Repository: Freie Universität Berlin, Math Department

Learning chemical reaction networks from trajectory data

Zhang, W. and Klus, S. and Conrad, T. O. F. and Schütte, Ch. (2019) Learning chemical reaction networks from trajectory data. SIAM Journal on Applied Dynamical Systems . ISSN 1536-0040 (In Press)

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Official URL: https://www.siam.org/publications/journals/siam-jo...

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.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Mathematics
Mathematical and Computer Sciences > Mathematics > Mathematical Modelling
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics
Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group
Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Proteomics Group
ID Code:2376
Deposited By: Admin Administrator
Deposited On:08 Oct 2019 19:02
Last Modified:08 Oct 2019 19:23

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