Become A Wiser, More Profitable Investor

✅   Generative Approaches to Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration

tech

Generative machine learning methods that use neural networks to parameterize large-scale and near genome-scale kinetic models have delivered significant efficiency gains in model construction, paving the way toward high-throughput dynamic metabolism studies for biomedical and biotechnological applications. Nevertheless, challenges remain in interpreting the outputs of generative neural networks and developing strategies to quickly adapt these networks to different organisms and physiological contexts without restarting the modeling process from scratch. Here, we present a systematic framework for repurposing generative neural networks trained on one physiological context to build large-scale kinetic models tailored for another context, thereby offering a new avenue for efficiently constructing models with targeted desired properties suitable for various physiological scenarios.

BioRxiv     Apr 06, 2025