Generative Approaches to Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration
Published in bioRxiv, 2025
We present a novel generative framework that leverages latent space exploration to generate dynamic metabolic models with targeted properties. This work introduces a new approach to controllably infer kinetic parameters in large-scale biological systems using pretrained neural network generators such as REKINDLE and RENAISSANCE.