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 60 articles )

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  1. Sego, T. J.; Mochan, Ericka D.; Ermentrout, G. Bard; Glazier, James A.: A multiscale multicellular spatiotemporal model of local influenza infection and immune response (2022)
  2. Kraikivski, Pavel: Computational software (2021)
  3. Behr, Nicolas: Tracelets and tracelet analysis of compositional rewriting systems (2020)
  4. Beica, Andreea; Feret, Jérôme; Petrov, Tatjana: Tropical abstraction of biochemical reaction networks with guarantees (2020)
  5. Boutillier, Pierre; Faure de Pebeyre, Aurélie; Feret, Jérôme: Proving the absence of unbounded polymers in rule-based models (2020)
  6. Cess, Colin G.; Finley, Stacey D.: Data-driven analysis of a mechanistic model of CAR T cell signaling predicts effects of cell-to-cell heterogeneity (2020)
  7. Leonard Schmiester, Yannik Schälte, Frank T. Bergmann, Tacio Camba, Erika Dudkin, Janine Egert, Fabian Fröhlich, Lara Fuhrmann, Adrian L. Hauber, Svenja Kemmer, Polina Lakrisenko, Carolin Loos, Simon Merkt, Wolfgang Müller, Dilan Pathirana, Elba Raimúndez, Lukas Refisch, Marcus Rosenblatt, Paul L. Stapor, Philipp Städter, Dantong Wang, Franz-Georg Wieland, Julio R. Banga, Jens Timmer, Alejandro F. Villaverde, Sven Sahle, Clemens Kreutz, Jan Hasenauer, Daniel Weindl: PEtab - interoperable specification of parameter estimation problems in systems biology (2020) arXiv
  8. Lüdtke, Stefan; Kirste, Thomas: Lifted Bayesian filtering in multiset rewriting systems (2020)
  9. Troják, Matej; Šafránek, David; Brim, Luboš; Šalagovič, Jakub; Červený, Jan: Executable biochemical space for specification and analysis of biochemical systems (2020)
  10. Vladyslav Oles; Anton Kukushkin: BoolSi: A Tool for Distributed Simulations and Analysis of Boolean Networks (2020) not zbMATH
  11. Wright, Thomas; Stark, Ian: Modelling patterns of gene regulation in the bond-calculus (2020)
  12. Alvarez, Robinson F.; Barbuto, José A. M.; Venegeroles, Roberto: A nonlinear mathematical model of cell-mediated immune response for tumor phenotypic heterogeneity (2019)
  13. Boreale, Michele: Algebra, coalgebra, and minimization in polynomial differential equations (2019)
  14. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Comparing chemical reaction networks: a categorical and algorithmic perspective (2019)
  15. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Symbolic computation of differential equivalences (2019)
  16. Eshan D. Mitra, Ryan Suderman, Joshua Colvin, Alexander Ionkov, Andrew Hu, Herbert M. Sauro, Richard G. Posner, William S. Hlavacek: PyBioNetFit and the Biological Property Specification Language (2019) arXiv
  17. Honorato-Zimmer, Ricardo; Millar, Andrew J.; Plotkin, Gordon D.; Zardilis, Argyris: Chromar, a language of parameterised agents (2019)
  18. Khetan, Jawahar; Barua, Dipak: Analysis of Fn14-NF-(\kappa)B signaling response dynamics using a mechanistic model (2019)
  19. Shin, Seung Woo; Thachuk, Chris; Winfree, Erik: Verifying chemical reaction network implementations: a pathway decomposition approach (2019)
  20. Suderman, Ryan; Mitra, Eshan D.; Lin, Yen Ting; Erickson, Keesha E.; Feng, Song; Hlavacek, William S.: Generalizing Gillespie’s direct method to enable network-free simulations (2019)

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