Heart disease is the leading cause of death in the United States. The underlying biochemical networks responsible for the progression of heart disease remain poorly understood because these networks are statically and dynamically complex; they are made up of many components that are connected by an intricate topology, and network response is determined by complicated nonlinear interactions between components. One strategy for dealing with the static and dynamic complexity found in cardiac signaling networks is the use of comprehensive network models. While several recent models of cardiac signaling have been developed, the utility of these models has been limited, in part because these models are not supported by state of the art software that facilitates the development and simulation of large scale, data driven models. To address these issues, we propose to develop a comprehensive, data-driven simulation model for studying cardiac signaling pathways. Phase I of this effort will focus on developing first a graphical and then a simulation model of two important G-protein signaling networks: Gs and Gq. After feasibility of the project has been established, Phase II will extend the model in three ways. First, other biochemical networks important to cardiac function will be added, including other signaling mechanisms, growth and death pathways, and cardiac electrophysiology. Second, experimental data will be gathered to speed model development and to validate model predictions. Third, network alterations occurring in specific diseased states will be added. The final product will be a comprehensive, commercially available computer model of the biochemical pathways responsible for normal and abnormal cardiac signal transduction and response.
| Rand, David G; Zhou, Qinlian; Buzzard, Gregery T et al. (2008) Computationally efficient strategy for modeling the effect of ion current modifiers. IEEE Trans Biomed Eng 55:3-13 |
