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Rulebased Modelling of Cellular Signalling
 PROCEEDINGS OF THE 18 TH INTERNATIONAL CONFERENCE ON CONCURRENCY THEORY (CONCUR’07), LECTURE NOTES IN COMPUTER SCIENCE
, 2007
"... Modelling is becoming a necessity in studying biological signalling pathways, because the combinatorial complexity of such systems rapidly overwhelms intuitive and qualitative forms of reasoning. Yet, this same combinatorial explosion makes the traditional modelling paradigm based on systems of di ..."
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Cited by 104 (22 self)
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Modelling is becoming a necessity in studying biological signalling pathways, because the combinatorial complexity of such systems rapidly overwhelms intuitive and qualitative forms of reasoning. Yet, this same combinatorial explosion makes the traditional modelling paradigm based on systems of differential equations impractical. In contrast, agentbased or concurrent languages, such as κ [1–3] or the closely related BioNetGen language [4–10], describe biological interactions in terms of rules, thereby avoiding the combinatorial explosion besetting differential equations. Rules are expressed in an intuitive graphical form that transparently represents biological knowledge. In this way, rules become a natural unit of model building, modification, and discussion. We illustrate this with a sizeable example obtained from refactoring two models of EGF receptor signalling that are based on differential equations [11, 12]. An exciting aspect of the agentbased approach is that it naturally lends itself to the identification and analysis of the causal structures that deeply shape the dynamical, and perhaps even evolutionary, characteristics of complex distributed biological systems. In particular, one can adapt the notions of causality and conflict, familiar from concurrency theory, to κ, our representation language of choice. Using the EGF receptor model as an example, we show how causality enables the formalization of the colloquial concept of pathway and, perhaps more surprisingly, how conflict can be used to dissect the signalling dynamics to obtain a qualitative handle on the range of system behaviours. By taming the combinatorial explosion, and exposing the causal structures and key kinetic junctures in a model, agent and rulebased representations hold promise for making modelling more powerful, more perspicuous, and of appeal to a wider audience.
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.
Scalable simulation of cellular signaling networks
 IN PROCEEDINGS OF APLAS 2007
, 2007
"... Given the combinatorial nature of cellular signalling pathways, where biological agents can bind and modify each other in a large number of ways, concurrent or agentbased languages seem particularly suitable for their representation and simulation [1–4]. Graphical modelling languages such as κ [5– ..."
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Cited by 59 (13 self)
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Given the combinatorial nature of cellular signalling pathways, where biological agents can bind and modify each other in a large number of ways, concurrent or agentbased languages seem particularly suitable for their representation and simulation [1–4]. Graphical modelling languages such as κ [5–8], or the closely related BNG language [9– 14], seem to afford particular ease of expression. It is unclear however how such models can be implemented. 6 Even a simple model of the EGF receptor signalling network can generate more than 10 23 nonisomorphic species [5], and therefore no approach to simulation based on enumerating species (beforehand, or even onthefly) can handle such models without sampling down the number of potential generated species. We present in this paper a radically different method which does not attempt to count species. The proposed algorothm uses a representation of the system together with a superapproximation of its ‘event horizon ’ (all events that may happen next), and a specific correction scheme to obtain exact timings. Being completely local and not based on any kind of enumeration, this algorithm has a per event time cost which is independent of (i) the size of the set of generable species (which can even be infinite), and (ii) independent of the size of the system (ie, the number of agent instances). We show how to refine this algorithm, using concepts derived from the classical notion of causality, so that in addition to the above one also has that the even cost is depending (iii) only logarithmically on the size of the model (ie, the number of rules). Such complexity properties reflect in our implementation which, on a current computer, generates about 10 6 events per minute in the case of the simple EGF receptor model mentioned above, using a system with 10 5 agents.
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: An overview
 RV 2010
, 2010
"... Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical a ..."
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Cited by 29 (6 self)
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Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach [31,8,35,22,21,5] that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas; the algorithms themselves depend on the class of systems being analyzed as well as the logic used for specifying the properties. Another approach to solve the model checking problem is to simulate the system for finitely many executions, and use hypothesis testing to infer whether the samples provide a statistical evidence for the satisfaction or violation of the specification. In this tutorial, we survey the statistical approach, and outline its main advantages in terms of efficiency, uniformity, and simplicity.
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.
Rulebased modeling of biochemical networks
 Complexity
, 2005
"... 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 cl ..."
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Cited by 22 (14 self)
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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. © 2005 Wiley Periodicals, Inc. Complexity 10: 22–41, 2005 Key Words: local rules; automatic model generation; networks; signal transduction; combinatorial complexity; systems biology The cell is a complex adaptive system whose emergent behavior we understand only poorly. One reason for our lack of understanding is the complexity of cellular decision making, which is often mediated by a system of interacting proteins. Systems of interacting proteins are particularly prominent in signal transduction [1], 1 the focus Correspondence to: William S. Hlavacek,
Graph Rewriting and Strategies for Modeling Biochemical Networks
 in "International Workshop on Natural Computing and Applications  NCA 2007, Timisoara, Roumanie", IEEE Computer Society
"... Abstract. In this paper, we present a rewriting framework for modeling molecular complexes, biochemical reaction rules, and generation of biochemical networks based on the representation of molecular complexes as a particular type of multigraphs with ports called molecular graphs. The advantage of t ..."
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Cited by 12 (4 self)
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Abstract. In this paper, we present a rewriting framework for modeling molecular complexes, biochemical reaction rules, and generation of biochemical networks based on the representation of molecular complexes as a particular type of multigraphs with ports called molecular graphs. The advantage of this approach is to obtain for free a rewriting calculus which allows defining at the same level transformation rules and strategies for modeling rule selection and application, in order to prototype network generation. 1
A Language for Biochemical Systems: Design and Formal Specification
"... Abstract. This paper introduces a Language for Biochemical Systems (LBS) which combines rulebased approaches to modelling with modularity. It is based on the Calculus of Biochemical Systems (CBS) which affords modular descriptions of metabolic, signalling and regulatory networks in terms of reactio ..."
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Cited by 10 (3 self)
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Abstract. This paper introduces a Language for Biochemical Systems (LBS) which combines rulebased approaches to modelling with modularity. It is based on the Calculus of Biochemical Systems (CBS) which affords modular descriptions of metabolic, signalling and regulatory networks in terms of reactions between modified complexes, occurring concurrently inside a hierarchy of compartments and with possible crosscompartment interactions and transport. Additional features of LBS, targeted towards practical and largescale applications, include species expressions for manipulating large complexes in a concise manner, parameterised modules with a notion of subtyping for writing reusable modules, and nondeterminism for handling combinatorial explosion. These features are demonstrated through examples. A formal specification of LBS is then given through an abstract syntax and a general semantics which is parametric on a structure pertaining to the specific choice of target semantical objects. Examples of such structures for the specific cases of Petri nets, coloured Petri nets, ODEs and continuous time Markov chains are also given. Keywords: Largescale, parametrised modules, subtyping, combinatorial