URDME

Urdme: a modular framework for stochastic simulation of reaction-transport processes in complex geometries. We have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses Unstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the Reaction-Diffusion Master Equation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Multiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possible to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at an early stage of development.


References in zbMATH (referenced in 25 articles )

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  1. Bartosz J. Bartmanski; Ruth E. Baker: StoSpa2: A C++ software package for stochastic simulations of spatially extended systems (2020) not zbMATH
  2. Engblom, Stefan: Stochastic simulation of pattern formation in growing tissue: a multilevel approach (2019)
  3. Lötstedt, Per: The linear noise approximation for spatially dependent biochemical networks (2019)
  4. Macnamara, Cicely K.; Mitchell, Elaine I.; Chaplain, Mark A. J.: Spatial-stochastic modelling of synthetic gene regulatory networks (2019)
  5. Smith, Stephen; Grima, Ramon: Spatial stochastic intracellular kinetics: a review of modelling approaches (2019)
  6. Chevallier, Augustin; Engblom, Stefan: Pathwise error bounds in multiscale variable splitting methods for spatial stochastic kinetics (2018)
  7. Isaacson, Samuel A.; Zhang, Ying: An unstructured mesh convergent reaction-diffusion master equation for reversible reactions (2018)
  8. Szymańska, Zuzanna; Cytowski, Maciej; Mitchell, Elaine; Macnamara, Cicely K.; Chaplain, Mark A. J.: Computational modelling of cancer development and growth: modelling at multiple scales and multiscale modelling (2018)
  9. Engblom, Stefan: Stability and strong convergence for spatial stochastic kinetics (2017)
  10. Engblom, Stefan; Hellander, Andreas; Lötstedt, Per: Multiscale simulation of stochastic reaction-diffusion networks (2017)
  11. Hellander, Andreas; Klosa, Jan; Lötstedt, Per; MacNamara, Shev: Robustness analysis of spatiotemporal models in the presence of extrinsic fluctuations (2017)
  12. Meinecke, Lina: Multiscale modeling of diffusion in a crowded environment (2017)
  13. Blanc, Emilie; Engblom, Stefan; Hellander, Andreas; Lötstedt, Per: Mesoscopic modeling of stochastic reaction-diffusion kinetics in the subdiffusive regime (2016)
  14. Del Razo, Mauricio J.; Qian, Hong: A discrete stochastic formulation for reversible bimolecular reactions via diffusion encounter (2016)
  15. Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas: MOLNs: a cloud platform for interactive, reproducible, and scalable spatial stochastic computational experiments in systems biology using pyurdme (2016) ioport
  16. Meinecke, Lina; Lötstedt, Per: Stochastic diffusion processes on Cartesian meshes (2016)
  17. Stefan Widgren, Pavol Bauer, Stefan Engblom: SimInf: An R package for Data-driven Stochastic Disease Spread Simulations (2016) arXiv
  18. Flegg, Mark B.; Hellander, Stefan; Erban, Radek: Convergence of methods for coupling of microscopic and mesoscopic reaction-diffusion simulations (2015)
  19. Lötstedt, Per; Meinecke, Lina: Simulation of stochastic diffusion via first exit times (2015)
  20. Agbanusi, I. C.; Isaacson, S. A.: A comparison of bimolecular reaction models for stochastic reaction-diffusion systems (2014)

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