Design of Experiments for Verifying Biomolecular Networks
File(s)
Working paper
Author(s)
Sedgwick, Ruby
Goertz, John
Stevens, Molly
Misener, Ruth
Wilk, Mark van der
Type
Journal Article
Abstract
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.
Date Issued
2020
Citation
2020
Copyright Statement
© 2020 The Author(s).
Sponsor
Engineering and Physical Sciences Research Council
Identifier
http://arxiv.org/abs/2011.10575v2
Grant Number
EP/P016871/1
Subjects
q-bio.QM
q-bio.QM
cs.LG
stat.ML
Notes
Comment: Updated to correct typo "that that" => "that"