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 26 articles )

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  1. Blanc, Emilie; Engblom, Stefan; Hellander, Andreas; Lötstedt, Per: Mesoscopic modeling of stochastic reaction-diffusion kinetics in the subdiffusive regime (2016)
  2. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Symbolic computation of differential equivalences (2016)
  3. 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)
  4. Chiarugi, Davide; Falaschi, Moreno; Hermith, Diana; Olarte, Carlos: Verification of spatial and temporal modalities in biochemical systems (2015)
  5. Clermont, Gilles; Zenker, Sven: The inverse problem in mathematical biology (2015)
  6. Danos, Vincent; Harmer, Russell; Honorato-Zimmer, Ricardo: Thermodynamic graph-rewriting (2015)
  7. Feret, Jér^ome: An algebraic approach for inferring and using symmetries in rule-based models (2015)
  8. Tschaikowski, Max; Tribastone, Mirco: A unified framework for differential aggregations in Markovian process algebra (2015)
  9. Danos, Vincent; Honorato-Zimmer, Ricardo; Jaramillo-Riveri, Sebastián; Stucki, Sandro: Coarse-graining the dynamics of ideal branched polymers (2014)
  10. Danos, Vincent; Honorato-Zimmer, Ricardo; Jaramillo-Riveri, Sebastián; Stucki, Sandro: Rigid geometric constraints for Kappa models (2014)
  11. Ganguly, Arnab; Petrov, Tatjana; Koeppl, Heinz: Markov chain aggregation and its applications to combinatorial reaction networks (2014)
  12. Degasperi, A.; Calder, M.: A process algebra framework for multi-scale modelling of biological systems (2013)
  13. Versari, Cristian; Zavattaro, Gianluigi: Complex functional rates in the modeling of nano devices (extended abstract) (2013)
  14. Feret, Jerome; Henzinger, Thomas; Koeppl, Heinz; Petrov, Tatjana: Lumpability abstractions of rule-based systems (2012)
  15. Klinke, David J.; Wang, Qing: Understanding immunology via engineering design: the role of mathematical prototyping (2012)
  16. Kocieniewski, PaweŁ; Faeder, James R.; Lipniacki, Tomasz: The interplay of double phosphorylation and scaffolding in MAPK pathways (2012)
  17. Thompson-Walsh, C.D.; Hayman, J.; Winskel, G.: Containment in rule-based models (2012)
  18. Camporesi, Ferdinanda; Feret, Jér^ome: Formal reduction for rule-based models (2011)
  19. De Maria, Elisabetta; Fages, François; Rizk, Aurélien; Soliman, Sylvain: Design, optimization and predictions of a coupled model of the cell cycle, circadian clock, DNA repair system, irinotecan metabolism and exposure control under temporal logic constraints (2011)
  20. Pedersen, Michael: A syntactic abstraction for rule-based languages with binding (2011)

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