Inferring secretory and metabolic pathway activity from omic data with secCellFie

Helen O Masson, Mojtaba Samoudi, Caressa M Robinson, Chih-Chung Kuo, Linus Weiss, Km Shams Ud Doha, Alex Campos, Vijay Tejwani, Hussain Dahodwala, Patrice Menard, Bjorn G Voldborg, Bradley Robasky, Susan T Sharfstein, Nathan E Lewis

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.
Original languageEnglish
JournalMetabolic Engineering
Volume81
Pages (from-to)273-285
ISSN1096-7176
DOIs
Publication statusPublished - 2024

Keywords

  • Computational biology
  • Genome-scale model
  • Metabolism
  • Omic data
  • Secretory pathway
  • Systems biology

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