Kernel Filter-Based Adaptive Controllers For Cybergenetics Applications

Abstract

Cybergenetics is an advancing field that seeks to implement control theory within biological systems. When applying feedback control for the regulation of gene expression or cell proliferation, model-based control strategies can be applied; in this context, online adaptive mathematical models can be used to keep models in tune with the current behaviour of the biological system. Controllers are often constrained by their sampling rate, which is usually relatively low when using microfluidics/microscopy platforms. Current adaptive filters can lead to an inaccurate predictive model when operating with a low sampling rate, leading to sub-optimal control. Here, we propose a kernel filter that can fit model parameters online to produce a more accurate predictive model that can be included within an adaptive model predictive control scheme. The use of the kernel filter is demonstrated in in silico and in vitro experiments, where we control a synthetic gene oscillator and a P53 oscillator, and observe a synthetic toggle switch. Our results show that the kernel filter outperforms a particle filter when used for parameter estimation in both the predictive model accuracy and when included within an adaptive model-based controller.