A High-Level Language for Rule-Based Modelling

Pedersen, Michael; Phillips, Andrew; Plotkin, Gordon

doi:10.1371/journal.pone.0114296

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…196 In collaborative model development, approaches used in computer programming are likely to prove useful because teams of programmers routinely write, maintain, and extend complex codes. Two products of recent efforts to bring software engineering concepts to modeling in systems biology are PySB 152 and LBSκ, 197 which are tools designed to ease the specification of models. A model specified using PySB takes the form of a Python-language program, which can be executed to obtain models in other formats and to direct simulations and analyses.…”

Section: High-level Modeling Languagesmentioning

confidence: 99%

Rule‐based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Chylek

Harris

Tung

et al. 2013

WIREs Mechanisms of Disease

106

146

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Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and post-translational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

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Section: High-level Modeling Languagesmentioning

confidence: 99%

Rule‐based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Chylek

Harris

Tung

et al. 2013

WIREs Mechanisms of Disease

106

146

View full text Add to dashboard Cite

show abstract

“…PySB transforms the Python code into either BioNetGen or Kappa rules, and provides methods that make it easier to create macros that encode recurrent biochemical patterns and to define complex networks as reusable modules. Pedersen et al [53] also introduce a modular extension to Kappa that provides a means for writing modular rule-based models.…”

Section: Biological Modelingmentioning

confidence: 99%

Dynamic Influence Networks for Rule-Based Models

Forbes

Burks²,

Lee³

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.

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“…These bonds are defined for exactly two entities, which precludes using them to express compartments. Static compartments can be integrated into these approaches [14, 38]. They are used to restrict the scope within which a reaction takes place.…”

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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A High-Level Language for Rule-Based Modelling

Cited by 11 publications

References 29 publications

Rule‐based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Rule‐based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Dynamic Influence Networks for Rule-Based Models

Expressive modeling and fast simulation for dynamic compartments

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