Transcriptional fusions data from SINDy for delay-differential equations: application to model bacterial zinc response

We extend the data-driven method of sparse identification of nonlinear dynamics (SINDy) developed by Brunton <i>et al.</i>, <i>Proc. Natl Acad. Sci. USA</i> <b>113</b> (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 dataset from a toy DDE and show excellent agreement. We then apply the method to experimental data of gene expressions in the bacterium <i>Pseudomonas aeruginosa</i> subject to the influence of zinc. The derived SINDy model suggests that the increase in zinc concentration mainly affects the time delay and not the strengths of the interactions between the different agents controlling the zinc export mechanism.