Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis
Author(s) -
Mingyuan Tian,
Jennifer L. Reed
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty445
Subject(s) - flux balance analysis , constraint (computer aided design) , computer science , flux (metallurgy) , data mining , expression (computer science) , proteomics , computational biology , mathematics , chemistry , biology , biochemistry , geometry , organic chemistry , gene , programming language
Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models.
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