Building trust in automated experimentation: uncertainty quantification in the era of high-throughput biolabs

Uncertainty quantification (UQ) is central to data analytics, particularly in the life sciences, where experiments are often affected by significant measurement noise. In emerging automated high-throughput biolabs, such as biofoundries, parallel cultivation systems, and smart analytics platforms, UQ should be a built-in feature rather than an optional add-on. These environments pose a unique challenge: robotic liquid handling must be combined with miniaturized biochemical analytics (including omics), process monitoring, online data analytics, and digital control. Although traditional UQ methods from classical and computational statistics remain valid and applicable, integrating them into highly parallelized experimental and digital workflows presents new challenges. These include data preprocessing, model-based data integration, decision-making, and experimental control. In this review, we examine the emerging demands on UQ in automated experimentation and survey recent frameworks, strategies, and computational tools designed to address them.