Design of Experiments for Verifying Biomolecular Networks
This work addresses the problem of efficiently validating mechanistic biomolecular network models, which is crucial for molecular and synthetic biologists due to the high cost and time commitment of experiments.
This paper proposes a design of experiments approach to efficiently validate biomolecular networks. It uses Gaussian processes to model the discrepancy between experimental results and the designed response, and a Bayesian optimization strategy to select subsequent sample points for validation.
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.