Zobrazit v EDS

Reactive SINDy: Discovering governing reactions from concentration data.

Uloženo v:
Podrobná bibliografie
Název: Reactive SINDy: Discovering governing reactions from concentration data.
Autoři: Hoffmann, Moritz, Fröhner, Christoph, Noé, Frank
Zdroj: Journal of Chemical Physics; 1/14/2019, Vol. 150 Issue 2, pN.PAG-N.PAG, 12p, 1 Diagram, 4 Charts, 7 Graphs
Témata: NONLINEAR dynamical systems, VECTOR analysis, DYNAMICAL systems, GENETIC regulation, PARSIMONIOUS models
Abstrakt: The inner workings of a biological cell or a chemical reactor 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 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 least-squares regression may fit the observations but will typically contain spurious reactions. Here we extend the sparse identification of nonlinear dynamics (SINDy) method to vector-valued 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Chemical Physics is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
Popis
Abstrakt:The inner workings of a biological cell or a chemical reactor 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 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 least-squares regression may fit the observations but will typically contain spurious reactions. Here we extend the sparse identification of nonlinear dynamics (SINDy) method to vector-valued 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. [ABSTRACT FROM AUTHOR]
ISSN:00219606
DOI:10.1063/1.5066099