TRIMER: Transcription Regulation Integrated with Metabolic Regulation
Abstract
There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER's applicability to both simulated and experimental data and provide performance comparison with other existing approaches.
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)
- OSTI Identifier:
- 1824873
- Alternate Identifier(s):
- OSTI ID: 1839206; OSTI ID: 1896670
- Report Number(s):
- BNL-222578-2022-JAAM
Journal ID: ISSN 2589-0042; S258900422101186X; 103218; PII: S258900422101186X
- Grant/Contract Number:
- 0012704; SC0012704; CCF-1553281; AC02-05CH11231
- Resource Type:
- Published Article
- Journal Name:
- iScience
- Additional Journal Information:
- Journal Name: iScience Journal Volume: 24 Journal Issue: 11; Journal ID: ISSN 2589-0042
- Publisher:
- Elsevier
- Country of Publication:
- Netherlands
- Language:
- English
- Subject:
- 09 BIOMASS FUELS; bioinformatics; metabolomics; transcriptomics
Citation Formats
Niu, Puhua, Soto, Maria J., Yoon, Byung-Jun, Dougherty, Edward R., Alexander, Francis J., Blaby, Ian, and Qian, Xiaoning. TRIMER: Transcription Regulation Integrated with Metabolic Regulation. Netherlands: N. p., 2021.
Web. doi:10.1016/j.isci.2021.103218.
Niu, Puhua, Soto, Maria J., Yoon, Byung-Jun, Dougherty, Edward R., Alexander, Francis J., Blaby, Ian, & Qian, Xiaoning. TRIMER: Transcription Regulation Integrated with Metabolic Regulation. Netherlands. https://doi.org/10.1016/j.isci.2021.103218
Niu, Puhua, Soto, Maria J., Yoon, Byung-Jun, Dougherty, Edward R., Alexander, Francis J., Blaby, Ian, and Qian, Xiaoning. Mon .
"TRIMER: Transcription Regulation Integrated with Metabolic Regulation". Netherlands. https://doi.org/10.1016/j.isci.2021.103218.
@article{osti_1824873,
title = {TRIMER: Transcription Regulation Integrated with Metabolic Regulation},
author = {Niu, Puhua and Soto, Maria J. and Yoon, Byung-Jun and Dougherty, Edward R. and Alexander, Francis J. and Blaby, Ian and Qian, Xiaoning},
abstractNote = {There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER's applicability to both simulated and experimental data and provide performance comparison with other existing approaches.},
doi = {10.1016/j.isci.2021.103218},
journal = {iScience},
number = 11,
volume = 24,
place = {Netherlands},
year = {2021},
month = {11}
}
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