Multiple Model-Informed Open-Loop Control of Uncertain Intracellular Signaling Dynamics
Figure 2
A framework for multiple-model open-loop control of uncertain intracellular signaling in the laboratory.
First, the model bank is populated with a set of relevant models that predict the system response to possible control inputs. During the initial stage, training data are used to generate weights maps. These maps inform the controller of the tendency for models to differ in their ability to accurately reproduce the system dynamics under different control inputs. During each time interval of the controller stage, the performance metrics for the models are optimized simultaneously using a multiobjective technique within a MPC framework to generate a candidate solution set. The tasks involved in the adaptive model weighting strategy are contained within the gray box: control inputs are selected from the solution set by prioritizing them according to the weight maps, then model weights are automatically recalibrated using the portion of training data that most closely corresponds to the proposed control input. Optimization and input selection cycles repeat for subsequent time intervals as the prediction horizon slides along until the entire open-loop control sequence is specified and ready to be applied to the in vitro system.