Dizzy

Dizzy is a tool for stochastic and deterministic simulation of chemical reactions. Developed by the Bolouri institute in Seattle, this version of the Dizzy tool has been extended at the Laboratory for Foundations of Computer Science, Edinburgh. The Edinburgh version extends the original with implementations of faster stochastic simulation algorithms such as the optimised direct method, the sorting direct method, and the logarithmic direct method. Users can compare the effectiveness of the stochastic simulators on the problems which interest them. As alternatives to stochastic simulation, users can solve their models using both ordinary and stochastic differential equation solvers. In addition, Dizzy allows users to perform sensitivity analysis on their models, in order to determine which parameters have the greatest impact on the model kinetics. The Dizzy software is freely available for download as Java source code and runs on many hardware and software platforms.


References in zbMATH (referenced in 18 articles )

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  1. Kadelka, Sarah; Boribong, Brittany P.; Li, Liwu; Ciupe, Stanca M.: Modeling the bistable dynamics of the innate immune system (2019)
  2. Roussel, Marc R.: A delayed mass-action model for the transcriptional control of Hmp, an NO detoxifying enzyme, by the iron-sulfur protein FNR (2019)
  3. Paul, Debdas; Radde, Nicole: The role of stochastic sequestration dynamics for intrinsic noise filtering in signaling network motifs (2018)
  4. Dayar, T.; Hermanns, H.; Spieler, D.; Wolf, V.: Bounding the equilibrium distribution of Markov population models. (2011)
  5. John, Mathias; Lhoussaine, Cédric; Niehren, Joachim; Versari, Cristian: Biochemical reaction rules with constraints (2011)
  6. Kuttler, Céline; Lhoussaine, Cédric; Nebut, Mirabelle: Rule-based modeling of transcriptional attenuation at the tryptophan operon (2010)
  7. Ribeiro, Andre S.: Stochastic and delayed stochastic models of gene expression and regulation (2010)
  8. Bortolussi, Luca; Policriti, Alberto: Dynamical systems and stochastic programming: to ordinary differential equations and back (2009)
  9. Chu, Dominique; Zabet, Nicolae Radu; Mitavskiy, Boris: Models of transcription factor binding: sensitivity of activation functions to model assumptions (2009)
  10. Ciocchetta, Federica; Hillston, Jane: Bio-PEPA: A framework for the modelling and analysis of biological systems (2009)
  11. Heath, Allison P.; Kavraki, Lydia E.: Computational challenges in systems biology (2009)
  12. Bracciali, Andrea; Brunelli, Marcello; Cataldo, Enrico; Degano, Pierpaolo: Synapses as stochastic concurrent systems (2008)
  13. Ciocchetta, Federica; Hillston, Jane; Kos, Martin; Tollervey, David: Modelling co-transcriptional cleavage in the synthesis of yeast pre-rRNA (2008)
  14. Thomas, Nigel: Comparing job allocation schemes where service demand is unknown (2008)
  15. Chu, Dominique; Blomfield, Ian C.: Orientational control is an efficient control mechanism for phase switching in the \textitE. coli fim system (2007)
  16. Zhu, Rui; Ribeiro, Andre S.; Salahub, Dennis; Kauffman, Stuart A.: Studying genetic regulatory networks at the molecular level: delayed reaction stochastic models (2007)
  17. Ramsey, Stephen; Orrell, David; Bolouri, Hamid: Dizzy: Stochastic simulation of large-scale genetic regularoty networks. (Supplementary material) (2005) ioport
  18. Ramsey, Stephen; Orrell, David; Bolouri, Hamid: Dizzy: stochastic simulation of large-scale genetic regularoty networks (2005) ioport