Experimentally Guided Iterative Parameter Estimation for Predictive Chemical Oscillator Models.

Chemical oscillators are fundamental to dynamic processes in biology, from circadian rhythms to metabolic regulation, inspiring efforts to design synthetic analogues for use in responsive materials, autonomous systems, and molecular computing. However, creating robust and tunable synthetic oscillators remains a major challenge due to the inherent complexity and difficulty of identifying conditions that support sustained oscillations. We herein describe an iterative approach based on mathematical modeling and parameter estimation guided by live experimental data to accurately model the oscillating chemical network. Fitting a kinetic model to the whole chemical network proves considerably more effective and time-efficient than determining reaction rates individually and enables quick screening of various parameters. We apply this method to achieve sustained oscillations in flow when changing various aspects of our recently developed oscillating system, demonstrating its potential to facilitate the development and optimization of organic oscillators as well as offering a general framework for analyzing and optimizing complex synthetic CRNs.