Learning chemical reaction networks from trajectory data

Abstract

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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• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Predicting catastrophes in nonlinear dynamical systems by compressive sensing
• Sparse learning of stochastic dynamic equations
• Asymptotic analysis of multiscale approximations to reaction networks
• Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression

Cited by in corpus (1)

• When Machine Learning Meets Multiscale Modeling in Chemical Reactions