  There is a growing trend in molecular and synthetic biology of using
mechanistic (non machine learning) models to design biomolecular networks. Once
designed, these networks need to be validated by experimental results to ensure
the theoretical network correctly models the true system. However, these
experiments can be expensive and time consuming. We propose a design of
experiments approach for validating these networks efficiently. Gaussian
processes are used to construct a probabilistic model of the discrepancy
between experimental results and the designed response, then a Bayesian
optimization strategy used to select the next sample points. We compare
different design criteria and develop a stopping criterion based on a metric
that quantifies this discrepancy over the whole surface, and its uncertainty.
We test our strategy on simulated data from computer models of biochemical
processes.
