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.

翻译：使用机械(非机器学习)模型设计生物分子网络的分子和合成生物学趋势日益明显。一旦设计,这些网络就需要通过实验结果加以验证,以确保理论网络正确模拟真实的系统。然而,这些实验可能成本高,耗时多。我们建议设计一个实验方法,以有效验证这些网络。高斯过程用来构建实验结果和设计反应之间差异的概率模型,然后用巴耶斯优化战略选择下一个取样点。我们比较不同的设计标准,并根据测量整个表面和不确定性的尺度,制定停止标准。我们测试我们从计算机模型中模拟生物化学过程数据的战略。