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 50 articles )

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  1. 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)
  2. Alvarez, Robinson F.; Barbuto, José A. M.; Venegeroles, Roberto: A nonlinear mathematical model of cell-mediated immune response for tumor phenotypic heterogeneity (2019)
  3. Boreale, Michele: Algebra, coalgebra, and minimization in polynomial differential equations (2019)
  4. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Comparing chemical reaction networks: a categorical and algorithmic perspective (2019)
  5. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Symbolic computation of differential equivalences (2019)
  6. 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
  7. Honorato-Zimmer, Ricardo; Millar, Andrew J.; Plotkin, Gordon D.; Zardilis, Argyris: Chromar, a language of parameterised agents (2019)
  8. Khetan, Jawahar; Barua, Dipak: Analysis of Fn14-NF-(\kappa)B signaling response dynamics using a mechanistic model (2019)
  9. Shin, Seung Woo; Thachuk, Chris; Winfree, Erik: Verifying chemical reaction network implementations: a pathway decomposition approach (2019)
  10. 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)
  11. Alfonso Landeros, Timothy Stutz, Kevin L. Keys, Alexander Alekseyenko, Janet S. Sinsheimer, Kenneth Lange, Mary Sehl: BioSimulator.jl: Stochastic simulation in Julia (2018) arXiv
  12. Bazzazi, Hojjat; Zhang, Yu; Jafarnejad, Mohammad; Popel, Aleksander S.: Computational modeling of synergistic interaction between (\alpha)V(\beta)3 integrin and VEGFR2 in endothelial cells: implications for the mechanism of action of angiogenesis-modulating integrin-binding peptides (2018)
  13. Fernández, Maribel; Kirchner, Hélène; Pinaud, Bruno: Labelled port graph -- a formal structure for models and computations (2018)
  14. Boutillier, Pierre; Ehrhard, Thomas; Krivine, Jean: Incremental update for graph rewriting (2017)
  15. Helms, Tobias; Warnke, Tom; Maus, Carsten; Uhrmacher, Adelinde M.: Semantics and efficient simulation algorithms of an expressive multilevel modeling language (2017)
  16. Mohammed, Abdulmelik; Czeizler, Elena; Czeizler, Eugen: Computational modelling of the kinetic tile assembly model using a rule-based approach (2017)
  17. Blanc, Emilie; Engblom, Stefan; Hellander, Andreas; Lötstedt, Per: Mesoscopic modeling of stochastic reaction-diffusion kinetics in the subdiffusive regime (2016)
  18. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Symbolic computation of differential equivalences (2016)
  19. Děd, T.; Šafránek, D.; Troják, M.; Klement, M.; Šalagovič, J.; Brim, L.: Formal biochemical space with semantics in Kappa and BNGL (2016)
  20. Eftimie, Raluca; Gillard, Joseph J.; Cantrell, Doreen A.: Mathematical models for immunology: current state of the art and future research directions (2016)

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