Virtual Cell: computational tools for modeling in cell biology - PubMed (original) (raw)

Review

. 2012 Mar-Apr;4(2):129-40.

doi: 10.1002/wsbm.165. Epub 2011 Dec 2.

Affiliations

Review

Virtual Cell: computational tools for modeling in cell biology

Diana C Resasco et al. Wiley Interdiscip Rev Syst Biol Med. 2012 Mar-Apr.

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.

Copyright © 2011 Wiley Periodicals, Inc.

PubMed Disclaimer

Figures

Figure 1

Figure 1

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 _s_ij area _A_i of the Voronoi cell and the distances _d_ij between the natural neighbors. In terms of these parameters, the spatially discretized diffusion equation takes the form, Ai∂tUi=∑j∈G(i)D(Uj−Ui)sijsym/dijsym where _U_i are the concentration values at the surface grid points, D is the diffusion coefficient, sijsym=(sij+sij)/2 and dijsym=(dij+dij)/2.

Figure 2

Figure 2

FLIP experiments with fibroblasts expressing GFP-Rac (adapted from (20)). (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.

Figure 3

Figure 3

Simulation of a 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 3). 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.

Figure 4

Figure 4

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

Figure 5

Figure 5

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.

Figure 6

Figure 6

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.

Figure 7

Figure 7

A simple version of the local excitation - global inhibition (LEGI) model (30, 31): (a) simulation geometry and (b) steady-state concentration gradients of the active form of a protein, _P_a, in arbitrary units.

Similar articles

Cited by

References

    1. Loew LM, Schaff JC. The Virtual Cell: a software environment for computational cell biology. Trends Biotechnol. 2001;19:401–6. - PubMed
    1. Moraru II, Schaff JC, Slepchenko BM, Loew LM. The virtual cell: an integrated modeling environment for experimental and computational cell biology. Ann N Y Acad Sci. 2002;971:595–6. - PubMed
    1. Moraru II, Schaff JC, Slepchenko BM, Blinov ML, Morgan F, Lakshminarayana A, et al. Virtual Cell modelling and simulation software environment. IET Syst Biol. 2008;2:352–62. - PMC - PubMed
    1. Slepchenko BM, Schaff JC, Carson JH, Loew LM. Computational cell biology: spatiotemporal simulation of cellular events. Annu Rev Biophys Biomol Struct. 2002;31:423–41. - PubMed
    1. Slepchenko BM, Loew LM. Use of Virtual Cell in studies of cellular dynamics. Int Rev Cell Mol Biol. 2010;283:1–56. - PMC - PubMed

Publication types

MeSH terms

LinkOut - more resources