Structural and Practical Identifiability Analysis of Models for Syncytia Growth

Syncytia are multinucleated cells that can occur due to virus infection of cells. Mathematical models in the form of ordinary differential equations can be used to simulate the growth of syncytia. Several novel ODE models can explain syncytia growth. Before employing ODE models on actual data, it is essential to analyze their structural (theoretical) and practical identifiability. Structural identifiability is an inherent property of each model, referring to our ability to determine parameter values within the model from ideal data. Practical Identifiability analysis of a model is concerned with assessing our ability to accurately determine parameter values given experimental conditions such as noise, infrequent sampling, and finite data. Combining these two techniques enables us to determine whether or not parameters within our syncytia models can be accurately determined to give biological insight into syncytia growth. Performing this analysis allows for the quantification of error inherent to our models and allows for experiments to be designed to successfully estimate key biological parameters and minimize uncertainty within the contexts of our proposed models.