Figure legends
Figure 1: A hypothetical 16-state nonlinear QSP-style model. Each node (circle) represents a system state (variable). Edges (solid black arrows) represent mass (or molar) balance reactions and dashed lines ending with bar are mass (or molar) action. (+) represents a positive reaction, e.g. stimulation of a reaction, and (-) represents a negative reaction, e.g., inhibition of a reaction.
Figure 2: A general framework for understanding different approaches to model-order reduction. The original full-order model is shown as the starting point. The model can then be reduced using either parametric methods (shown as blue arrows) or nonparametric methods (shown in orange). Parametric approaches produced a reduced-order model based on mathematical techniques that stem from either reducing the number of compartments (nodes) and/or reactions (edges). The nonparametric methods approximate the full-order model input-output relationship (e.g. dose to INR relationship) with a simpler empirical model, which might be a black-box model input-output such as an artificial neural network or a user defined empirical function.
Figure 3: An illustration of the concept of dimension reduction. (A) 3-dimensional surface showing a hypothetical relationship between variables X,Y, and Z; (B) The same surface rotated clock-wise around the Z-axis so that only X-Z perspective is visible. With a small margin of error, Z can be approximated as a function of X, rather than as an independent parameter, thereby reducing the order of the model.