Modeling cardiac β-adrenergic signaling with normalized-Hill differential equations: comparison with a biochemical model

We developed a logic-based differential equation modeling approach for cell signaling networks based on normalized Hill activation/inhibition functions controlled by logical AND and OR operators to characterize signaling crosstalk. Using this approach, we modeled the cardiac β1-adrenergic signaling network, including 36 reactions and 25 species. Direct comparison of this model to an extensively characterized and validated biochemical model of the same network revealed that the new model gave reasonably accurate predictions of key network properties, even with default parameters. Normalized Hill functions improved quantitative predictions of global functional relationships compared with prior logic-based approaches. Comprehensive sensitivity analysis revealed the significant role of PKA negative feedback on upstream signaling and the importance of phosphodiesterases as key negative regulators of the network. The model was then extended to incorporate recently identified protein interaction data involving integrin-mediated mechanotransduction.The normalized-Hill differential equation modeling approach allows quantitative prediction of network functional relationships and dynamics, even in systems with limited biochemical data.The β-adrenergic signaling pathway plays a key role in the regulation of normal heart function and the development of heart failure [1-5]. Systems analysis of β-adrenergic signaling in the heart may provide important new insights into the mechanisms of heart failure and reveal new therapeutic targets. Previous mathematical models of cardiac β-adrenergic signaling have characterized how biochemical mechanisms of this pathway determine its coordinated regulation of cell contractility in health and disease [6-8]. However, this work relied on extensive biochemical data from the literature that may not be available for more recently discovered pathways. Therefore, more scalable modeling approaches are needed.As an alternative to generating biochemical