Model predictive control of cancer cellular dynamics: a new strategy for therapy design - Smart, Benjamin; de Cesare, Irene; Renson, Ludovic; Marucci, Lucia (Frontiers in Control Engineering, 2022)
Recent advancements in Cybergenetics have led to the development of new computational and experimental platforms that enable to robustly steer cellular dynamics by applying external feedback control. Such technologies have never been applied to regulate intracellular dynamics of cancer cells. Here, we show in silico that adaptive model predictive control (MPC) can effectively be used to steer signalling dynamics in Non-Small Cell Lung Cancer (NSCLC) cells to resemble those of wild-type cells, and to support the design of combination therapies. Our optimisation-based control algorithm enables tailoring the cost function to force the controller to alternate different drugs and/or reduce drug exposure, minimising both drug-induced toxicity and resistance to treatment. Our results pave the way for new cybergenetics experiments in cancer cells for drug combination therapy design.