Inferring global a priori identifiability of optical biosensor experiment models

Flow-cell optical biosensors are a popular means of studying biomolecular interactions. The time course of data produced shows the progress of the interaction in real time. In a quantitative study, data is used to estimate parameters, such as rate constants, in a mathematical model of interactant concentrations over time. A study unable to assign a unique estimate to each parameter may be less informative than desired. This result can be anticipated prior to data collection by testing the assumed model class for global a priori identifiability. In the case where an interaction model is an uncontrolled continuous-time linear switching system, the testing method available is applicable only in a special case. This paper proposes an algorithm that is sufficient for classifying a previously unclassified test case as globally a priori identifiable.