Model Predictive Control of Cancer Cellular Dynamics: A New Strategy for Therapy Design

Abstract

Recent advancements in Cybergenetics have lead to the development of new computational/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 optimization-based control algorithm enables tailoring the cost function to force the controller to alternate different drugs and/or reduce drug exposure to minimize both drug-induced toxicity and the possibility to cause resistance to treatment. Our results pave the way for new cybergenetics experiments in cancer cells for drug combination therapy design.