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Modeling metabolic variation with single-cell expression data

View ORCID ProfileYuanchao Zhang, Man S. Kim, Elizabeth Nguyen, View ORCID ProfileDeanne M. Taylor
doi: https://doi.org/10.1101/2020.01.28.923680
Yuanchao Zhang
Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19041, USADepartment of Genetics, Rutgers University, Piscataway, NJ 08854, USA
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Man S. Kim
Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19041, USA
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Elizabeth Nguyen
Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19041, USA
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Deanne M. Taylor
Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19041, USADepartment of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
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  • For correspondence: taylordm@email.chop.edu
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Abstract

Cellular metabolism encompasses the biochemical reactions and transportation of various metabolites in cells and their surroundings, which are integrated at all levels of cellular functions. We developed a method to systematically simulate cellular metabolism using single-cell RNA-seq (scRNA-seq) data through constraint-based context specific metabolic modeling. We simulated the NAD+ biosynthesis activity in 7 different mouse tissues, and the simulated NAD+ biosynthesis flux levels showed significant linear correlation with experimental measurements in previous research. We also show that the simulated NAD+ biosynthesis fluxes are reproducible using two additional scRNA-seq datasets.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 30, 2020.
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Modeling metabolic variation with single-cell expression data
Yuanchao Zhang, Man S. Kim, Elizabeth Nguyen, Deanne M. Taylor
bioRxiv 2020.01.28.923680; doi: https://doi.org/10.1101/2020.01.28.923680
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Modeling metabolic variation with single-cell expression data
Yuanchao Zhang, Man S. Kim, Elizabeth Nguyen, Deanne M. Taylor
bioRxiv 2020.01.28.923680; doi: https://doi.org/10.1101/2020.01.28.923680

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