Parameter Optimisation for FCϵRIγ Pathway to Two Different Datasets Using Least-Squares Optimisation

Abstract

Syk is a tyrosine kinase important to bridge the receptor ligation and downstream signallings such as Ca2+ and PI3K. Once the cell receptor binds with the ligand, FCϵRIγ (ITAM receptor) is recruited and phosphorylated by Lyn. The phosphorylated ITAM then recruits protein tyrosine kinase (Syk). The previously developed FCϵRIγ (FCϵ) model contained a greater level of complexity. This study aims to build a simple model of signalling of FCϵ that still represents biological understanding. The parameter estimation is addressed using least-squares optimisation, which implements the Levenburg-Marquardt gradient method (greedy algorithm) to minimise an objective function. More importantly, this model was fitted to two data sets that captured a temporal FCϵ, Syk and Grb2 phosphorylation. Model uncertainty often has done as an analysis that is carried out after model construction and calibration have been completed. This study assessed for sensitivity to parameter choices and model uncertainty to perform the analysis. The modular design principles are applied to the construction of the model. The model is designed to be reproducible. In other words, the model can be effectively applied in simulation conditions or optimised to new datasets for new experimental situations.