Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 150, Issue 2, Pages -Publisher
AMER INST PHYSICS
DOI: 10.1063/1.5066099
Keywords
-
Funding
- NSF [PHY-1427654]
- Deutsche Forschungsgemeinschaft [SFB 958, TRR 186, SFB 1114]
- Einstein Foundation Berlin (ECMath Project CH17)
- European Research Council [ERC CoG 772230]
Ask authors/readers for more resources
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. Published under license by AIP Publishing.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available