Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

PLoS Computational Biology
Zhuo WangNathan D Price

Abstract

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid...Continue Reading

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Citations

Aug 15, 2018·Environmental Microbiology·Anne E OtwellNitin S Baliga
Mar 9, 2019·PLoS Computational Biology·Fangzhou ShenZhuo Wang
Apr 12, 2019·PLoS Computational Biology·Anna S Blazier, Jason A Papin
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Methods Mentioned

BETA
gene knockout
dissection
PCR

Software Mentioned

GX
hybrid
IDREAM
FBA
ARACNE
MTA
Inferelator
MADE
CoreReg
EXAMO

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