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-gene regulatory network (TRN), which is modeled through a Bayesian network (BN). In this article, 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. 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.

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cover image Journal of Computational Biology
Journal of Computational Biology
Volume 30Issue Number 7July 2023
Pages: 751 - 765
PubMed: 36961389

History

Published online: 12 July 2023
Published in print: July 2023
Published ahead of print: 24 March 2023

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Affiliations

Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.
Maria J. Soto
US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Shuai Huang
Department of Industrial and Systems Engineering, University of Washington at Seattle, Seattle, Washington, USA.
Byung-jun Yoon
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.
Brookhaven National Laboratory, Computational Science Initiative, Upton, New York, USA.
Edward R. Dougherty
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.
Francis J. Alexander
Brookhaven National Laboratory, Computational Science Initiative, Upton, New York, USA.
Ian Blaby
US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Xiaoning Qian* [email protected]
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.
Brookhaven National Laboratory, Computational Science Initiative, Upton, New York, USA.

Notes

*
Address correspondence to: Dr. Xiaoning Qian, Texas A&M University, College Station, TX 77843, USA [email protected]

Authors' Contributions

Methodology, investigation, software, writing—original draft, and writing—review and editing by P.N. Investigation, validation, and writing—review and editing by M.J.S. Conceptualization, methodology, and writing—review and editing by S.H. Conceptualization, methodology, investigation, formal analysis, writing—review and editing, and funding acquisition by B.-J.Y. Conceptualization, investigation, formal analysis, writing—review and editing, and funding acquisition by E.R.D. Conceptualization, investigation, formal analysis, writing—review and editing, resources, and funding acquisition by F.J.A. Conceptualization, investigation, validation, writing—review and editing, funding acquisition, resources, and supervision by I.B. Conceptualization, methodology, investigation, formal analysis, writing—original draft, writing—review and editing, funding acquisition, resources, and supervision by X.Q.

Author Disclosure Statement

The authors declare they have no conflicting financial interests.

Funding Information

P.N. and X.Q. are partially supported by the National Science Foundation under grants CCF-1553281. This study has been supported by the DOE Joint Genome Institute (http://jgi.doe.gov) by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the U.S. Department of Energy. This presented material is based upon the study supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract number DE-0012704. Publication fees are supported by the U.S. Department of Energy, Office of Science, RadBio program, under Award KP1601011/FWP CC121.

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