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Updated: Sep 18, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Modeling host-pathway dynamics at the genome scale with machine learning.

Charlotte Merzbacher¹, Oisin Mac Aodha², Diego A Oyarzún³

¹School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.

Metabolic Engineering

|June 24, 2025

Summary

This summary is machine-generated.

This study introduces a new computational method combining kinetic and genome-scale metabolic models for predicting dynamic metabolic behavior in engineered microbes. This approach accelerates strain design for sustainable chemical production.

Keywords:

Genome-scale metabolic models Kinetic models Machine learning

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Area of Science:

Metabolic Engineering
Synthetic Biology
Computational Biology

Background:

Pathway engineering is key for sustainable chemical production.
Predicting dynamic metabolic changes in engineered hosts is challenging.
Current genome-scale models lack dynamic simulation capabilities.

Purpose of the Study:

To develop a novel computational framework integrating kinetic and genome-scale metabolic models.
To enable prediction of dynamic metabolite and enzyme behavior during fermentation.
To improve computational strain design for microbial production systems.

Main Methods:

Integrated kinetic pathway models with genome-scale metabolic models.
Employed surrogate machine learning models to accelerate Flux Balance Analysis (FBA) calculations.
Simulated local nonlinear dynamics informed by global host metabolic state.

Main Results:

Achieved simulation speed-ups of over two orders of magnitude.
Demonstrated consistent prediction of metabolite dynamics in Escherichia coli.
Successfully predicted effects of genetic perturbations and varying carbon sources.

Conclusions:

The integrated modeling approach provides a comprehensive framework for computational strain design.
The method facilitates screening of dynamic control circuits and optimization strategies.
This work bridges the gap between static and dynamic modeling in metabolic engineering.