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McSNAC: A software to approximate first-order signaling networks from mass cytometry data

摘要Modeling how cells transfer a signal from an extracellular stimulus to different compartments within the cell is critical to understand how different stimuli result in different cellular responses. We designed a software (McSNAC) that proposes a minimal mathematical model to describe biochemical signaling events learning from time-stamped cytometry data. This software makes linear approximations which hold true in many scenarios, but can also break down under several conditions. We explore this approximation in depth to provide guidelines regarding its applicability. In addition, we provide a user interface for non-technical users to analyze their time-stamped cytometry data with this approach.

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作者 Darren Wethington [1] Sayak Mukherjee [2] Jayajit Das [3] 学术成果认领
作者单位 Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, Ohio 43205, United States;Biomedical Sciences Graduate Program, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States [1] Battelle Memorial Institute, Columbus, Ohio 43201, United States [2] Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, Ohio 43205, United States;Biomedical Sciences Graduate Program, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States;Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States;Pelotonia Institute for Immuno-Oncology, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States;Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio 43210, United States;Biophysics Graduate Program, The Ohio State University, Columbus, Ohio 43210, United States [3]
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DOI 10.15302/J-QB-022-0308
发布时间 2024-07-26(万方平台首次上网日期,不代表论文的发表时间)
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