BioNetGen

BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. BioNetGen allows a user to create a computational model that characterizes the dynamics of a signal transduction system, and that accounts comprehensively and precisely for specified enzymatic activities, potential post-translational modifications and interactions of the domains of signaling molecules. The output defines and parameterizes the network of molecular species that can arise during signaling and provides functions that relate model variables to experimental readouts of interest. Models that can be generated are relevant for rational drug discovery, analysis of proteomic data and mechanistic studies of signal transduction.


References in zbMATH (referenced in 35 articles )

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  1. Boutillier, Pierre; Ehrhard, Thomas; Krivine, Jean: Incremental update for graph rewriting (2017)
  2. Helms, Tobias; Warnke, Tom; Maus, Carsten; Uhrmacher, Adelinde M.: Semantics and efficient simulation algorithms of an expressive multilevel modeling language (2017)
  3. Mohammed, Abdulmelik; Czeizler, Elena; Czeizler, Eugen: Computational modelling of the kinetic tile assembly model using a rule-based approach (2017)
  4. Blanc, Emilie; Engblom, Stefan; Hellander, Andreas; Lötstedt, Per: Mesoscopic modeling of stochastic reaction-diffusion kinetics in the subdiffusive regime (2016)
  5. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Symbolic computation of differential equivalences (2016)
  6. Eftimie, Raluca; Gillard, Joseph J.; Cantrell, Doreen A.: Mathematical models for immunology: current state of the art and future research directions (2016)
  7. Pantoja-Hernández, Libertad; Álvarez-Buylla, Elena; Aguilar-Ibáñez, Carlos F.; Garay-Arroyo, Adriana; Soria-López, Alberto; Martínez-García, Juan Carlos: Retroactivity effects dependency on the transcription factors binding mechanisms (2016)
  8. Vandin, Andrea; Tribastone, Mirco: Quantitative abstractions for collective adaptive systems (2016)
  9. Chiarugi, Davide; Falaschi, Moreno; Hermith, Diana; Olarte, Carlos: Verification of spatial and temporal modalities in biochemical systems (2015)
  10. Clermont, Gilles; Zenker, Sven: The inverse problem in mathematical biology (2015)
  11. Danos, Vincent; Harmer, Russell; Honorato-Zimmer, Ricardo: Thermodynamic graph-rewriting (2015)
  12. Feret, Jér^ome: An algebraic approach for inferring and using symmetries in rule-based models (2015)
  13. Tschaikowski, Max; Tribastone, Mirco: A unified framework for differential aggregations in Markovian process algebra (2015)
  14. Zunino, Roberto; Nikolić, \Djurica; Priami, Corrado; Kahramanoğulları, Ozan; Schiavinotto, Tommaso: $\ell $: an imperative DSL to stochastically simulate biological systems (2015)
  15. Danos, Vincent; Honorato-Zimmer, Ricardo; Jaramillo-Riveri, Sebastián; Stucki, Sandro: Coarse-graining the dynamics of ideal branched polymers (2014)
  16. Danos, Vincent; Honorato-Zimmer, Ricardo; Jaramillo-Riveri, Sebastián; Stucki, Sandro: Rigid geometric constraints for Kappa models (2014)
  17. Ganguly, Arnab; Petrov, Tatjana; Koeppl, Heinz: Markov chain aggregation and its applications to combinatorial reaction networks (2014)
  18. Degasperi, A.; Calder, M.: A process algebra framework for multi-scale modelling of biological systems (2013)
  19. Versari, Cristian; Zavattaro, Gianluigi: Complex functional rates in the modeling of nano devices (extended abstract) (2013) ioport
  20. Feret, Jerome; Henzinger, Thomas; Koeppl, Heinz; Petrov, Tatjana: Lumpability abstractions of rule-based systems (2012)

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