Bioprocess Modeling Made Easy With Hybrax
- 1University of Vienna
Abstract
Hybrid models that embed neural networks in ordinary differential equations are increasingly popular in bioprocess modeling, but they are challenging to set up. Much of the effort goes into the code around the model: wet-lab data must be imported and pre-processed, feeds and sampling must be accounted for in the balance equations, and a training loop with cross-validation must be set up. Researchers usually write this code from scratch for each project. In addition, no widely adopted format for bioprocess data exists, and datasets are mostly published in bespoke spreadsheets or not at all. As a result, models and datasets from the literature are difficult to reproduce, reuse, or compare.We present Hybrax, an open-source Python framework that addresses these problems. It combines a flexible format for bioprocess data with a customizable end-to-end training pipeline for mechanistic and hybrid models. Hybrax derives the transport terms in the process dynamics automatically and provides the utilities needed for training, so that researchers can focus on developing the model itself. It also facilitates model sharing and reproducibility: each training checkpoint contains the model parameters, code, configuration, and training data required to run the model or retrain it from scratch. We demonstrate Hybrax by re-implementing two models from the literature, one trained on real-world E. coli data and one on synthetic HEK293 data, with performance comparable to the original models. Hybrax is available at https://github.com/hybrax/hybrax.
Related
- A novel route for valerolactam production in Corynebacterium glutamicum: metabolic engineering and bioprocess optimization
- Thermal stress-based pathway engineering and bioprocess optimization of Corynebacterium glutamicum for de novo hydroxyectoine production
- Bioprocess optimization of
- Is level-1 blob reconstruction under the network multispecies coalescent easy?
- Algal Biomass and Algal-Derived Preparations in Livestock Nutrition: Gut Microbiota Modulation, Host Molecular Pathways, and Bioprocess Innovations.
BibTeX
@misc{libiselleregger2026bioprocess,
title = {{Bioprocess Modeling Made Easy With Hybrax}},
author = {J. Libiseller-Egger and M. Gotsmy and G. Guillen-Gosalbez and J. Zanghellini},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.05.764695},
url = {https://www.biorxiv.org/content/10.64898/2026.11.05.764695v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Bioprocess Modeling Made Easy With Hybrax
AU - J. Libiseller-Egger
AU - M. Gotsmy
AU - G. Guillen-Gosalbez
AU - J. Zanghellini
PY - 2026
DA - 2026/11/05
PB - bioRxiv
DO - 10.64898/2026.11.05.764695
UR - https://www.biorxiv.org/content/10.64898/2026.11.05.764695v1
AB - Hybrid models that embed neural networks in ordinary differential equations are increasingly popular in bioprocess modeling, but they are challenging to set up. Much of the effort goes into the code around the model: wet-lab data must be imported and pre-processed, feeds and sampling must be accounted for in the balance equations, and a training loop with cross-validation must be set up. Researchers usually write this code from scratch for each project. In addition, no widely adopted format for bioprocess data exists, and datasets are mostly published in bespoke spreadsheets or not at all. As a result, models and datasets from the literature are difficult to reproduce, reuse, or compare.We present Hybrax, an open-source Python framework that addresses these problems. It combines a flexible format for bioprocess data with a customizable end-to-end training pipeline for mechanistic and hybrid models. Hybrax derives the transport terms in the process dynamics automatically and provides the utilities needed for training, so that researchers can focus on developing the model itself. It also facilitates model sharing and reproducibility: each training checkpoint contains the model parameters, code, configuration, and training data required to run the model or retrain it from scratch. We demonstrate Hybrax by re-implementing two models from the literature, one trained on real-world E. coli data and one on synthetic HEK293 data, with performance comparable to the original models. Hybrax is available at https://github.com/hybrax/hybrax.
ER -