Parametric identifiability of a structured kinetic model for mammalian cell cultures

Abstract

Model parametric identifiability poses the question - "based on the mathematical behaviour of the model itself and assuming noise-free data - can all of the model parameters, in principle, be uniquely identified?" A class of parametric models, such as kinetic models, are used to capture the complex dynamics of cellular processes. However, increased model fidelity is often accompanied by an enlargement of the model parameter set and the expensive and time-consuming nature of mammalian cell culture experiments makes model parameter identifiability especially valuable as the development of unidentifiable models can be halted prior to experimental validation stages, in turn saving time and valuable resources. In this work, parameter identifiability analysis was performed on a state-of-the-art cell culture model described by a large differential algebraic equation system containing 200 equations and over 300 parameters. However, both the size and properties of this very large model has meant that a "practical identifiability" or estimability method had to be applied.