If you are using BioNetGen software, please check updates page often for new versions with extended capabilities. The latest version released 07/12/04.

May, 2005 M. L. Blinov, J. R. Faeder, B. Goldstein, W. S. Hlavacek. (2005) A network model of Early Events in Epidermal Growth Factor Receptor Signaling that Accounts for Combinatorial Complexity. (in press) BioSystems. [Reprint (pdf)] Abstract. We consider a model of early events in signaling by the epidermal growth factor (EGF) receptor (EGFR). The model includes EGF, EGFR, the adapter proteins Grb2 and Shc, and the guanine nucleotide exchange factor Sos, which is activated through EGF-induced formation of EGFR-Grb2-Sos and EGFR-Shc-Grb2-Sos assemblies at the plasma membrane. The protein interactions involved in signaling can potentially generate a diversity of protein complexes and phosphoforms; however, this diversity has been largely ignored in models of EGFR signaling. Here, we develop a model that accounts more fully for potential molecular diversity by specifying rules for protein interactions and then using these rules to generate a reaction network that includes all chemical species and reactions implied by the protein interactions. We obtain a model that predicts the dynamics of 356 molecular species, which are connected through 3,749 unidirectional reactions. This network model is compared with a previously developed model that includes only 18 chemical species but incorporates the same scope of protein interactions. The predictions of this model are reproduced by the network model, which also yields new predictions. For example, the network model predicts distinct temporal patterns of autophosphorylation for different tyrosine residues of EGFR. A comparison of the two models suggests experiments that could lead to mechanistic insights about competition among adapter proteins for EGFR binding sites and the role of EGFR monomers in signal transduction.

March-April, 2005 J. R. Faeder*, M. L. Blinov*, B. Goldstein, W. S. Hlavacek. (2005) Rule-based modeling of biochemical networks. Complexity 10, 22-41. [Reprint (pdf)] * These two authors contributed equally Abstract. We present a method for generating a biochemical reaction network from a description of the interactions of components of biomolecules. The interactions are specified in the form of reaction rules, each of which defines a class of reaction associated with a type of interaction. Reactants within a class have shared properties, which are specified in the rule defining the class. A rule also provides a rate law, which governs each reaction in a class, and a template for transforming reactants into products. A set of reaction rules can be applied to a seed set of chemical species and, subsequently, any new species that are found as products of reactions to generate a list of reactions and a list of the chemical species that participate in these reactions, i.e., a reaction network, which can be translated into a mathematical model.

March, 2005 J. R. Faeder, M. L. Blinov, W. S. Hlavacek, B. Goldstein. (2005) Combinatorial complexity and dynamical restictions of network flows in signal transduction. Syst. Biol. 2, 5-15 [Reprint (pdf)] Abstract. The activities and interactions of proteins that govern the cellular response to a signal generate a multitude of protein phosphorylation states and heterogeneous protein complexes. Here, using a computational model that accounts for 307 molecular species implied by specified interactions of four proteins involved in signalling by the immunoreceptor Fc&epsilonRI, we determine the relative importance of molecular species that can be generated during signalling, chemical transitions among these species, and reaction paths that lead to activation of the protein tyrosine kinase (PTK) Syk. By all of these measures and over 2- and 10-fold ranges of model parameters--rate constants and initial concentrations--only a small portion of the biochemical network is active. The spectrum of active complexes, however, can be shifted dramatically, even by a change in the concentration of a single protein, which suggests that the network can produce qualitatively different responses under different cellular conditions and in response to different inputs. Reduced models that reproduce predictions of the full model for a particular set of parameters lose their predictive capacity when parameters are varied over 2-fold ranges.

June, 2004 M. L. Blinov, J. R. Faeder, B. Goldstein, W. S. Hlavacek. (2004) BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20, 3289-3291. [Reprint (pdf)] Abstract. 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.

June, 2004 Byron Goldstein, James R. Faeder and William S. Hlavacek (2004) MATHEMATICAL AND COMPUTATIONAL MODELS OF IMMUNE-RECEPTOR SIGNALLING. Nat. Rev. Immunol. 4, 445-456. [Reprint (pdf)] Abstract. The process of signalling through receptors of the immune system involves highly connected networks of interacting components. Understanding the often counter-intuitive behaviour of these networks requires the development of mathematical and computational models. Here, we focus on the application of these models to understand signalling through immune receptors that are involved in antigen recognition. Simple models, which ignore the details of the signalling machinery, have provided considerable insight into how ligand receptor binding properties affect signalling outcomes. Detailed models, which include specific molecular components and interactions beyond the ligand and receptor, are difficult to develop but have already provided new mechanistic understanding and uncovered relationships that are difficult to detect by experimental observation alone. They offer hope that models might eventually predict the full spectrum of signalling behaviour.

February, 2003 J. R. Faeder, W. S. Hlavacek, I. Reischl, M. L. Blinov, H. Metzger, A. Redondo, C. Wofsy, and B. Goldstein. (2003) Investigation of early events in FceRI-mediated signaling using a detailed mathematical model. J. Immunol. 170, 3769-81. [Reprint (pdf)] Abstract. Aggregation of Fc epsilon RI on mast cells and basophils leads to autophosphorylation and increased activity of the cytosolic protein tyrosine kinase Syk. We investigated the roles of the Src kinase Lyn, the immunoreceptor tyrosine-based activation motifs (ITAMs) on the beta and gamma subunits of Fc epsilon RI, and Syk itself in the activation of Syk. Our approach was to build a detailed mathematical model of reactions involving Fc epsilon RI, Lyn, Syk, and a bivalent ligand that aggregates Fc(epsilon)RI. We applied the model to experiments in which covalently cross-linked IgE dimers stimulate rat basophilic leukemia cells. The model makes it possible to test the consistency of mechanistic assumptions with data that alone provide limited mechanistic insight. For example, the model helps sort out mechanisms that jointly control dephosphorylation of receptor subunits. In addition, interpreted in the context of the model, experimentally observed differences between the beta- and gamma-chains with respect to levels of phosphorylation and rates of dephosphorylation indicate that most cellular Syk, but only a small fraction of Lyn, is available to interact with receptors. We also show that although the beta ITAM acts to amplify signaling in experimental systems where its role has been investigated, there are conditions under which the beta ITAM will act as an inhibitor.