  Automation is becoming ubiquitous in all laboratory activities, leading
towards precisely defined and codified laboratory protocols. However, the
integration between laboratory protocols and mathematical models is still
lacking. Models describe physical processes, while protocols define the steps
carried out during an experiment: neither cover the domain of the other,
although they both attempt to characterize the same phenomena. We should
ideally start from an integrated description of both the model and the steps
carried out to test it, to concurrently analyze uncertainties in model
parameters, equipment tolerances, and data collection. To this end, we present
a language to model and optimize experimental biochemical protocols that
facilitates such an integrated description, and that can be combined with
experimental data. We provide a probabilistic semantics for our language based
on a Bayesian interpretation that formally characterizes the uncertainties in
both the data collection, the underlying model, and the protocol operations. On
a set of case studies we illustrate how the resulting framework allows for
automated analysis and optimization of experimental protocols, including Gibson
assembly protocols.
