APPLYING FEEDBACK CONTROL TO AUTOMATICALLY TRACK AND DESIGN COMPLEX DYNAMICS IN SYSTEMS AND SYNTHETIC BIOLOGY

Mathematical modelling is widely used in System and Synthetic Biology to understand nonlinear biochemical phenomena (e.g. gene expression temporal oscillations), and to design and validate engineered gene circuits. Although widely used, biochemical models can be challenging in both their derivation and associated parameter identification. Parameter and model uncertainty can significantly alter the reliability of model predictions of nonlinear molecular dynamic behaviours. This, in turns, challenges the use of models to design effective cancer treatments which target multiple signaling pathways to avoid resistance and adaptation.Recently, in Synthetic Biology, principles from Control Engineering have been applied to steer gene expression in living cells. The so called "external feedback control" exploits microfluidics/microscopy platforms to culture and perturb dynamically living cells, while monitoring gene expression by means of fluorescent reporters and segmentation algorithms. The application of external feedback control in mammalian cell is very recent [1] and has, so far, only provided the proof-of-concept that Relay and Model Predictive Control strategies can be used to regulate exogenous and endogenous gene expression.This project aims both at improving and extending current capabilities of mammalian cell external feedback control, and at exploring its application to address important open challenges in Systems and Synthetic Biology.The improved methodologies we aim at implementing include both refining the segmentation algorithms to encompass Deep-Learning approaches, and adopting model-free strategies (e.g. Adaptive Model Predictive Control) able to cope with the stochastic nature of gene expression regulation and uncertainties in system mathematical models. While developing these tools, we will test the usability of external feedback control strategies to:1) automatically map from experiments nonlinear dynamic features of endogenous and exogenous gene regulatory networks applying Control-based continuation, whose application so far has been limited to electro-mechanical systems; 2) design superior combination therapies to overcome the emergence of drug resistance in lung cancer cell lines; this part of the project will be developed in collaboration with AstraZeneca, currently partnering with the main supervisor (Dr Marucci) in an EPSRC Fellowship.Outcomes of this interdisciplinary project will impact researchers across the Systems and Synthetic Biology, and Control Engineering communities.
[1] Postiglione et al. ACS Synthetic Biology 2018