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Virtual Cell: computational tools for modeling in cell biology

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Abstract The Virtual Cell (VCell) is a general computational framework for modeling physicochemical and electrophysiological processes in living cells. Developed by the National Resource for Cell Analysis and Modeling at the University of Connecticut Health Center, it provides automated tools for simulating a wide range of cellular phenomena in space and time, both deterministically and stochastically. These computational tools allow one to couple electrophysiology and reaction kinetics with transport mechanisms, such as diffusion and directed transport, and map them onto spatial domains of various shapes, including irregular three‐dimensional geometries derived from experimental images. In this article, we review new robust computational tools recently deployed in VCell for treating spatially resolved models. WIREs Syst Biol Med 2012, 4:129–140. doi: 10.1002/wsbm.165 This article is categorized under: Models of Systems Properties and Processes > Cellular Models Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Mechanistic Models

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Discretization of diffusion equation on a surface: A local subset of surface grid points is projected on the tangential plane orthogonal to the outward normal vector n at point i (left), and Voronoi tessellation is then applied to projections shown in red (right). This procedure automatically determines the natural neighbors of point i, along with necessary geometric parameters: the sides sij, area Ai of the Voronoi cell, and the distances dij between the natural neighbors. In terms of these parameters, the spatially discretized diffusion equation takes the form, , where Ui and Uj are the concentration values at the surface grid points, D is the diffusion coefficient, and ; G(i) is the set of indexes of natural neighbors of point i.

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Fluorescence loss in photobleaching (FLIP) experiments with fibroblasts expressing GFP‐Rac. (Reprinted with permission from Ref 20. Copyright 2006 The American Society for Cell Biology). (a) A representative cell, 30 min after replating on fibronectin. The red line delineates the photobleached area. (b) Cell in (a) at the indicated times during the photobleaching protocol.

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Simulation of a fluorescence loss in photobleaching (FLIP) experiment in VCell using geometry obtained from a z‐stack of confocal images (details of how image‐based geometries are generated in VCell are given below in Section Incorporating Image‐based Geometries and Experimental Data in VCell Models). The snapshot represents distribution of the membrane‐bound GFP‐Rac, in arbitrary units, shortly (0.5 s) after the start of photobleaching. For this time, surface density of the membrane‐bound Rac in the unbleached area remains close to maximum.

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VCell Geometry Editor (VCell Beta 5.0) includes tools for creating geometries both analytically and from experimental images.

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Segmentation of an imported image. The Image Geometry Editor window includes tools for adjusting an uploaded image [cropping (D), magnifying, and adjusting of brightness (C)], as well as tools for segmenting the image [(E), (F), (J), (K), and (H)] necessary for creating a valid VCell geometry.

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Field Data Manager display. The Field Data Manager tool allows a user to use irregular spatial distributions from experimental images (or simulation results) as initial conditions in VCell models.

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A simple version of the local excitation–global inhibition (LEGI) model30,31: (a) simulation geometry and (b) steady‐state concentration gradients of the active form of a protein, Pa, in arbitrary units.

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Models of Systems Properties and Processes > Cellular Models
Models of Systems Properties and Processes > Mechanistic Models
Analytical and Computational Methods > Computational Methods

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