Journal Article

Systems biology informed deep learning for inferring parameters and hidden dynamics

Karniadakis, George Em, Lu, Lu, Yazdani, Alireza, Raissi, Maziar

2020

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Analysis / Apoptosis / Biological models (mathematics) / Biology / Biology and Life Sciences / Computer and Information Sciences / Computer applications / Control theory / Data integrity / Deep Learning / Inferential statistics / Machine learning / Medicine and Health Sciences / Methods / Models, Biological / Neural networks / Noise pollution / Noise standards / Optimization techniques / Ordinary differential equations / Parameter estimation / Physical Sciences / Rate constants / Research and Analysis Methods / Systems biology / Systems Biology - methods / Technology application / White noise