Parameter estimation for biochemical reaction networks using Wasserstein distances

Kaan Öcal, Ramon Grima, Guido Sanguinetti

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number034002
Pages (from-to)1-23
Number of pages24
JournalJournal of Physics A: Mathematical and Theoretical
Issue number3
Early online date18 Nov 2019
Publication statusPublished - 23 Dec 2019


  • Wasserstein distance
  • Bayesian optimization
  • Chemical Master Equation
  • parameter estimation


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