Code and data for "Incoherent feedback from coupled amino acids and ribosome pools generates damped oscillations in growing bacteria"

Experimental data gathered and code used for the simulations employed in the manuscript "Incoherent feedback from coupled amino acids and ribosome pools generates damped oscillations in growing bacteria".
In the "shift simulations" folder, you can find the simulation of a base model, which can be used to reproduce the oscillatory behavior induced by nutrient shifts. In addition, there is a code for the numerical computation of the steady-state conditions.
In the "CHI BIO DATA" folder, you can find the data gathered during the chi bio experiments.
In the "Microfluidic (30 degrees) data" folder, you can find the data gathered during the microfluidic experiments performed at 30 degrees.
For more information on the data and code, please refer to the method section of the paper.

Abstract
Current theories of bacterial growth physiology demonstrate impressive predictive power but are often phenomenological, lacking mechanistic detail. Incorporating such details would significantly enhance our ability to predict and control bacterial growth under varying environmental conditions. The "Flux Controlled Regulation" (FCR) model serves as a reference framework, linking ribosome allocation to translation efficiency through a steady-state assumption. However, it neglects ppGpp-mediated nutrient sensing and transcriptional regulation of ribosomal operons. We propose a mechanistic model that extends the FCR framework by incorporating three key components: (i) the amino acid pool, (ii) ppGpp sensing of translation elongation rate, and (iii) transcriptional regulation of protein allocation by ppGpp-sensitive promoters. Our model aligns with observed steady-state growth laws and makes testable predictions for unobserved quantities. Here, we show that during environmental changes, the incoherent feedback between sensing and regulation generates oscillatory relaxation dynamics, a behavior that we support by new and existing experimental data.