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Standardized microplates are used to conduct large-scale biomedical research. The design of microplate layouts plays an essential role in handling so-called plate effects, i.e., systematic variations across the geometry of a microplate. An effective layout allows us to detect and negate plate effects. The randomized placement of controls and compounds produces layouts of limited effectiveness, so specific approaches are needed. A previously developed system, PLAID, proposed a constraint satisfaction model to construct effective plate layouts. However, PLAID does not scale well with microplate dimensions. To improve on PLAID, we propose Constraint Optimization of MicroPlate Designs (COMPD), which allows for greater flexibility and higher quality of the layouts.

Microplates are widely used for high-throughput experiments but are vulnerable to systematic plate effects. Careful placement of materials on a microplate can counteract them. While some tools exist, they often rely on randomization, are proprietary, and web-based, raising privacy concerns. PLAID and COMPD are two constraint programming (CP) models that produce higher quality layouts than randomization. However, they require MiniZinc and CP expertise, with no built-in visualization. MPLACE addresses these barriers as an open-source, Python-based GUI. MPLACE streamlines the workflow with both models by providing convenient data entry, multiple layout configurations, saving, loading, and visualization capabilities.

Liquid-handling instruments are indispensable tools in modern biomedical laboratories, streamlining compound and sample management tasks with precision and efficiency. Compound dispensing from large chemical libraries divided over hundreds of microwell plates can require substantial swapping of plates, particularly if compounds from multiple source plates are to be dispensed in each destination plate. Despite robotisation, plate swapping is a time-consuming necessity for high-throughput experiments, posing a significant bottleneck. In this paper, we explore the application of constraint programming (CP) to the planning of liquid-handling tasks to minimise plate swaps in automated dispensing. We formulate the problem as a combination of a set partitioning problem and the construction of a bipartite network. We present six CP models implemented in MiniZinc and evaluate their performance on synthetic benchmarks using three state-of-the-art constraint solvers. This work highlights the potential of CP to enhance the scalability and efficiency of automated compound transfer systems.