Bibliographic Details
| Title: |
Python-based geometry preparation and simulation visualization toolkits for STEPS. |
| Authors: |
Weiliang Chen, DeSchutter, Erik |
| Source: |
Frontiers in Neuroinformatics; Apr2014, p1-11, 11p |
| Subject Terms: |
DATA visualization, PYTHON programming language, STOCHASTIC processes, GEOMETRIC modeling, SIMULATION methods & models |
| Abstract: |
STEPS is a stochastic reaction-diffusion simulation engine that implements a spatial extension of Gillespie's Stochastic Simulation Algorithm (SSA) in complex tetrahedral geometries. An extensive Python-based interface is provided to STEPS so that it can interact with the large number of scientific packages in Python. However, a gap existed between the interfaces of these packages and the STEPS user interface, where supporting toolkits could reduce the amount of scripting required for research projects. This paper introduces two new supporting toolkits that support geometry preparation and visualization for STEPS simulations. [ABSTRACT FROM AUTHOR] |
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| Database: |
Biomedical Index |
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