Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction
Abstract
TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a Transcription Factor (TF)-gene regulatory network (TRN), which is modeled via a Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. Here we have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.
- Authors:
-
[1];
[4]
- Texas A & M Univ., College Station, TX (United States)
- USDOE Joint Genome Institute (JGI), Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. of Washington, Seattle, WA (United States)
- Texas A & M Univ., College Station, TX (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)
- OSTI Identifier:
- 1968825
- Report Number(s):
- BNL-224194-2023-JAAM
Journal ID: ISSN 1557-8666
- Grant/Contract Number:
- SC0012704; CCF-1553281; AC02- 05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of computational biology (Online)
- Additional Journal Information:
- Journal Name: Journal of computational biology (Online); Journal Volume: 30; Journal Issue: 7; Journal ID: ISSN 1557-8666
- Publisher:
- Mary Ann Liebert, Inc. Publishers
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES; Bayesian network structure learning; metabolic engineering; optimal experimental design; regulated metabolic network modeling; uncertainty quantification
Citation Formats
Niu, Puhua, Soto, Maria J., Huang, Shuai, Yoon, Byung-jun, Dougherty, Edward R., Alexander, Francis J., Blaby, Ian, and Qian, Xiaoning. Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction. United States: N. p., 2023.
Web. doi:10.1089/cmb.2022.0368.
Niu, Puhua, Soto, Maria J., Huang, Shuai, Yoon, Byung-jun, Dougherty, Edward R., Alexander, Francis J., Blaby, Ian, & Qian, Xiaoning. Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction. United States. https://doi.org/10.1089/cmb.2022.0368
Niu, Puhua, Soto, Maria J., Huang, Shuai, Yoon, Byung-jun, Dougherty, Edward R., Alexander, Francis J., Blaby, Ian, and Qian, Xiaoning. Fri .
"Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction". United States. https://doi.org/10.1089/cmb.2022.0368. https://www.osti.gov/servlets/purl/1968825.
@article{osti_1968825,
title = {Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction},
author = {Niu, Puhua and Soto, Maria J. and Huang, Shuai and Yoon, Byung-jun and Dougherty, Edward R. and Alexander, Francis J. and Blaby, Ian and Qian, Xiaoning},
abstractNote = {TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a Transcription Factor (TF)-gene regulatory network (TRN), which is modeled via a Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. Here we have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.},
doi = {10.1089/cmb.2022.0368},
journal = {Journal of computational biology (Online)},
number = 7,
volume = 30,
place = {United States},
year = {Fri Mar 24 00:00:00 EDT 2023},
month = {Fri Mar 24 00:00:00 EDT 2023}
}
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