Many industrial biotechnology applications require the optimization of complex multi-output processes, often with limited data and costly, laborious experiments. Hence we have developed and evaluated a customizable Bayesian optimization (BO) framework, BAIO2, using AI surrogates for multivariate, multi-output biotechnological applications. We focus on BO frameworks and potential surrogate models for two application cases: media optimization and genetic engineering for production of desired molecules using micro-organisms. Predictive analyses and simulation experiments are used to assess surrogate model performance and guide experimental design. Performance of BAIO2 is experimentally evaluated for the media optimization case to reach a target lipid distribution. The predictive analyses and simulations show that surrogate model performance varies by data size and round: multi-layer perceptrons (MLP) and Gaussian processes (GP) were generally strong with good exploration capabilities, whereas XGBoost regression (XGBR) often excelled in prediction but struggled to explore the search space. Simulations provide further insights into optimal experimental design and model selection. We conclude that BAIO2 is effective for multi-output biotechnological optimization and adaptable to different experimental setups. Future work will expand on a wider variety of acquisition functions and improve uncertainty estimation.
