Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration

2026-04-20 · Source: Machine learning : nature.com subject feeds · Field: Science & Research — Life Sciences & Biology, Artificial Intelligence & Machine Learning · Depth: Expert, long

Summary

A new latent-space exploration framework has been developed to address the challenge of scarce enzyme kinetic parameters in building large-scale dynamic metabolic models. Published on April 20, 2026, this framework repurposes a trained generative neural network to produce models with targeted dynamics in new regimes without requiring additional training. The approach was demonstrated in *Escherichia coli*, where latent inputs were used to tune aerobic response speed, identify rate-limiting enzymes, and retarget the generative network to anaerobic dynamics. The framework was also successfully extended to *Saccharomyces cerevisiae*, showing robust control over metabolic dynamics across various training stages and diverse latent inputs. This method transforms latent variables into practical control knobs for kinetic model behavior, aiming to accelerate cell-factory design and enable personalized metabolic modeling.

Key takeaway

For AI Scientists and Research Scientists developing metabolic models, this framework offers a significant advantage by allowing the adaptation of existing generative networks to new physiological states without costly retraining. You can leverage latent variables as direct control knobs to fine-tune metabolic dynamics, accelerating the design of cell factories and advancing personalized metabolic modeling. Consider integrating this latent-space exploration into your model development workflow to enhance flexibility and efficiency.

Key insights

Latent space exploration enables repurposing trained generative networks for targeted metabolic dynamics without retraining.

Principles

• Generative networks can rapidly parameterize kinetic models.
• Latent variables can serve as control knobs for model behavior.

Method

The method involves repurposing a trained generative neural network to explore its latent space, allowing for the generation of kinetic models with targeted dynamics in new physiological states without additional training.

In practice

• Tune aerobic response speed in *E. coli*.
• Identify rate-limiting enzymes.
• Retarget models to anaerobic dynamics.

Topics

• Kinetic Parameter Inference
• Metabolic Networks
• Generative Neural Networks
• Latent Space Exploration
• Escherichia coli

Code references

• realLCSB/LaTeX
• EPFL-LCSB/renaissance
• EPFL-LCSB/rekindle
• EPFL-LCSB/skimpy
• EPFL-LCSB/rekindle

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine learning : nature.com subject feeds.