# Machine learning in bioprocess development: From promise to practice

arXiv: https://arxiv.org/abs/2210.02200 · HTML: https://mlsanity.com/paper/2210.02200/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 219 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Physics-informed neural networks (PINNs) for fluid mechanics: A review](https://mlsanity.com/paper/2105.09506/) (2021)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [What Are Bayesian Neural Network Posteriors Really Like?](https://mlsanity.com/paper/2104.14421/) (2021)
- [Applications of Deep Learning and Reinforcement Learning to Biological Data](https://mlsanity.com/paper/1711.03985/) (2017)
- [Iterative Random Forests to detect predictive and stable high-order interactions](https://mlsanity.com/paper/1706.08457/) (2017)
- [Variational Autoencoder with Learned Latent Structure](https://mlsanity.com/paper/2006.10597/) (2020)
- [A Comparison of Word Embeddings for the Biomedical Natural Language Processing](https://mlsanity.com/paper/1802.00400/) (2018)
- [Reinforcement Learning for Batch Bioprocess Optimization](https://mlsanity.com/paper/1904.07292/) (2019)
- [Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks](https://mlsanity.com/paper/2207.12051/) (2022)
- [Recurrent Neural Network-based Model Predictive Control for Continuous Pharmaceutical Manufacturing](https://mlsanity.com/paper/1807.09556/) (2018)
- [Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectors](https://mlsanity.com/paper/2011.13863/) (2020)
- [Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation](https://mlsanity.com/paper/2010.10124/) (2020)

## Built on by (most cited first, 4 of 4)

- [Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review](https://mlsanity.com/paper/2506.12322/) (2025)
- [Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study](https://mlsanity.com/paper/2512.03460/) (2025)
- [Deep Set Neural Networks for forecasting asynchronous bioprocess timeseries](https://mlsanity.com/paper/2312.02079/) (2023)
- [Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling](https://mlsanity.com/paper/2607.20539/) (2026)

## Abstract (arXiv)

Fostered by novel analytical techniques, digitalization and automation, modern bioprocess development provides high amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. The aim of this review is to demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.