A parallel and distributed discrete event approach for spatial cell-biological simulations
ACM SIGMETRICS Performance Evaluation Review
Parallel and Distributed Spatial Simulation of Chemical Reactions
Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
Exploring the performance of spatial stochastic simulation algorithms
Journal of Computational Physics
Visual analytics for stochastic simulation in cell biology
i-KNOW '11 Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
Trading Computation Time for Synchronization Time in Spatial Distributed Simulation
PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
The Relevance of Topology in Parallel Simulation of Biological Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Many biochemical networks involve reactions localized on the cell membrane. This can give rise to spatial gradients of the concentration of cytosolic species. Moreover, the number of membrane molecules can be small and stochastic effects can become relevant. Pathways usually consist of a complex interaction network and are characterized by a large set of parameters. The inclusion of spatial and stochastic effects is a major challenge in developing quantitative and dynamic models of pathways. Results: We have developed a particle-based spatial stochastic method (GMP) to simulate biochemical networks in space, including fluctuations from the diffusion of particles and reactions. Gradients emerging from membrane reactions can be resolved. As case studies for the GMP method we used a simple gene expression system and the phosphoenolpyruvate:glucose phosphotransferase system pathway. Availability: The source code for the GMP method is available at http://www.science.uva.nl/research/scs/CellMath/GMP Contact: jrodrigu@science.uva.nl