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Monte Carlo cell simulations

DOI: 10.1186/gb-2001-3-1-reports2001

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Abstract:

The general Monte Carlo simulator of cellular microphysiology (MCell) on this site is a program that allows three-dimensional dynamic simulations of subcellular architecture and physiology. In addition to an introduction to the modeling of cellular physiology, details of probability and random-event-based methods and complex geometry generation are discussed. The MCell program can be downloaded after registration (free to academic users). The site provides a good introduction to researchers interested in using Monte Carlo Simulations (a random number based simulation method, with the physical process simulated directly by sampling a probability distribution function) to answer questions in cellular physiology and structure, as well as having much to offer for users of the MCell tool. There are links to other simulation tools such as NEURON and GENESIS and visualization tool sites such as Open Visualization Data Explorer and POV-Ray. The minimal nature of the site and the clear introduction to the simulation of microphysiology are its most striking features.Most parts of the site can be found easily and there are tutorials for its use. There are some ambiguous links, including example images. Once in any of the internal pages, there are no links to other pages, which makes moving around a bit tedious. Browsing is easy but customization is not possible. The site has a lot of large images, and on a low-bandwidth connection this might slow access down. Printing pages is not ideal, as text and images do not fit on standard pages, but text and images can be easily downloaded. The response to feedback is excellent and useful answers were obtained within a working day.There is no mention of when the site was last updated, but the new downloading facility for the program was added in October 2001.Monte Carlo simulation methods are not widely represented in cell-simulation websites, so the good overview of modeling in cell biology and the relevance of the Monte Carlo approach

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