BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains

Blinov, Michael L.; Faeder, James R.; Goldstein, Byron; Hlavacek, William S.

doi:10.1093/bioinformatics/bth378

Cited by 386 publications

(392 citation statements)

References 11 publications

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“…YPM also contains a synonymous embedded model of the pheromone system encoded in the BioNetGen language (27). We manually simplified the full pheromone response system model encoded in the YPM to eliminate molecules and reaction rules that were not directly involved in the MAPK cascade (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Inclusion of Ste5 dimerization would have increased the number of species in the model from 236 to over 20,000, dramatically slowing down simulation and hindering parameter estimation. We wrote MATLAB scripts that used a genetic algorithm (Genetic Algorithm and Direct Search Toolbox, v2.1, R2007a; MathWorks) to perform parameter optimization and execute BioNetGen (version 2.0.46) (27) simulations of the models (SI Materials and Methods). We varied abundances over the wide range of 10-10 6 molecules to explore the full potential of the pathway architecture rather than limiting ourselves to abundances that could easily be achieved experimentally.…”

Section: Measurement Of Fluorescent Protein Fusions and Reporter Genementioning

confidence: 99%

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Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

Thomson

Benjamin

Bush

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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Although the proteins comprising many signaling systems are known, less is known about their numbers per cell. Existing measurements often vary by more than 10-fold. Here, we devised improved quantification methods to measure protein abundances in the Saccharomyces cerevisiae pheromone response pathway, an archetypical signaling system. These methods limited variation between independent measurements of protein abundance to a factor of two. We used these measurements together with quantitative models to identify and investigate behaviors of the pheromone response system sensitive to precise abundances. The difference between the maximum and basal signaling output (dynamic range) of the pheromone response MAPK cascade was strongly sensitive to the abundance of Ste5, the MAPK scaffold protein, and absolute system output depended on the amount of Fus3, the MAPK. Additional analysis and experiment suggest that scaffold abundance sets a tradeoff between maximum system output and system dynamic range, a prediction supported by recent experiments.quantitative immunoblotting | single-cell fluorescence quantification | genetic algorithmic model optimization T he Saccharomyces cerevisiae pheromone response system has been useful for understanding eukaryotic cell signaling (1-3). In haploid cells, the pheromone response system detects mating pheromone in the extracellular environment secreted by yeast of the opposite mating type. The system triggers a number of responses, including induction of gene expression, arrest in cell cycle progression, changes in morphology, and eventually, mating. The molecules and interactions by which the system operates are relatively well-understood (Fig. 1).Changes in the number of molecules per cell (hereafter called abundances) of signaling proteins can alter quantitative signaling behaviors. For example, in the pheromone response system, overexpression of Fus3 increases pheromone-induced sensitivity to cell cycle arrest (4), and overexpression of Gpa1 decreases pheromone-induced transcription (5). Similarly, changes in the abundances of MAPK cascade scaffold proteins (Ste5 in the yeast pheromone response system) ( Fig. 1) can alter system behavior. In models, when scaffold is limiting, increasing scaffold abundance first increases system output and then, eventually sequesters the associated protein kinases onto separate scaffolds, diminishing the number of fully assembled complexes and system output (6). This peak and decline behavior has been experimentally shown for Ste5 in this yeast system (7) and the Kinase Suppressor of Ras (KSR) scaffold protein in Ras signaling in Xenopus oocytes (8).Previous investigators reported focused and genome-wide inventories of pheromone system protein abundances (9-13). These measurements differed by up to 12-fold (Fig. 2) (14, 15). We thought some discrepancies might be because of differences and systematic biases in quantification methods (Discussion). We developed improved measurement methods and used these methods to quantify system proteins. We used...

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Section: Resultsmentioning

confidence: 99%

Section: Measurement Of Fluorescent Protein Fusions and Reporter Genementioning

confidence: 99%

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

Thomson

Benjamin

Bush

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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“…We also encoded the model in the BioNetGen™ language (Faeder et al, 2005a;Blinov et al, 2006), which enables automatic building (and solution) of ODEs based on specified reaction rules that serve as generators of chemical reactions. This formal model specification can be used for future work as the BioNetGen™ software allows for simulations of systems of thousands of reactions (Blinov et al, 2004;Faeder et al, 2005b). We found that the stochastic simulations performed using BioNetGen™ are more than 10 times faster than in MATLAB.…”

Section: Numerical Implementation and Simulation Protocolsmentioning

confidence: 99%

Stochastic effects and bistability in T cell receptor signaling

Lipniacki

Hat

Faeder

et al. 2008

Journal of Theoretical Biology

119

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The stochastic dynamics of T cell receptor (TCR) signaling are studied using a mathematical model intended to capture kinetic proofreading (sensitivity to ligand-receptor binding kinetics) and negative and positive feedback regulation mediated respectively by the phosphatase SHP1 and the MAP kinase ERK. The model incorporates protein-protein interactions involved in initiating TCR-mediated cellular responses and reproduces several experimental observations about the behavior of TCR signaling, including robust responses to as few as a handful of ligands (agonist peptide-MHC complexes on an antigen-presenting cell), distinct responses to ligands that bind TCR with different lifetimes, and antagonism. Analysis of the model indicates that TCR signaling dynamics are marked by significant stochastic fluctuations and bistability, which is caused by the competition between the positive and negative feedbacks. Stochastic fluctuations are such that single-cell trajectories differ qualitatively from the trajectory predicted in the deterministic approximation of the dynamics. Because of bistability, the average of single-cell trajectories differs markedly from the deterministic trajectory. Bistability combined with stochastic fluctuations allows for switch-like responses to signals, which may aid T cells in making committed cell-fate decisions.

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“…The "don't care, don't write" approach adopted in the Kappa-calculus, as well as in other rule-based languages like BioNetGen [4], opened the way for introducing compositional modeling in rule-based process calculi, and provides very compact and readable descriptions of biochemical systems in the presence of sophisticated molecule bindings. While compositional modeling represents in general a desirable advantage in the hands of the modeler, it becomes a limit when important properties of the system cannot be described in a compositional calculus because of their intrinsic non-compositionality.…”

Section: Resultsmentioning

confidence: 99%

Complex Functional Rates in Rule-Based Languages for Biochemistry

Versari

Zavattaro

2012

Lecture Notes in Computer Science

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Rule-based languages (like, for example, Kappa, BioNetGen, and BioCham) have emerged as successful models for the representation, analysis, and simulation of bio-chemical systems. In particular Kappa, although based on reactions, differs from traditional chemistry as it allows for a graph-like representation of complexes. It follows the "don't care, don't write" approach: a rule contains the description of only those parts of the complexes that are actually involved in a reaction. Hence, given any possible combination of complexes that contain the reactants, such complexes can give rise to the reaction. In this paper we address the problem of extending the "don't care, don't write" approach to cases in which the actual structure of the complexes involved in the reaction could affect it (for instance, the mass of the complexes could influence the rate). The solutions that we propose is κF , an extension of the Kappa-calculus in which rates are defined as functions of the actually involved complexes.

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BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains

Cited by 386 publications

References 11 publications

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

Stochastic effects and bistability in T cell receptor signaling

Complex Functional Rates in Rule-Based Languages for Biochemistry

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