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Systems biology informed deep learning for inferring parameters and hidden dynamics.

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.

SUBMITTER: Yazdani A

PROVIDER: S-EPMC7710119 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Systems biology informed deep learning for inferring parameters and hidden dynamics.

Yazdani Alireza A Lu Lu L Raissi Maziar M Karniadakis George Em GE

PLoS computational biology 20201118 11

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 sys ...[more]

PMID: 33206658

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