Specific Aims Acknowledgements: Andrew McCulloch, Stuart Campbell, Victor Chiu [1] Insel et al. J. Biological Chemistry (2004) 279:19: [2] Saucerman and McCulloch. Annals NY Acad. Sciences (2006) 1080: [3] Saucerman et al. J. Biological Chemistry (2003) 278:48: [4] Iancu et al. Biophysical J. (2007) 92: [5] [6] [7] Kordylewski et al. Circulation Research (1993) 73:1: Introduction Device Description Methods Verification and Validation Results (2) IP Landscape Discussion and Conclusions ReferencesReferences Virtual Cell Virtual Cell was used to set up and solve Michaelis-Menten kinetics and rate laws describing the β 1 -adrenergic and M 2 - muscarinic signaling pathways [5]. Virtual Cell was used to set up and solve Michaelis-Menten kinetics and rate laws describing the β 1 -adrenergic and M 2 - muscarinic signaling pathways [5]. In the cardiac myocyte, these pathways were modeled in three compartments: a caveolar, an extracaveolar, and a cytosolic compartment. This was achieved by incorporating Michaelis- Menten kinetics and rate laws from two previously proposed models [3, 4]. In the cardiac myocyte, these pathways were modeled in three compartments: a caveolar, an extracaveolar, and a cytosolic compartment. This was achieved by incorporating Michaelis- Menten kinetics and rate laws from two previously proposed models [3, 4]. The model was then used to see how compartmentation of signaling pathways affected cAMP production. The model was then used to see how compartmentation of signaling pathways affected cAMP production. Dynamic Effects of Caveolae Unfolding and Effects of Compartmentalization in Cardiomyocytes Amy Hsieh 1, Corbin Clawson 1, and Tom Bartol 2 1 Department of Bioengineering, University of California, San Diego, CA; 2 The Salk Institute for Biological Studies, San Diego, CA A model of the β 1 -adrenergic and M 2 -muscarinic signaling pathways in cardiac myocytes incorporating caveolae localized pathways has been developed in Virtual Cell. Results from this model produced the same trends as those found in experimental data [3]. A model of the β 1 -adrenergic and M 2 -muscarinic signaling pathways in cardiac myocytes incorporating caveolae localized pathways has been developed in Virtual Cell. Results from this model produced the same trends as those found in experimental data [3]. The unfolding dynamics of a single caveola has been modeled in MCell/Dreamm. The unfolding dynamics of a single caveola has been modeled in MCell/Dreamm. These models provide new approaches for studying the compartmentation of cAMP in cardiac signaling. These models provide new approaches for studying the compartmentation of cAMP in cardiac signaling. IP Landscape Results Caveolae are nm invaginations of the plasma membrane first discovered in electron microscopy images of the plasma membrane [1]. Caveolae are nm invaginations of the plasma membrane first discovered in electron microscopy images of the plasma membrane [1]. In cardiac myocytes, caveolae concentrate signaling pathways such as the  -adrenergic receptor pathway [2].  -adrenergic signaling pathways classically regulate heart rate, contractility, rate of relaxation, and modulate metabolism. In cardiac myocytes, caveolae concentrate signaling pathways such as the  -adrenergic receptor pathway [2].  -adrenergic signaling pathways classically regulate heart rate, contractility, rate of relaxation, and modulate metabolism. In this study, caveolar and extracaveolar microdomains were integrated into a kinetic model of  -adrenergic signaling. These were then modeled in Virtual Cell and MCell/Dreamm to study cAMP production as a result of the unfolding dynamics of caveolae and compartmentalization. In this study, caveolar and extracaveolar microdomains were integrated into a kinetic model of  -adrenergic signaling. These were then modeled in Virtual Cell and MCell/Dreamm to study cAMP production as a result of the unfolding dynamics of caveolae and compartmentalization. Figure 1: A) Previously proposed kinetic model of  -adrenergic signaling used to study cAMP production, but it could not fully explain discrepancies in cAMP levels within cardiomyocytes [3]. B) Recently proposed compartmentation model of cardiac myocytes with caveolar and extracaveolar microdomains and a bulk cytosolic domain [4]. A B IP Landscape Future Directions Each of the models still require values that were, to date, unfound in literature. Thus, experimental work to determine these parameters is necessary. Each of the models still require values that were, to date, unfound in literature. Thus, experimental work to determine these parameters is necessary. Due to limitations in time, only a single caveola was modeled in MCell. Possible future directions include expanding the MCell model to incorporate the appropriate number of caveola found in a cardiac myocyte and inclusion of additional signaling pathways found in the cell. Due to limitations in time, only a single caveola was modeled in MCell. Possible future directions include expanding the MCell model to incorporate the appropriate number of caveola found in a cardiac myocyte and inclusion of additional signaling pathways found in the cell. Linking of results from MCell and Virtual Cell: using caveolae unfolding dependent rate constants determined from MCell, these values can be then be incorporated into Virtual Cell to provide a better description of spatial and temporal responses in a cell. Linking of results from MCell and Virtual Cell: using caveolae unfolding dependent rate constants determined from MCell, these values can be then be incorporated into Virtual Cell to provide a better description of spatial and temporal responses in a cell. Monte Carlo Cell and Dreamm Virtual Cell Figure 2: A) Signaling cascades of a cardiac myocyte modeled in Virtual Cell. B) Temporal responses of cyclic AMP accumulation to 1uM isoproterenol: i) Multi- compartmental model (top) ii) Previously proposed model and experimental data from literature [3] (bottom). A B Monte Carlo Cell (MCell) and Dreamm MCell was used to model the unfolding dynamics of a caveola and to study its effects on cAMP production [6]. MCell was used to model the unfolding dynamics of a caveola and to study its effects on cAMP production [6]. Results from electron microscopy images [7] were used to approximate the size of a caveola and for simplicity, the caveola and cell were modeled as boxes. Results from electron microscopy images [7] were used to approximate the size of a caveola and for simplicity, the caveola and cell were modeled as boxes. Reactions were defined for the caveola and using specialized Monte Carlo algorithms, MCell simulated the movements and reactions of molecules at the subcellular and cellular level. Reactions were defined for the caveola and using specialized Monte Carlo algorithms, MCell simulated the movements and reactions of molecules at the subcellular and cellular level. The resulting three-dimensional reaction-diffusion system was simulated in Dreamm. The resulting three-dimensional reaction-diffusion system was simulated in Dreamm. Figure 3: A) Mesh wireframe networks of the world (extracellular space), caveola, and cell, B) Molecules rendered intracellularly and extracellularly. Figure 4: Images of the dynamic unfolding of a caveola. Figure 4: Transmembrane complexes modeled in randomized locations of the caveola BA