BioNetGen rule-based modeling references

Introduction to rule-based modeling

Signal transduction networks often exhibit combinatorial complexity: the number of protein complexes and modification states that potentially can be generated during the response to a signal is large, because signaling proteins contain multiple sites of modification and interact with multiple binding partners. The conventional approach of manually specifying each species and reactions becomes impossible. An alternative to the conventional approach is a rule-based description, where the system is specified by a set of rules that serve as generators for species and reactions.

  • Mayer et al, 2009 [PMID: 19835637| pdf] - Molecular Machines or Pleiomorphic Ensembles
  • Hlavacek et al, 2009 [PMID: 19638613] - The complexity of cell signaling and the need for a new mechanics
  • Hlavacek et al, 2006 [PMID: 16849649| pdf] - Rules for modeling signal-transduction systems.
  • Hlavacek et al, 2003 [PMID: 14708119 | pdf] - The complexity of complexes in signal transduction.

Examples of rule-based models

BioNetGen is used for detailed mechanistic modeling of signal transduction network by describing activities and interactions among domains of biomolecules (e.g. phosphorylation of specific tyrosine residues, interactions between SH2 domain and phosphotyrosine).

  • Nag et al., 2011 [PMID: 20733205] A detailed mathematical model of serial engagement of IgE-FceRI
  • Barua et al, 2009 [PMID: 19381268] Activation of Jak-family protein tyrosine kinases.
  • Nag et al, 2008 [PMID: 19348745] LAT-Grb2-SOS1 interactions
  • Lipniacki et al, 2008 [PMID: 18556025] - T cell receptor signaling
  • Barua et al, 2008 [PMID: 18204097] - Tandem SH2 interactions with p85 kinase
  • Barua et al, 2007 [PMID: 17208977] - Structure-based kinetic model of Shp2 interactions.
  • Mu et al, 2007 [PMID: 17933853] - Carbon-fate maps for metabolic reactions.
  • Blinov et al, 2006 [PMID: 16233948| pdf] - Early events in epidermal growth factor receptor signaling
  • Faeder et al, 2005 [PMID: 17091578 | pdf] - Identifying subnetworks in in FcεRI-mediated signaling .
  • Faeder et al, 2003 [PMID: 12646643| pdf] - Early events in FcεRI-mediated signaling
  • Goldstein et al, 2002 [PMID: 12217386| pdf] - Early events in FcεRI-mediated signaling

BioNetGen software for rule-based modeling

  • Faeder et al, 2009 [PMID: 19399430| pdf] - Rule-Based Modeling of Biochemical Systems with BioNetGen
  • Colvin et al, 2009 [PMID: 19213740 ] - Simulation of large-scale rule-based models.
  • Yang et al, 2008 [PMID: 18851068 ] - Kinetic Monte Carlo method for rule-based modeling of biochemical networks.
  • Blinov et al, 2006 [doi:10.1007/11905455_5| pdf] - Graph theory for rule-based modeling of biochemical networks.
  • Blinov et al, 2005 [PMID: 16273053| pdf] - `On-the-fly' or `generate-first' modeling?
  • Faeder et al, 2005 [doi:10.1002/cplx.v10:4 | pdf] - Rule-based modeling of biochemical networks
  • Blinov et al, 2004 [PMID: 15217809 |pdf] - Description of BioNetGen software

Rule-based models storage and visualization

A large mechanistic model (accounting for many species and activities and interactions among domains of biomolecules) is very difficult to store, visualize, or modify. The standard way of storage is in electronic exchange formats like (SBML). SBML file specifies each of individual species and interactions, but carries no information about domains of proteins and composition of multi-protein species. Simulation and visualization tools (such as CellDesigner, VCell etc) display each species and interaction separately, making representation very cluttered. An alternative is specifying rule-based description of the system, sufficient to restore the complete model.