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Updated: Aug 5, 2025

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Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction.

Puhua Niu1, Maria J Soto2, Shuai Huang3

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|March 24, 2023
PubMed
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<b>Title:</b> DAP-Seq Reveals Cluster-Situated Regulator Control of Numerous <i>Streptomyces</i> Natural Product Biosynthetic Genes.

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This study introduces TRIMER, a metabolic engineering tool that integrates gene regulatory networks with metabolic models. Sensitivity analysis of TRIMER enhances metabolic flux predictions and guides experimental design for improved metabolite yields.

Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Genome-scale modeling is crucial for metabolic engineering.
  • Integrating transcription factor-gene regulatory networks (TRN) with metabolic models improves prediction accuracy.
  • Bayesian networks (BN) are used to model TRNs, but their structural uncertainty impacts downstream predictions.

Purpose of the Study:

  • To perform sensitivity analysis on metabolic flux predictions within the TRIMER pipeline.
  • To quantify the uncertainty arising from Bayesian network structures used for TRN modeling.
  • To guide optimal experimental design (OED) for enhancing TRN models and metabolic engineering outcomes.

Main Methods:

  • Developed a computational strategy to construct an uncertainty class of TRN models based on regulatory order uncertainty from transcriptomic data.
Keywords:
Bayesian network structure learningmetabolic engineeringoptimal experimental designregulated metabolic network modelinguncertainty quantification

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  • Applied sensitivity analysis to the TRIMER pipeline to assess the impact of TRN model uncertainty on metabolite yields.
  • Utilized simulated experiments to validate the sensitivity analysis strategy for Bayesian network structure learning.

    • Main Results:

    • The proposed strategy effectively quantifies the importance of regulatory network edges in reducing metabolic flux prediction uncertainty.
    • Sensitivity analysis results demonstrate the potential to guide OED for improving TRN modeling.
    • Simulated experiments confirmed the effectiveness of the approach in enhancing metabolic engineering objectives.

    Conclusions:

    • Sensitivity analysis of TRIMER provides a robust method for uncertainty quantification in TRN-integrated metabolic models.
    • This approach effectively guides experimental design to improve metabolic engineering strategies.
    • The findings facilitate more accurate predictions and targeted modifications for desired metabolite production.