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22
Rules for Modeling SignalTransduction Systems
 Science’s STKE
, 2006
"... Formalized rules for proteinprotein interactions have recently been introduced to represent the binding and enzymatic activities of proteins in cellular signaling. Rules encode an understanding of how a system works in terms of the biomolecules in the system and their possible states and interactio ..."
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Cited by 77 (20 self)
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Formalized rules for proteinprotein interactions have recently been introduced to represent the binding and enzymatic activities of proteins in cellular signaling. Rules encode an understanding of how a system works in terms of the biomolecules in the system and their possible states and interactions. A set of rules can be as easy to read as a diagrammatic interaction map, but unlike most such maps, rules have precise interpretations. Rules can be processed to automatically generate a mathematical or computational model for a system, which enables explanatory and predictive insights into the system’s behavior. Rules are independent units of a model specification that facilitate model revision. Instead of changing a large number of equations or lines of code, as may be required in the case of a conventional mathematical model, a protein interaction can be introduced or modified simply by adding or changing a single rule that represents the interaction of interest. Rules can be defined and visualized by using graphs, so no specialized training in mathematics or computer science is necessary to create models or to take advantage of the representational precision of rules. Rules can be encoded in a machinereadable format to enable electronic storage and exchange of models, as well as basic knowledge about proteinprotein interactions. Here, we review the motivation for rulebased modeling; applications of the approach; and issues that arise in model specification, simulation, and testing. We also discuss rule visualization and exchange and the software available for rulebased modeling.
Rulebased modeling of biochemical systems with BioNetGen
 IN METHODS IN MOLECULAR BIOLOGY: SYSTEMS BIOLOGY
, 2009
"... Rulebased modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate sitespecific details about proteinprotein interactio ..."
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Cited by 43 (10 self)
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Rulebased modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate sitespecific details about proteinprotein interactions into a model for the dynamics of a signaltransduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of proteinprotein interactions are difficult to specify and track with a conventional modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a rulebased model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in rulebased modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly largescale models for other biochemical systems.
Graph theory for rulebased modeling of biochemical networks
 Lect. Notes Comput. Sci
, 2006
"... Abstract. We introduce a graphtheoretic formalism suitable for modeling biochemical networks marked by combinatorial complexity, such as signaltransduction systems, in which proteinprotein interactions play a prominent role. This development extends earlier work by allowing for explicit represen ..."
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Cited by 30 (10 self)
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Abstract. We introduce a graphtheoretic formalism suitable for modeling biochemical networks marked by combinatorial complexity, such as signaltransduction systems, in which proteinprotein interactions play a prominent role. This development extends earlier work by allowing for explicit representation of the connectivity of a protein complex. Within the formalism, typed attributed graphs are used to represent proteins and their functional components, complexes, conformations, and states of posttranslational covalent modification. Graph transformation rules are used to represent proteinprotein interactions and their effects. Each rule defines a generalized reaction, i.e., a class of potential reactions that are logically consistent with knowledge or assumptions about the represented biomolecular interaction. A model is specified by defining 1) molecularentity graphs, which delimit the molecular entities and material components of a system and their possible states, 2) graph transformation rules, and 3) a seed set of graphs representing chemical species, such as the initial species present before introduction of a signal. A reaction network is generated iteratively through application of the graph transformation rules. The rules are first applied to the seed graphs and then to any and all new graphs that subsequently arise as a result of graph transformation. This procedure continues until no new graphs are generated or a specified termination condition is satisfied. The formalism supports the generation of a list of reactions in a system, which can be used to derive different types of physicochemical models, which can be simulated and analyzed in different ways. The processes of generating and simulating the network may be combined so that species are generated only as needed. 1
Statistical model checking in BioLab: applications to the automated analysis of TCell receptor signaling pathway
 In CMSB’08
, 2008
"... Abstract. We present an algorithm, called BioLab, for verifying temporal properties of rulebased models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a ..."
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Cited by 25 (7 self)
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Abstract. We present an algorithm, called BioLab, for verifying temporal properties of rulebased models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a formalism for representing and reasoning about propositions qualified in terms of time. Properties are then verified using sequential hypothesis testing on executions generated using stochastic simulation. BioLab is optimal, in the sense that it generates the minimum number of executions necessary to verify the given property. BioLab also provides guarantees on the probability of it generating TypeI (i.e., falsepositive) and TypeII (i.e., falsenegative) errors. Moreover, these error bounds are prespecified by the user. We demonstrate BioLab by verifying stochastic effects and bistability in the dynamics of the Tcell receptor signaling network.
Constraintbased simulation of biological systems described by molecular interaction maps
 In Proceeding of Third International Workshop on Constraintbased Methods in Bioinformatics, WCB 2007
, 2007
"... Abstract. We present a method to simulate biochemical networks described by the graphical notation of Molecular Interaction Maps within stochastic Concurrent Constraint Programming. Such maps are compact, as they represent implicitly a wide set of reactions, and therefore not easy to simulate with s ..."
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Cited by 6 (3 self)
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Abstract. We present a method to simulate biochemical networks described by the graphical notation of Molecular Interaction Maps within stochastic Concurrent Constraint Programming. Such maps are compact, as they represent implicitly a wide set of reactions, and therefore not easy to simulate with standard tools. The encoding we propose is capable to stochastically simulate these maps implicitly, without generating the full list of reactions. 1
signaling
, 2005
"... network model of early events in epidermal growth factor receptor ..."
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Applications to the automated analysis of TCell Receptor Signaling Pathway ⋆
"... Abstract. We present an algorithm, called BioLab, for verifying temporal properties of rulebased models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a ..."
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Abstract. We present an algorithm, called BioLab, for verifying temporal properties of rulebased models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a formalism for representing and reasoning about propositions qualified in terms of time. Properties are then verified using sequential hypothesis testing on executions generated using stochastic simulation. BioLab is optimal, in the sense that it generates the minimum
Conference on Cellular Information Processing
, 2008
"... Complexity and modularity of intracellular ..."
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Systems biology Carbon fate maps for metabolic reactions
"... Motivation: Stable isotope labeling of smallmolecule metabolites (e.g., 13 Clabeling of glucose) is a powerful tool for characterizing pathways and reaction fluxes in a metabolic network. Analysis of isotope labeling patterns requires knowledge of the fates of individual atoms and moieties in reac ..."
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Motivation: Stable isotope labeling of smallmolecule metabolites (e.g., 13 Clabeling of glucose) is a powerful tool for characterizing pathways and reaction fluxes in a metabolic network. Analysis of isotope labeling patterns requires knowledge of the fates of individual atoms and moieties in reactions, which can be difficult to collect in a useful form when considering a large number of enzymatic reactions. Results: We report carbonfate maps for 4,605 enzymecatalyzed reactions documented in the KEGG database. Every fate map has been manually checked for consistency with known reaction mechanisms. A map includes a standardized structurebased identifier for each reactant (namely, an InChI ™ string); indices for carbon atoms that are uniquely derived from the metabolite identifiers; structural data, including an identification of homotopic and prochiral carbon atoms; and a bijective map relating the corresponding carbon atoms in substrates and products. Fate maps are defined using the BioNetGen ™ language (BNGL), a formal modelspecification language, which allows a set of maps to be automatically translated into isotopomer massbalance equations. Availability: The carbon fate maps and software for visualizing the maps are freely available