Smoldyn

Smoldyn is a computer program for cell-scale biochemical simulations. It simulates each molecule of interest individually to capture natural stochasticity and to yield nanometer-scale spatial resolution. It treats other molecules implicitly, enabling it to simulate hundreds of thousands of molecules over several minutes of real time. Simulated molecules diffuse, react, are confined by surfaces, and bind to membranes much as they would in a real biological system. Smoldyn is easy to use and easy to install. It is more accurate and faster than other particle-based simulators. Smoldyn’s unique features include: a ”virtual experimenter” who can manipulate or measure the simulated system, support for spatial compartments, molecules with excluded volume, and simulations in 1, 2, or 3 dimensions.


References in zbMATH (referenced in 18 articles )

Showing results 1 to 18 of 18.
Sorted by year (citations)

  1. Erban, Radek: Coarse-graining molecular dynamics: stochastic models with non-Gaussian force distributions (2020)
  2. Kang, Hye-Won; Erban, Radek: Multiscale stochastic reaction-diffusion algorithms combining Markov chain models with stochastic partial differential equations (2019)
  3. Smith, Stephen; Grima, Ramon: Spatial stochastic intracellular kinetics: a review of modelling approaches (2019)
  4. Alfonso Landeros, Timothy Stutz, Kevin L. Keys, Alexander Alekseyenko, Janet S. Sinsheimer, Kenneth Lange, Mary Sehl: BioSimulator.jl: Stochastic simulation in Julia (2018) arXiv
  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. Chapman, S. Jonathan; Erban, Radek; Isaacson, Samuel A.: Reactive boundary conditions as limits of interaction potentials for Brownian and Langevin dynamics (2016)
  9. Dobramysl, Ulrich; Rüdiger, Sten; Erban, Radek: Particle-based multiscale modeling of calcium puff dynamics (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. Flegg, Mark B.: Smoluchowski reaction kinetics for reactions of any order (2016)
  12. Meinecke, Lina; Engblom, Stefan; Hellander, Andreas; Lötstedt, Per: Analysis and design of jump coefficients in discrete stochastic diffusion models (2016)
  13. Flegg, Mark B.; Hellander, Stefan; Erban, Radek: Convergence of methods for coupling of microscopic and mesoscopic reaction-diffusion simulations (2015)
  14. Lötstedt, Per; Meinecke, Lina: Simulation of stochastic diffusion via first exit times (2015)
  15. Montes, Jesús; LaTorre, Antonio; Muelas, Santiago; Merchán-Pérez, Ángel; Peña, José M.: Comparative study of metaheuristics for the curve-fitting problem: modeling neurotransmitter diffusion and synaptic receptor activation (2015)
  16. Karamitros, M.; Luan, S.; Bernal, M. A.; Allison, J.; Baldacchino, G.; Davidkova, M.; Francis, Z.; Friedland, W.; Ivantchenko, V.; Ivantchenko, A.; Mantero, A.; Nieminem, P.; Santin, G.; Tran, H. N.; Stepan, V.; Incerti, S.: Diffusion-controlled reactions modeling in Geant4-DNA (2014)
  17. Wang, Siyang; Elf, Johan; Hellander, Stefan; Lötstedt, Per: Stochastic reaction-diffusion processes with embedded lower-dimensional structures (2014)
  18. Hellander, Stefan; Lötstedt, Per: Flexible single molecule simulation of reaction-diffusion processes (2011)


Further publications can be found at: http://www.smoldyn.org/publications.html