# Multi-fidelity batch Bayesian optimization for bioprocess development across scales

arXiv: https://arxiv.org/abs/2508.10970 · HTML: https://mlsanity.com/paper/2508.10970/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 60 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Max-value Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/1703.01968/) (2017)
- [Unexpected Improvements to Expected Improvement for Bayesian Optimization](https://mlsanity.com/paper/2310.20708/) (2023)
- [Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020](https://mlsanity.com/paper/2104.10201/) (2021)
- [Multi-fidelity Bayesian Optimisation with Continuous Approximations](https://mlsanity.com/paper/1703.06240/) (2017)
- [Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning](https://mlsanity.com/paper/1903.04703/) (2019)
- [The reparameterization trick for acquisition functions](https://mlsanity.com/paper/1712.00424/) (2017)
- [BOSS: Bayesian Optimization over String Spaces](https://mlsanity.com/paper/2010.00979/) (2020)
- [GIBBON: General-purpose Information-Based Bayesian OptimisatioN](https://mlsanity.com/paper/2102.03324/) (2021)
- [Scalable Thompson Sampling using Sparse Gaussian Process Models](https://mlsanity.com/paper/2006.05356/) (2020)
- [Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes](https://mlsanity.com/paper/2110.11665/) (2021)
- [New Paradigms for Exploiting Parallel Experiments in Bayesian Optimization](https://mlsanity.com/paper/2210.01071/) (2022)
- [Machine Learning-Assisted Discovery of Flow Reactor Designs](https://mlsanity.com/paper/2308.08841/) (2023)
- [Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectors](https://mlsanity.com/paper/2011.13863/) (2020)
- [Efficient computation of the Knowledge Gradient for Bayesian Optimization](https://mlsanity.com/paper/2209.15367/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development](https://mlsanity.com/paper/2606.19230/) (2026)
- [Out-Of-The-Loop Multi-Fidelity Bayesian Optimization](https://mlsanity.com/paper/2608.04113/) (2026)

## Abstract (arXiv)

Bioprocesses are central to modern biotechnology, enabling sustainable production of pharmaceuticals, specialty chemicals, cosmetics, and food. However, developing high-performing processes remains costly and complex, requiring iterative, multi-scale experimentation from microtiter plates to pilot reactors. Conventional Design of Experiments (DoE) approaches often struggle to address process scale-up and the joint optimization of reaction conditions and biocatalyst selection. We present a multi-fidelity batch Bayesian optimization framework to accelerate bioprocess development and reduce experimental costs. The method integrates Gaussian processes tailored for multi-fidelity modeling and mixed-variable optimization. At each iteration, the algorithm proposes not only the next experimental conditions but also the appropriate scale and choice of biocatalyst (i.e., cell clones). To benchmark performance, we developed a custom simulation of a Chinese hamster ovary bioprocess that captures the non-linear, coupled dynamics of scale-up across different clones. Multiple case studies demonstrate that the proposed workflow achieves a reduction in experimental costs while improving yield compared to industrial DoE baselines. This work provides a data-efficient strategy for bioprocess optimization and highlights opportunities for incorporating transfer learning and uncertainty-aware design for sustainable biotechnology.