Learning chemical reaction networks from trajectory data

Wei Zhang, Stefan Klus, Tim Conrad, Christof Schütte

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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 l1 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.

Original languageEnglish
Pages (from-to)2000-2046
Number of pages47
JournalSIAM Journal on Applied Dynamical Systems
Issue number4
Publication statusPublished - 2019


  • Asymptotic analysis
  • Chemical reactions
  • Data-driven methods
  • Inverse problems
  • L sparse optimization

ASJC Scopus subject areas

  • Analysis
  • Modelling and Simulation


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