This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Major challenges of the post-genomics era include a detailed understanding of structure/function relationships for macromolecules and macromolecular assemblies, the functional dynamics of complex biochemical signaling and metabolic networks, and the functional impact of complex structural architectures at subcellular, cellular, and/or multicellular levels. Computational modeling and simulation are increasingly important to such studies, and require increasingly powerful approaches that can incorporate highly realistic spatial detail and stochastic chemical interactions, and extend across broad ranges of space and time. Together with colleagues at the Salk Institute (Thomas M. Bartol and Terrence J. Sejnowski, Computational Neurobiology Laboratory), we are developing and applying methods for large-scale 3-D reconstructions and realistic microphysiological simulations, the latter based on Monte Carlo algorithms (MCell, www.mcell.psc.edu). In brief, MCell simulations can use high-resolution 3-D polygon meshes to represent curved cell and organelle membranes, and the defined structures and diffusion space(s) then can be populated with molecules that interact probabilistically (Stiles et al., 2001). Diffusing molecules move by means of a Brownian Dynamics random walk, and tests for all possible molecular reaction transitions (e.g., binding, unbinding, conformational changes) use Monte Carlo probabilities derived from bulk solution rate constants (Stiles and Bartol, 2001). Thus, details about individual molecular structure are ignored (making computation feasible), but the functional impact of individual molecular positions and densities within realistic subcellular topologies are included explicitly. Stiles, JR, and Bartol, TM. 2001. Monte Carlo methods for simulating realistic synaptic microphysiology using MCell. In: Computational Neuroscience: Realistic Modeling for Experimentalists, ed. De Schutter, E. CRC Press, Boca Raton, pp. 87-127. Stiles, JR, Bartol, TM, Salpeter, MM, Salpeter, EE, and Sejnowski, TJ. 2001. Synaptic variability: new insights from reconstructions and Monte Carlo simulations with MCell. In: Synapses, ed. Cowan, WM, Stevens, CF, and Sudhof, TC. Johns Hopkins Univ. Press, Baltimore, pp. 681-731.
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