# Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation

arXiv: https://arxiv.org/abs/2410.18864 · HTML: https://mlsanity.com/paper/2410.18864/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Reinforcement Learning for Batch Bioprocess Optimization](https://mlsanity.com/paper/1904.07292/) (2019)
- [Stochastic data-driven model predictive control using Gaussian processes](https://mlsanity.com/paper/1908.01786/) (2019)
- [Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC](https://mlsanity.com/paper/2203.13671/) (2022)
- [Hybrid physics-informed metabolic cybergenetics: process rates augmented with machine-learning surrogates informed by flux balance analysis](https://mlsanity.com/paper/2401.00670/) (2024)
- [Linking intra- and extra-cellular metabolic domains via neural-network surrogates for dynamic metabolic control](https://mlsanity.com/paper/2310.17179/) (2023)

## Built on by (most cited first, 1 of 1)

- [An Uncertainty-Aware Dynamic Decision Framework for Progressive Multi-Omics Integration in Classification Tasks](https://mlsanity.com/paper/2507.01032/) (2025)

## Abstract (arXiv)

This work presents an omics-driven modeling pipeline that integrates machine-learning tools to facilitate the dynamic modeling of multiscale biological systems. Random forests and permutation feature importance are proposed to mine omics datasets, guiding feature selection and dimensionality reduction for dynamic modeling. Continuous and differentiable machine-learning functions can be trained to link the reduced omics feature set to key components of the dynamic model, resulting in a hybrid model. As proof of concept, we apply this framework to a high-dimensional proteomics dataset of textitSaccharomyces cerevisiae. After identifying key intracellular proteins that correlate with cell growth, targeted dynamic experiments are designed, and key model parameters are captured as functions of the selected proteins using Gaussian processes. This approach captures the dynamic behavior of yeast strains under varying proteome profiles while estimating the uncertainty in the hybrid model's predictions. The outlined modeling framework is adaptable to other scenarios, such as integrating additional layers of omics data for more advanced multiscale biological systems, or employing alternative machine-learning methods to handle larger datasets. Overall, this study outlines a strategy for leveraging omics data to inform multiscale dynamic modeling in systems biology and bioprocess engineering.