Hoffmann, M. and Fröhner, Chr. and Noé, F. (2019) Reactive SINDy: Discovering governing reactions from concentration data. J. Chem. Phys., 150 (2). 025101. ISSN 00219606, ESSN: 10897690

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Official URL: https://dx.doi.org/10.1063/1.5066099
Abstract
The inner workings of a biological cell or a chemical reaction can be rationalized by the network of reactions, whose structure reveals the most important functional mechanisms. For complex systems, these reaction networks are not known a priori and cannot be efficiently computed with ab initio methods, therefore an important approach goal is to estimate effective reaction networks from observations, such as time series of the main species. Reaction networks estimated with standard machine learning techniques such as leastsquares regression may fit the observations, but will typically contain spurious reactions. Here we extend the sparse identification of nonlinear dynamics (SINDy) method to vectorvalued ansatz functions, each describing a particular reaction process. The resulting sparse tensor regression method “reactive SINDy” is able to estimate a parsimonious reaction network. We illustrate that a gene regulation network can be correctly estimated from observed time series.
Item Type:  Article 

Additional Information:  SFB1114 Preprint in bioRxiv: 10/2018 (https://doi.org/10.1101/442095) 
Subjects:  Mathematical and Computer Sciences > Mathematics > Applied Mathematics 
Divisions:  Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group 
ID Code:  2353 
Deposited By:  Silvia Hoemke 
Deposited On:  26 Jun 2019 13:21 
Last Modified:  26 Jun 2019 13:21 
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