STEPS

STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python. We describe how the use of the Python language improved the user interface of the program STEPS. STEPS is a simulation platform for modeling and stochastic simulation of coupled reaction-diffusion systems with complex 3-dimensional boundary conditions. Setting up such models is a complicated process that consists of many phases. Initial versions of STEPS relied on a static input format that did not cleanly separate these phases, limiting modelers in how they could control the simulation and becoming increasingly complex as new features and new simulation algorithms were added. We solved all of these problems by tightly integrating STEPS with Python, using SWIG to expose our existing simulation code.


References in zbMATH (referenced in 18 articles )

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  1. Kang, Hye-Won; Erban, Radek: Multiscale stochastic reaction-diffusion algorithms combining Markov chain models with stochastic partial differential equations (2019)
  2. Sayyidmousavi, Alireza; Rohlf, Katrin; Ilie, Silvana: A hybrid method for micro-mesoscopic stochastic simulation of reaction-diffusion systems (2019)
  3. Thanh, Vo Hong: A critical comparison of rejection-based algorithms for simulation of large biochemical reaction networks (2019)
  4. Isaacson, Samuel A.; Zhang, Ying: An unstructured mesh convergent reaction-diffusion master equation for reversible reactions (2018)
  5. Engblom, Stefan; Hellander, Andreas; Lötstedt, Per: Multiscale simulation of stochastic reaction-diffusion networks (2017)
  6. Lin, Zhongwei; Tropper, Carl; McDougal, Robert A.; Ishlam Patoary, Mohammand Nazrul; Lytton, William W.; Yao, Yiping; Hines, Michael L.: Multithreaded stochastic PDES for reactions and diffusions in neurons (2017)
  7. Meinecke, Lina: Multiscale modeling of diffusion in a crowded environment (2017)
  8. Blanc, Emilie; Engblom, Stefan; Hellander, Andreas; Lötstedt, Per: Mesoscopic modeling of stochastic reaction-diffusion kinetics in the subdiffusive regime (2016)
  9. Del Razo, Mauricio J.; Qian, Hong: A discrete stochastic formulation for reversible bimolecular reactions via diffusion encounter (2016)
  10. 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
  11. Meinecke, Lina; Engblom, Stefan; Hellander, Andreas; Lötstedt, Per: Analysis and design of jump coefficients in discrete stochastic diffusion models (2016)
  12. Meinecke, Lina; Lötstedt, Per: Stochastic diffusion processes on Cartesian meshes (2016)
  13. Lötstedt, Per; Meinecke, Lina: Simulation of stochastic diffusion via first exit times (2015)
  14. Agbanusi, I. C.; Isaacson, S. A.: A comparison of bimolecular reaction models for stochastic reaction-diffusion systems (2014)
  15. Erban, Radek; Flegg, Mark B.; Papoian, Garegin A.: Multiscale stochastic reaction-diffusion modeling: application to actin dynamics in filopodia (2014)
  16. Ewald, Roland; Uhrmacher, Adelinde M.: SESSL: a domain-specific language for simulation experiments (2014)
  17. Wang, Siyang; Elf, Johan; Hellander, Stefan; Lötstedt, Per: Stochastic reaction-diffusion processes with embedded lower-dimensional structures (2014)
  18. De Schutter, Erik: The importance of stochastic signaling processes in the induction of long-term synaptic plasticity (2013) ioport