Coloured stochastic multilevel multiset rewriting

Oury, Nicolas; Plotkin, Gordon

doi:10.1145/2037509.2037534

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Modular DSLs targeted to the synthetic biology domain may hence be of increasing interest. With respect to language design, one promising direction is to extend Kappa sites with a notion of colour as in coloured Petri nets [ 13 , 30 ], allowing more elaborate conditions on internal state and changes thereof during rule application.…”

Section: Discussionmentioning

confidence: 99%

“…This has prompted the development of a range of new modelling formalisms inspired by computer science, allowing models to be written in a structured fashion and generally supporting multiple types of simulation and analysis based on a single model. These formalisms include process calculi such as the stochastic π -calculus [ 1 , 2 ], the continuous π -calculus [ 3 ], Beta binders [ 4 , 5 ], BlenX [ 6 ], PEPA [ 7 ] and BioPEPA [ 8 ]; and rule-based languages such as BIOCHAM [ 9 ], Kappa [ 10 , 11 ], BioNetGen [ 12 ] and Stochastic Multilevel Multiset Rewriting [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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A High-Level Language for Rule-Based Modelling

2015

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Rule-based languages such as Kappa excel in their support for handling the combinatorial complexities prevalent in many biological systems, including signalling pathways. But Kappa provides little structure for organising rules, and large models can therefore be hard to read and maintain. This paper introduces a high-level, modular extension of Kappa called LBS-κ. We demonstrate the constructs of the language through examples and three case studies: a chemotaxis switch ring, a MAPK cascade, and an insulin signalling pathway. We then provide a formal definition of LBS-κ through an abstract syntax and a translation to plain Kappa. The translation is implemented in a compiler tool which is available as a web application. We finally demonstrate how to increase the expressivity of LBS-κ through embedded scripts in a general-purpose programming language, a technique which we view as generally applicable to other domain specific languages.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A High-Level Language for Rule-Based Modelling

2015

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“…The rule-based approach is being extended to multi-level modelling [29,33], where model entities can be nested, and downward and upward causation between different levels is possible, and to the representation of spatial relations in models [34]. Thus, the approach holds great potential at theoretical and practical levels.…”

Section: Discussionmentioning

confidence: 99%

“…Rule-based modelling addresses combinatorial expansion in systems modelling, and enables the simulation and analysis of reactions whose formal analysis would otherwise be intractable [2,27,28]. The rule-based approach is being extended to multi-level modelling [29,33], where model entities can be nested, and downward and upward causation between different levels is possible, and to the representation of spatial relations in models [34]. Thus, the approach holds great potential at theoretical and practical levels.…”

Section: Discussionmentioning

confidence: 99%

A rule-based kinetic model of RNA polymerase II C-terminal domain phosphorylation

Aitken

Alexander

Beggs

2013

J. R. Soc. Interface.

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The complexity of many RNA processing pathways is such that a conventional systems modelling approach is inadequate to represent all the molecular species involved. We demonstrate that rule-based modelling permits a detailed model of a complex RNA signalling pathway to be defined. Phosphorylation of the RNA polymerase II (RNAPII) C-terminal domain (CTD; a flexible tail-like extension of the largest subunit) couples pre-messenger RNA capping, splicing and 3′ end maturation to transcriptional elongation and termination, and plays a central role in integrating these processes. The phosphorylation states of the serine residues of many heptapeptide repeats of the CTD alter along the coding region of genes as a function of distance from the promoter. From a mechanistic perspective, both the changes in phosphorylation and the location at which they take place on the genes are a function of the time spent by RNAPII in elongation as this interval provides the opportunity for the kinases and phosphatases to interact with the CTD. On this basis, we synthesize the available data to create a kinetic model of the action of the known kinases and phosphatases to resolve the phosphorylation pathways and their kinetics.

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“…(called “sequence variables” in the rewriting literature [45]) play a central role, as they denote the entities unaffected by a rule and allow operations on those multisets of entities [46]. One line of work that very closely follows the idea of multiset rewriting is Colored Stochastic Multilevel Multiset Rewriting (CSMMR) [4, 47]; another is ML-Rules [7].…”

Section: Introductionmentioning

confidence: 99%

Expressive modeling and fast simulation for dynamic compartments

Köster,

Henning,

Warnke

et al. 2024

Preprint

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Compartmentalization is vital for cell biological processes. The field of rule-based stochastic simulation has acknowledged this, and many tools and methods have capabilities for compartmentalization. However, mostly, this is limited to a static compartmental hierarchy and does not integrate compartmental changes. Integrating compartmental dynamics is challenging for the design of the modeling language and the simulation engine. The language should support a concise yet flexible modeling of compartmental dynamics. Our work is based on ML-Rules, a rule-based language for multi-level cell biological modeling that supports a wide variety of compartmental dynamics, whose syntax we slightly adapt. To develop an efficient simulation engine for compartmental dynamics, we combine specific data structures and new and existing algorithms and implement them in the Rust programming language. We evaluate the concept and implementation using two case studies from existing cell-biological models. The execution of these models outperforms previous simulations of ML-Rules by two orders of magnitude. Finally, we present a prototype of a WebAssembly-based implementation to allow for a low barrier of entry when exploring the language and associated models without the need for local installation.Author summaryBiochemical dynamics are constrained by and influence the dynamics of cellular compartments. Basic constraints are considered by many modeling and simulation tools, e.g., certain reactions may only occur in specific cellular compartments and at a speed influenced by the compartmental volume. However, to capture the functioning of complex compartmental dynamics such as cell proliferation or the fission or fusion of mitochondria, additional efforts are required from tool designers. These refer to how the modeler can specify these dynamics succinctly and unambiguously and how the resulting model can be executed efficiently. For modeling, we rely on ML-Rules, an expressive, formal rule-based language for modeling biochemical systems, which ships with the required features and which we only slightly adapt in our re-implementation. We design a new simulation engine that combines efficient data structures and various algorithms for efficient simulation. The achieved efficiency will enable thorough analysis, calibration, and validation of compartmental dynamics and, thus, allow the “in-silico” pursuit of research questions for which compartmental dynamics are essential. To further facilitate exploring the interplay of compartmental and non-compartmental dynamics, we exploit recent advances in web technology so that ML-Rules models can be run efficiently in the web browser.

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Coloured stochastic multilevel multiset rewriting

Cited by 6 publications

References 23 publications

A High-Level Language for Rule-Based Modelling

A High-Level Language for Rule-Based Modelling

A rule-based kinetic model of RNA polymerase II C-terminal domain phosphorylation

Expressive modeling and fast simulation for dynamic compartments

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