Developing Improved Models of Signal Transduction Pathways via Systems Biology
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Gaining an improved understanding of the molecular mechanisms involved in the acute phase response (APR) in the liver upon trauma or injury can lead to improved treatment of complications arising from inflammatory disorders. The dynamics of expression and interaction of the IL-6 signaling pathway molecules is a key factor of the phenotypical characteristic of the APR, as IL-6 has been identified as one of the systemic inflammatory mediators involved in the regulation of the hepatic APR. This work develops and analyzes a comprehensive mathematic model for signal transduction through the JAK/STAT and the MAPK signaling pathways in hepatocytes stimulated by IL-6. Interactions among the two signaling pathways are systematically investigated using sensitivity analysis in order to ultimately derive and validate an improved model. An important aspect of this work is the novel use of sensitivity analysis for determining which parts of the model may benefit from further model refinement, whereas traditionally sensitivity analysis has been applied to determine the contribution of parameters of an existing model to the dynamic behavior, i.e., such that the important parameters should be estimated from experimental data. While the exact nature of the additional mechanisms to include depends upon biological insight into the model, sensitivity analysis indicates which parameters may be masking more detailed mechanisms of importance to the model’s predictions. In this work, results from the sensitivity analysis are used to determine a location for including a (previously) hidden feedback loop between twice phosphorylated ERK and SOS as parameters contributing to reactions affecting these proteins were computed to be important. Additionally, experiments with GFP reporter cells were carried out where the amount of observed fluorescence is quantified to determine a profile for the concentration of GFPs. An inverse problem is formulated and solved that determines the transcription factor concentration from the measured fluorescence intensity profiles. These experimental results are compared to simulation data with the original and the newly developed model and were found to be in excellent agreement with the model derived in this work.