Journal
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
Volume 53, Issue 3, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/1751-8121/ab5877
Keywords
Wasserstein distance; parameter estimation; chemical Master equation; Bayesian optimization
Categories
Funding
- EPSRC Centre for Doctoral Training in Data Science
- UK Engineering and Physical Sciences Research Council [EP/L016427/1]
- University of Edinburgh
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We present a method for estimating parameters in stochastic models of biochemical reaction networks by fitting steady-state distributions using Wasserstein distances. We simulate a reaction network at different parameter settings and train a Gaussian process to learn the Wasserstein distance between observations and the simulator output for all parameters. We then use Bayesian optimization to find parameters minimizing this distance based on the trained Gaussian process. The effectiveness of our method is demonstrated on the three-stage model of gene expression and a genetic feedback loop for which moment-based methods are known to perform poorly. Our method is applicable to any simulator model of stochastic reaction networks, including Brownian dynamics.
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