Sensitivity analysis in biological modeling: An application in the model development of staphylococcal enterotoxin B pre-apoptotic pathways

One of the challenges in a system-level approach in systems biology is the development of in silico models from experiments that can accurately capture the cellular behavior. The hurdles in this effort, known as reverse engineering, are multiple and include network size and complexity, and quantity and quality of measurements. System analysis can help unraveling the complexity in cellular networks. One such method is sensitivity analysis, which shows the dependence of system behavior on model parameters. For the measurement aspect of modeling, information theoretic approach such as the Fisher information matrix (FIM) can provide a measure of the degree of information content in noisy measurement data for estimating the accuracy of parameter estimates. These tools are included in a MATLAB-based graphical user interface (GUI) named BioSens, for ease-of-use by non-experts in systems theory. The utility of sensitivity analysis and the Fisher information matrix is demonstrated in the model development of staphylococcal enterotoxin-B (SEB) response in kidney cells.