Open AccessSystems biology informed deep learning for inferring parameters and hidden dynamics
Author(s) -
Alireza Yazdani,
Lu Lu,
Maziar Raissi,
George Em Karniadakis
Publication year - 2020
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1007575
Subject(s) - benchmark (surveying) , ordinary differential equation , inference , forcing (mathematics) , computer science , set (abstract data type) , artificial neural network , artificial intelligence , machine learning , deep learning , focus (optics) , differential equation , algorithm , mathematics , physics , mathematical analysis , geodesy , geography , programming language , optics
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.