Appendix 1 Glossary of Terms
Amplification: a process in which the response of the system to a stimulus increases over time via internal processes
Automatable : An algorithmic method that requires no user intervention.
Compartment : a chamber (real or hypothetical) in which the variable is homogenous throughout and assumed to be instantaneously mixed, for example the concentration in a compartment.
Controllable state : A state that is changed when the input variables are changed (e.g. the concentration in the body is changed by dose).
Damping: a process in which the oscillations in a system, caused by some stimulus are reduced, restricted or inhibited over time.
Edge : a graphical representation of movement or an action. This is usually shown as a line with a bar at the end or an arrow.
Empirical function : A user defined function to describe a set of input-output data with no (or very few) assumption about the underlying processes that generated the data.
Extrapolation : The prediction of an outcome or response by a model under conditions or values of input variables other than those (typically outside of the range) upon which the model was built.
Full-order model: a model that is its original (full) size, e.g. an original QSP model
Input-output (I/O): pertaining to the input variables (e.g. dose, dose time, route, etc) and the output variables represented by a set of response variables (e.g. haemodynamic variables such as blood pressure and heart rate etc)
Model dimension : The number of state variables & parameters in a model.
Model-order reduction : a method to reduce a large model into a simpler model
Model-order : A representation of the size of a model which is related to the number of parameters.
Node : a graphical representation of a compartment in a model. This is usually shown as a box or a circle (depending on preference)
Nonlinear system : A system whose outputs are not proportional to inputs. In a nonlinear system, small changes in the inputs may lead to substantial changes in the outputs.
Observable state : A state (node) that when changed results in a change in the observed response (e.g. changes in the concentration of a drug results in changes in blood pressure).
Pseudo-data : A set of input-output data simulated (e.g. from full-order QSP model) to reflecting its behaviour and is used in training a surrogate model.
Quasi-steady state approximation : An approach to approximating equilibrium systems with different time scales by assuming that rapidly equilibrating states reach equilibrium instantaneously.
Reduced-order model : a model that is smaller in size (number of states & parameters) than the full-order model from which it was derived.
State : a variable in a model whose value evolves through time. This is the same as a concentration in a pharmacokinetic model. It differs from a compartment as the compartment is the location (hypothetical or real) of the concentration. In a pharmacokinetic example, a state is the concentration of a particular drug in a particular compartment. A state is normally shown graphically as a node.
Structural Identifiability : A property of a model whereby no two (or more) sets of parameters give identical model predictions. A model that is identifiable can be used for parameter estimation (and vice versa).
System agnostic : The method applied to the system is not affected by/dependent on the nature of the system.