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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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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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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.
Abstracting the differential semantics of rulebased models: exact and automated model reduction (revised version)
, 2010
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The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology.
 Nat. Biotechnol.
, 2014
"... The reuse of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SB ..."
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Cited by 9 (1 self)
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The reuse of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a communitydriven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a communitydriven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow. Synthetic biology treats biological organisms as a new technological medium with a unique set of characteristics, such as the ability to selfrepair, evolve and replicate. These characteristics create their own engineering challenges, but offer a rich and largely untapped source of potential applications across a broad range of sectors 1,2 . Applications such as biomolecular computing 3 , metabolic engineering 4 , or reconstruction and exploration of natural cell biology Every engineering field relies on a set of 'standards' 7 that practitioners follow to enable the exchange and reuse of designs for 'systems' , 'devices' and 'components' . Similarly, the representation of synthetic biology designs using computerreadable 'data standards' has the potential to facilitate the forward engineering of novel biological systems from previously characterized devices and components. For example, such standards could enable synthetic biology companies to offer catalogs of devices and components by means of computerreadable data sheets, just as modern semiconductor companies do for electronics. Such standards could also enable a synthetic biologist to develop portions of a design using one software tool, refine the design using another tool, and finally transmit it electronically to a colleague or commercial fabrication company. In order for synthetic biology designs to scale up in complexity, researchers will need to make greater use of specialized design tools and parts repositories. Seamless intertool communication would, for example, allow the separation of genetic network design from network simulation, and the separation of both from codon optimization and synthesis. The wide adoption of a design standard would allow the growing number of software tools to more directly support an integrated design workflow 8 involving synthetic biologists from both research and commercial institutions.
Faeder J: Compartmental rulebased modeling of biochemical systems
 Proceedings of the 2009 Winter Simulation Conference (WSC), IEEE 2009
"... Rulebased modeling is an approach to modeling biochemical kinetics in which proteins and other biological components are modeled as structured objects and their interactions are governed by rules that specify the conditions under which reactions occur. BIONETGEN is an opensource platform that prov ..."
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Cited by 6 (0 self)
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Rulebased modeling is an approach to modeling biochemical kinetics in which proteins and other biological components are modeled as structured objects and their interactions are governed by rules that specify the conditions under which reactions occur. BIONETGEN is an opensource platform that provides a simple yet expressive language for rulebased modeling (BNGL). In this paper we describe compartmental BNGL (cBNGL), which extends BNGL to enable explicit modeling of the compartmental organization of the cell and its effects on system dynamics. We show that by making localization a queryable attribute of both molecules and species and introducing appropriate volumetric scaling of reaction rates, the effects of compartmentalization can be naturally modeled using rules. These properties enable the construction of new rule semantics that include both universal rules, those defining interactions that can take place in any compartment in the system, and transport rules, which enable movement of molecular complexes between compartments. 1
G.E.: Rulebender: integrated modeling, simulation and visualization for rulebased intracellular biochemistry
 BMC bioinformatics 13(Suppl
, 2012
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Computational modeling and verification of signaling pathways in cancer
 In ANB, volume 6479 of LNCS
, 2010
"... Abstract. We propose and analyze a rulebased model of the HMGB1 signaling pathway. The protein HMGB1 can activate a number of regulatory networks the p53, NFκB, Ras and Rb pathways that control many physiological processes of the cell. HMGB1 has been recently shown to be implicated in cancer, in ..."
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Abstract. We propose and analyze a rulebased model of the HMGB1 signaling pathway. The protein HMGB1 can activate a number of regulatory networks the p53, NFκB, Ras and Rb pathways that control many physiological processes of the cell. HMGB1 has been recently shown to be implicated in cancer, inflammation and other diseases. In this paper, we focus on the NFκB pathway and construct a crosstalk model of the HMGB1p53NFκBRasRb network to investigate how these couplings influence proliferation and apoptosis (programmed cell death) of cancer cells. We first built a singlecell model of the HMGB1 network using the rulebased BioNetGen language. Then, we analyzed and verified qualitative properties of the model by means of simulation and statistical model checking. For model simulation, we used both ordinary differential equations and Gillespie's stochastic simulation algorithm. Statistical model checking enabled us to verify our model with respect to behavioral properties expressed in temporal logic. Our analysis showed that HMGB1activated receptors can generate sustained oscillations of irregular amplitude for the NFκB, IκB, A20 and p53 proteins. Also, knockout of A20 can destroy the IκBNFκB negative feedback loop, leading to the development of severe inflammation or cancer. Our model also predicted that the knockout or overexpression of the IκB kinase can influence the cancer cell's fate apoptosis or survival through the crosstalk of different pathways. Finally, our work shows that computational modeling and statistical model checking can be effectively combined in the study of biological signaling pathways.
Thermodynamic graphrewriting
"... Abstract. We develop a new ‘thermodynamic ’ approach to stochastic graphrewriting. The ingredients are a finite set of reversible graphrewriting rules G (called generating rules), a finite set of connected graphs P (called energy patterns), and an energy cost function : P → R. The idea is that G ..."
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Abstract. We develop a new ‘thermodynamic ’ approach to stochastic graphrewriting. The ingredients are a finite set of reversible graphrewriting rules G (called generating rules), a finite set of connected graphs P (called energy patterns), and an energy cost function : P → R. The idea is that G defines the qualitative dynamics by showing which transformations are possible, while P and specify the longterm probability pi of any graph reachable under G. Given G, P, we construct a finite set of rules GP which (i) has the same qualitative transition system as G, and (ii) when equipped with suitable rates, defines a continuoustime Markov chain of which pi is the unique fixed point. The construction relies on the use of site graphs and a technique of ‘growth policy ’ for quantitative rule refinement which is of independent interest. The ‘division of labour ’ between the qualitative and the longterm quantitative aspects of the dynamics leads to intuitive and concise descriptions for realistic models (see the example in §4). It also guarantees thermodynamical consistency (aka detailed balance), otherwise known to be undecidable, which is important for some applications. Finally, it leads to parsimonious parameterizations of models, again an important point in some applications. 1
FORMAL REPRESENTATION OF THE HIGH OSMOLARITY GLYCEROL PATHWAY IN YEAST
, 2009
"... The high osmolarity glycerol (HOG) signalling system in yeast belongs to the class of Mitogen Activated Protein Kinase (MAPK) pathways that are found in all eukaryotic organisms. It includes at least three scaffold proteins that form complexes, and involves reactions that are strictly dependent on t ..."
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The high osmolarity glycerol (HOG) signalling system in yeast belongs to the class of Mitogen Activated Protein Kinase (MAPK) pathways that are found in all eukaryotic organisms. It includes at least three scaffold proteins that form complexes, and involves reactions that are strictly dependent on the set of species bound to a certain complex. The scaffold proteins lead to a combinatorial increase in the number of possible states. To date, representations of the HOG pathway have used simplifying assumptions to avoid this combinatorial problem. Such assumptions are hard to make and may obscure or remove essential properties of the system. This paper presents a detailed generic formal representation of the HOG system without such assumptions, showing the molecular interactions known from the literature. The model takes complexes into account, and summarises existing knowledge in an unambiguous and detailed representation. It can thus be used to anchor discussions about the HOG system. In the commonly used Systems Biology Markup Language (SBML), such a model would need to explicitly enumerate all state variables. The Kappa modelling language which we use supports representation of complexes without such enumeration. To conclude, we compare Kappa with a few other modelling languages and software tools that could also be used to represent and model the HOG system.