SINDy for delay-differential equations: application to model bacterial zinc response

Abstract

We extend the data-driven method of Sparse Identification of Nonlinear Dynamics (SINDy) developed by Brunton et al, Proc. Natl. Acad. Sci USA 113 (2016) to the case of delay differential equations (DDEs). This is achieved in a bilevel optimization procedure by first applying SINDy for fixed delay and then subsequently optimizing the error of the reconstructed SINDy model over delay times. We test the SINDy-delay method on a noisy short data set from a toy delay differential equation and show excellent agreement. We then apply the method to experimental data of gene expressions in the bacterium {\it Pseudomonas aeruginosa} subject to the influence of zinc. The derived SINDy model suggests that the increase of zinc concentration mainly affects the time delay and not the strengths of the interactions between the different agents controlling the zinc export mechanism.

21 pages. To appear in Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences

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• A Bayesian machine scientist to aid in the solution of challenging scientific problems
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• SINDy for delay-differential equations: application to model bacterial zinc response
• A normal form for excitable media
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Cited by in corpus (3)

• SINDy for delay-differential equations: application to model bacterial zinc response
• Data-driven Discovery of Delay Differential Equations with Discrete Delays
• Hyperparameter Optimization in the Estimation of PDE and Delay-PDE models from data