Learning chemical reaction networks from trajectory data

  • We develop a data-driven method to learn chemical reaction networks from trajectory data. Modeling the reaction system as a continuous-time Markov chain and assuming the system is fully observed,our method learns the propensity functions of the system with predetermined basis functions by maximizing the likelihood function of the trajectory data under l^1 sparse regularization. We demonstrate our method with numerical examples using synthetic data and carry out an asymptotic analysis of the proposed learning procedure in the infinite-data limit.

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Metadaten
Author:Wei Zhang, Stefan Klus, Tim Conrad, Christof Schütte
Document Type:Article
Parent Title (English):SIAM Journal on Applied Dynamical Systems (SIADS)
Volume:18
Issue:4
First Page:2000
Last Page:2046
Year of first publication:2019
ArXiv Id:http://arxiv.org/abs/1902.04920