Abstract
BackgroundNew approaches are needed for large-scale predictive modeling of cellular signaling networks. While mass action and enzyme kinetic approaches require extensive biochemical data, current logic-based approaches are used primarily for qualitative predictions and have lacked direct quantitative comparison with biochemical models.ResultsWe 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.ConclusionsThe normalized-Hill differential equation modeling approach allows quantitative prediction of network functional relationships and dynamics, even in systems with limited biochemical data.
Highlights
New approaches are needed for large-scale predictive modeling of cellular signaling networks
We developed a normalized-Hill differential equation modeling approach that combines advantages of both biochemical and Boolean models
The analysis presented here demonstrates that the normalized-Hill differential equation modeling approach can provide reasonably accurate predictions of signaling properties, even when little parameter data is available
Summary
New approaches are needed for large-scale predictive modeling of cellular signaling networks. As an alternative to generating biochemically detailed kinetic models, several modeling approaches that are more closely based on network topology have been developed including Boolean modeling [9], fuzzy logic modeling [10] and extreme pathways analysis [11]. These approaches require few or no parameters and facilitate large-scale analysis of systems properties, such as feedback loops and feasible solution spaces. Others have modeled signaling networks with continuous approximations of Boolean functions [18] that are implemented to minimize steady-state differences between Boolean and continuous models
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