Optimal Experimental Design Methods

Covariate balanced allocation of samples to batches to mitigate the impacts of technical variability.

The paper introduces a tool to optimally allocate samples to batches to balance covariates and reduce technical variability impacts.

Accelerating Cell Culture Media Development Using Bayesian Optimization-Based Iterative Experimental Design

This paper uses Bayesian optimization to accelerate cell culture media development, achieving better results with fewer experiments in two applications.

Rationalised experiment design for parameter estimation with sensitivity clustering

This paper introduces PARSEC, a model-based design of experiments framework that improves parameter estimation by using parameter sensitivity clustering and Bayesian computation.

Designing microplate layouts using artificial intelligence

This paper introduces a constraint programming method for designing microplate layouts to reduce bias and batch effects, improving accuracy in dose-response experiments and drug screening.

NLoed: A Python package for nonlinear optimal experimental design in systems biology

NLoed provides a Python package for nonlinear optimal experimental design in systems biology, supporting diverse models and data types with efficient computation.

Deep Reinforcement Learning for Optimal Experimental Design in Biology

This paper applies reinforcement learning to optimal experimental design in biology, showing it outperforms existing methods in bacterial growth parameter inference under parametric uncertainty.

Selection of time points for costly experiments: a comparison between human intuition and computer-aided experimental design

This study compares human intuition with a computational tool for selecting optimal time points in experiments, finding that the tool reduces bias and improves information capture.