Systems biology informed deep learning for inferring parameters and hidden dynamics

By Alireza Yazdani, Lu Lu, Maizar Raissi, G. Em Karniadakis. bioRxiv, 2019

339 citations recorded by the source corpus.

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. Author summary The dynamics of systems biological processes are usually modeled using ordinary differential equations (ODEs), which introduce various unknown parameters that need to be estimated efficiently from noisy measurements of concentration for a few species only. In this work, we present a new “systems-informed neural network” to infer the dynamics of experimentally unobserved species as well as the unknown parameters in the system of equations. By incorporating the system of ODEs into the neural networks, we effectively add constraints to the optimization algorithm, which makes the method robust to noisy and sparse measurements.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/33da5e93b3c9c02256c6a98f8a843ae62e27d436

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Citations in the corpus

On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks (2020, background): “PINNs in particular, have demonstrated remarkable power in applications including fluid dynamics [12, 13, 14], biomedical engineering [15, 16, 17], meta-material design [18, 19], free boundary problems [20], Bayesian networks and uncertainty quantification [21, 22], high dimensional PDEs [23, 24, 25”
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MIONet: Learning multiple-input operators via tensor product (2022, methodology): “These methods have shown promising results in diverse applications, such as ﬂuid mechanics [38], optics [4], systems biology [44, 5], and biomedicine [14].”
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