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Bioenergetic Profile Experiment using C2C12 Myoblast Cells
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Model-based assessment of mammalian cell metabolic functionalities using omics data.

Anne Richelle1,2, Benjamin P Kellman2,3, Alexander T Wenzel3,4,5

  • 1Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA.

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Summary

Researchers can now link omics data to cell functions using a new framework. This tool predicts metabolic pathway usage from omics data, connecting molecular changes to cellular capabilities.

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Area of Science:

  • Systems biology
  • Metabolic engineering
  • Computational biology

Background:

  • Omics experiments generate vast datasets, but linking them to specific cell functions remains difficult due to complex biological interdependencies.
  • Understanding how gene, protein, and metabolite changes affect cellular functions is crucial for biological research.

Purpose of the Study:

  • To develop a computational framework for inferring changes in metabolic functions directly from omics data.
  • To provide a method for quantifying cellular metabolic capabilities across various biological scales.

Main Methods:

  • Curated and standardized lists of mammalian metabolic tasks.
  • Utilized genome-scale metabolic networks to define gene sets for each metabolic task.
  • Developed a computational approach to overlay omics data onto metabolic task gene sets and predict pathway usage.

Main Results:

  • Successfully demonstrated the framework's ability to quantify metabolic functions using transcriptomic data from diverse biological samples.
  • The approach allows for analysis from single-cell to whole-tissue and organ levels.
  • The framework is integrated into the GenePattern platform (CellFie) for accessibility.

Conclusions:

  • The presented framework enables researchers to bridge the gap between omics data and cellular metabolic functions.
  • This tool facilitates a deeper understanding of cellular processes and metabolic adaptations in various biological contexts.
  • The integration into GenePattern promotes wider adoption and application in biological research.