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Title: Inferring secretory and metabolic pathway activity from omic data with secCellFie
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.  more » « less
Award ID(s):
2030039
PAR ID:
10518731
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ;
Corporate Creator(s):
Editor(s):
na
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Metabolic Engineering
Volume:
81
Issue:
C
ISSN:
1096-7176
Page Range / eLocation ID:
273 to 285
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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