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Related Experiment Video

Updated: Oct 1, 2025

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline.

Puhua Niu¹, Maria J Soto², Byung-Jun Yoon^1,3

¹Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

|March 4, 2022

Summary

This summary is machine-generated.

This study introduces TRIMER, a computational tool for predicting metabolite yields. TRIMER integrates gene regulatory and metabolic networks to guide metabolic engineering strategies in microbes like E. coli and yeast.

Keywords:

Bioinformatics Cell Biology Gene Expression Metabolism Systems biology

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Last Updated: Oct 1, 2025

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Published on: July 11, 2014

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High-Throughput Metabolic Profiling for Model Refinements of Microalgae

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Published on: December 4, 2021

Area of Science:

Computational Biology
Metabolic Engineering
Systems Biology

Background:

Metabolic engineering relies on understanding gene regulation and metabolic flux.
Predicting the impact of genetic modifications, especially transcription factor (TF) deletions, is crucial for optimizing metabolite production.
Existing models often struggle to integrate transcriptional regulation with metabolic networks effectively.

Purpose of the Study:

To present a novel computational pipeline, TRIMER (Transcription Regulation Integrated with MEtabolic Regulation), for condition-dependent metabolite yield prediction.
To develop a hybrid model that integrates transcription factor (TF)-gene regulatory networks (TRNs) with Bayesian networks (BNs) for enhanced metabolic reaction regulation.
To provide a scalable method for predicting knockout phenotypes and metabolic fluxes in microbial systems.

Main Methods:

Developed TRIMER, a hybrid computational model combining TRNs and BNs.
Inferred BNs from transcriptomic expression data to model metabolic reactions.
Integrated TF-gene regulatory information with metabolic network models.
Applied the pipeline to genome-scale models of *E. coli* and yeast.

Main Results:

TRIMER accurately predicts condition-dependent metabolite yields.
The model successfully forecasts knockout phenotypes and metabolic fluxes resulting from TF deletions.
Demonstrated reliable predictions for both *E. coli* and yeast at the genome scale.
The hybrid approach effectively regulates metabolic reactions by integrating transcriptional and metabolic information.

Conclusions:

TRIMER offers a robust framework for metabolite yield prediction in metabolic engineering.
The integration of transcriptional regulation with metabolic networks is key to improving predictive accuracy.
This protocol facilitates the design of more effective genetic modifications for microbial cell factories.