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Automation is becoming ubiquitous in all laboratory activities, leadingtowards precisely defined and codified laboratory protocols. However, theintegration between laboratory protocols and mathematical models is stilllacking. Models describe physical processes, while protocols define the stepscarried out during an experiment: neither cover the domain of the other,although they both attempt to characterize the same phenomena. We shouldideally start from an integrated description of both the model and the stepscarried out to test it, to concurrently analyze uncertainties in modelparameters, equipment tolerances, and data collection. To this end, we presenta language to model and optimize experimental biochemical protocols thatfacilitates such an integrated description, and that can be combined withexperimental data. We provide a probabilistic semantics for our language basedon a Bayesian interpretation that formally characterizes the uncertainties inboth the data collection, the underlying model, and the protocol operations. Ona set of case studies we illustrate how the resulting framework allows forautomated analysis and optimization of experimental protocols, including Gibsonassembly protocols.

A Language for Modeling And Optimizing Experimental Biological Protocols