What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data Article Swipe
Anne Richelle
,
Benjamin P. Kellman
,
Alexander T. Wenzel
,
Austin W.T. Chiang
,
Tyler Reagan
,
Jahir M. Gutierrez
,
Chintan Joshi
,
Shangzhong Li
,
Joanne K. Liu
,
Helen O. Masson
,
Jooyong Lee
,
Zerong Li
,
Laurent Heirendt
,
Christophe Trefois
,
Edwin F. Juarez
,
Tyler Bath
,
David Borland
,
Jill P. Mesirov
,
Kimberly Robasky
,
Nathan E. Lewis
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.04.26.057943
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1101/2020.04.26.057943
Large-scale omics experiments have become standard in biological studies, leading to a deluge of data. However, researchers still face the challenge of connecting changes in the omics data to changes in cell functions, due to the complex interdependencies between genes, proteins and metabolites. Here we present a novel framework that begins to overcome this problem by allowing users to infer how metabolic functions change, based on omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. We then used genome-scale metabolic networks to define gene modules responsible for each specific metabolic task. We further developed a framework to overlay omics data on these modules to predict pathway usage for each metabolic task. The proposed approach allows one to directly predict how changes in omics experiments change cell or tissue function. We further demonstrated how this new approach can be used to leverage the metabolic functions of biological entities from the single cell to their organization in tissues and organs using multiple transcriptomic datasets (human and mouse). Finally, we created a web-based CellFie module that has been integrated into the list of tools available in GenePattern ( www.genepattern.org ) to enable adoption of the approach.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2020.04.26.057943
- https://www.biorxiv.org/content/biorxiv/early/2020/04/28/2020.04.26.057943.full.pdf
- OA Status
- green
- Cited By
- 7
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3017800419
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3017800419Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2020.04.26.057943Digital Object Identifier
- Title
-
What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics dataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-28Full publication date if available
- Authors
-
Anne Richelle, Benjamin P. Kellman, Alexander T. Wenzel, Austin W.T. Chiang, Tyler Reagan, Jahir M. Gutierrez, Chintan Joshi, Shangzhong Li, Joanne K. Liu, Helen O. Masson, Jooyong Lee, Zerong Li, Laurent Heirendt, Christophe Trefois, Edwin F. Juarez, Tyler Bath, David Borland, Jill P. Mesirov, Kimberly Robasky, Nathan E. LewisList of authors in order
- Landing page
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https://doi.org/10.1101/2020.04.26.057943Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2020/04/28/2020.04.26.057943.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2020/04/28/2020.04.26.057943.full.pdfDirect OA link when available
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Leverage (statistics), Omics, Computer science, Computational biology, Task (project management), Function (biology), Systems biology, Biology, Bioinformatics, Machine learning, Engineering, Genetics, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2023: 1, 2022: 3, 2021: 1, 2020: 2Per-year citation counts (last 5 years)
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56Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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