Systems biology informed deep learning for inferring parameters and hidden dynamics
Name
file.pdf
Description
Published version
Size
2.71 MB
Format
Adobe PDF
Checksum (MD5)
1177624e943d5a2f7a15b8618099f927
Author(s)
Yazdani, Alireza
Lu, Lu
Raissi, Maziar
Karniadakis, George Em
Date Issued
November 18, 2020
Journal
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
Yazdani A, Lu L, Raissi M, Karniadakis GE (2020) Systems biology informed deep learning for inferring parameters and hidden dynamics. PLOS Computational Biology 16(11): e1007575
Version
Final published version
Abstract
Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been one of the core subjects in systems biology, and is the focus of this study. We have developed a new systems-biology-informed deep learning algorithm that incorporates the system of ordinary differential equations into the neural networks. Enforcing these equations effectively adds constraints to the optimization procedure that manifests itself as an imposed structure on the observational data. Using few scattered and noisy measurements, we are able to infer the dynamics of unobserved species, external forcing, and the unknown model parameters. We have successfully tested the algorithm for three different benchmark problems.
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1007575
