Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast
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-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
- OSTI Identifier:
- 1361007
- Alternate Identifier(s):
- OSTI ID: 1357712; OSTI ID: 1629583
- Grant/Contract Number:
- AR0000426; AC02-05CH11231
- Resource Type:
- Published Article
- Journal Name:
- PLoS Computational Biology (Online)
- Additional Journal Information:
- Journal Name: PLoS Computational Biology (Online) Journal Volume: 13 Journal Issue: 5; Journal ID: ISSN 1553-7358
- Publisher:
- Public Library of Science (PLoS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Wang, Zhuo, Danziger, Samuel A., Heavner, Benjamin D., Ma, Shuyi, Smith, Jennifer J., Li, Song, Herricks, Thurston, Simeonidis, Evangelos, Baliga, Nitin S., Aitchison, John D., Price, Nathan D., and Nielsen, ed., Jens. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast. United States: N. p., 2017.
Web. doi:10.1371/journal.pcbi.1005489.
Wang, Zhuo, Danziger, Samuel A., Heavner, Benjamin D., Ma, Shuyi, Smith, Jennifer J., Li, Song, Herricks, Thurston, Simeonidis, Evangelos, Baliga, Nitin S., Aitchison, John D., Price, Nathan D., & Nielsen, ed., Jens. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast. United States. https://doi.org/10.1371/journal.pcbi.1005489
Wang, Zhuo, Danziger, Samuel A., Heavner, Benjamin D., Ma, Shuyi, Smith, Jennifer J., Li, Song, Herricks, Thurston, Simeonidis, Evangelos, Baliga, Nitin S., Aitchison, John D., Price, Nathan D., and Nielsen, ed., Jens. Wed .
"Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast". United States. https://doi.org/10.1371/journal.pcbi.1005489.
@article{osti_1361007,
title = {Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast},
author = {Wang, Zhuo and Danziger, Samuel A. and Heavner, Benjamin D. and Ma, Shuyi and Smith, Jennifer J. and Li, Song and Herricks, Thurston and Simeonidis, Evangelos and Baliga, Nitin S. and Aitchison, John D. and Price, Nathan D. and Nielsen, ed., Jens},
abstractNote = {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-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.},
doi = {10.1371/journal.pcbi.1005489},
journal = {PLoS Computational Biology (Online)},
number = 5,
volume = 13,
place = {United States},
year = {Wed May 17 00:00:00 EDT 2017},
month = {Wed May 17 00:00:00 EDT 2017}
}
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