439c8a75ecc853e881cb.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/bf88745ea761439c8a75ecc853e881cb"}}, "publications": [{"entity": "publication", "iuid": "7e5001475dcb4a998445d222b9eab1c6", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/7e5001475dcb4a998445d222b9eab1c6.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/7e5001475dcb4a998445d222b9eab1c6"}}, "title": "A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling.", "authors": [{"family": "Erdem", "given": "Cemal", "initials": "C", "orcid": "0000-0003-3663-3646", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f71b5af589264614a52eefa8baba3132.json"}}, {"family": "Mutsuddy", "given": "Arnab", "initials": "A"}, {"family": "Bensman", "given": "Ethan M", "initials": "EM"}, {"family": "Dodd", "given": "William B", "initials": "WB", "orcid": "0000-0001-7568-4732", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4ee5a4fc7c8f407c8c1697a1d0b898d1.json"}}, {"family": "Saint-Antoine", "given": "Michael M", "initials": "MM"}, {"family": "Bouhaddou", "given": "Mehdi", "initials": "M"}, {"family": "Blake", "given": "Robert C", "initials": "RC"}, {"family": "Gross", "given": "Sean M", "initials": "SM"}, {"family": "Heiser", "given": "Laura M", "initials": "LM", "orcid": "0000-0003-3330-0950", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5ce0d380df324afcbbaa1971aa8472cc.json"}}, {"family": "Feltus", "given": "F Alex", "initials": "FA", "orcid": "0000-0002-2123-6114", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bf88745ea761439c8a75ecc853e881cb.json"}}, {"family": "Birtwistle", "given": "Marc R", "initials": "MR"}], "type": "journal article", "published": "2022-06-21", "journal": {"title": "Nat Commun", "issn": "2041-1723", "volume": "13", "issue": "1", "pages": "3555", "issn-l": "2041-1723"}, "abstract": "Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFN\u03b3 pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFN\u03b3 acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFN\u03b3-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.", "doi": "10.1038/s41467-022-31138-1", "pmid": "35729113", "labels": {"DDLS Fellow": null, "Cemal Erdem": null}, "xrefs": [{"db": "pmc", "key": "PMC9213456"}, {"db": "pii", "key": "10.1038/s41467-022-31138-1"}, {"db": "figshare", "key": "10.6084/m9.figshare.19658802.v1"}], "notes": [], "created": "2023-10-27T08:53:41.726Z", "modified": "2023-10-27T08:53:41.979Z"}]}