Parameter estimation for biochemical reaction networks using Wasserstein distances
| Autor: | Guido Sanguinetti, Kaan Öcal, Ramon Grima |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Statistics and Probability Stochastic modelling Computer science Molecular Networks (q-bio.MN) chemical Master equation General Physics and Astronomy 02 engineering and technology Quantitative Biology - Quantitative Methods 03 medical and health sciences symbols.namesake Applied mathematics Quantitative Biology - Molecular Networks Wasserstein distance Gaussian process Mathematical Physics Quantitative Methods (q-bio.QM) Bayesian optimization Estimation theory Chemical Master Equation Statistical and Nonlinear Physics Feedback loop 021001 nanoscience & nanotechnology Settore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin) Moment (mathematics) 030104 developmental biology Modeling and Simulation FOS: Biological sciences Brownian dynamics symbols 0210 nano-technology parameter estimation Distance based |
| Zdroj: | Öcal, K, Grima, R & Sanguinetti, G 2019, ' Parameter estimation for biochemical reaction networks using Wasserstein distances ', Journal of Physics A: Mathematical and Theoretical, vol. 53, no. 3, 034002, pp. 1-23 . https://doi.org/10.1088/1751-8121/ab5877 |
| DOI: | 10.48550/arxiv.1907.07986 |
| Popis: | 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. Comment: 22 pages, 8 figures. Slight modifications/additions to the text; added new section (Section 4.4) and Appendix |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|