TRuML: A Translator for Rule-Based Modeling Languages

Suderman, Ryan; Hlavacek, William S.

doi:10.1101/171306

Cited by 4 publications

(10 citation statements)

References 11 publications

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“…to obtain the reaction product (Faeder et al, 2005(Faeder et al, , 2009. The authors' comparison of the time traces of Kappa-and BNGL-defined rules shows equivalent results under stochastic realisations (Suderman and Hlavacek, 2017). An earlier study by Suderman and Deeds (2013) also showed equality of results between both frameworks.…”

Section: Discrepancies In Dynamics Between Modelsmentioning

confidence: 89%

“…of Kappa-and BNGL-defined rules shows equivalent results under stochastic realisations (Suderman and Hlavacek, 2017). An earlier study by Suderman and Deeds (2013) also showed equality of results between both frameworks.…”

Section: /23mentioning

confidence: 94%

“…Further analysis suggested that activation of proteins encoded with fine detail is slower than in the ODE model. It is worth noting that although the use of BNG removes the need to define all possible bond combinations explicitely, this does not mean that it can change the results of Kappa simulations This claim can be supported with an example given by the developers of BNGL in a publication reporting on the translator between BNG and Kappa, TRuML (Suderman and Hlavacek, 2017). Their example shows on antibodies with identical antigen-binding sites that equivalent rule sets in both RBM frameworks have the same rate constants.…”

Section: Discrepancies In Dynamics Between Modelsmentioning

confidence: 99%

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Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Wysocka

Simpson

Page³

et al. 2022

Preprint

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Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule-based (RB) modelling, have addressed these issues. Although the advantages of RB modelling over ODEs have been presented and discussed in numerous reviews, no direct comparison of the time courses of a molecular system encoded in the two frameworks has been made before. To make such a comparison, a set of reactions that underlie an ODE model by Fernandez et al. [1] was manually encoded in the Kappa language, one of the RB frameworks. A comparison of the models was performed at the level of model specification and results were acquired through model simulations. We found that the Kappa model recapitulated the general dynamics of its ODE counterpart with minor differences. These differences occur whenever molecules have multiple sites binding the same interactor. The notation of such rules requires a complete listing of all possible binding configurations. Furthermore, activation of these molecules in the RB model is slower than in the ODE one but can be corrected by revision of the rate constants used in the relevant rules. We conclude that the RB representation offers a more expressive and flexible syntax that eases access to fine-grain details of the model, facilitating model reuse. In parallel with these analyses, this manuscript reports a refactored model of a DARPP-32 interaction network that can serve as a canvas for the development of a more complex interaction network to study this particular molecular system.

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Section: Discrepancies In Dynamics Between Modelsmentioning

confidence: 89%

Section: /23mentioning

confidence: 94%

Section: Discrepancies In Dynamics Between Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Wysocka

Simpson

Page³

et al. 2022

Preprint

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“…Features or conventions present in the modeling language, such as those described in this section, can result in language-specific behaviors. This is especially evident when attempting to translate a model into a different rule-based modeling language (Suderman and Hlavacek, 2017). Documentation of pathological cases (both for the modeling language and the simulation engine) as well as the semantics of the rule-based modeling language are essential for accurate and consistent modeling and simulation.…”

Section: Identical Site Pathologiesmentioning

confidence: 99%

Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

et al. 2018

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Gillespie's direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie's direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie's direct method for network-free simulation. Finally, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.

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“…Other model standards may be indirectly supported by converting to BNGL or SBML. For example, rule-based models defined in the Kappa language [46,47] can be converted to BNGL using the software tool TRuML [48].…”

Section: Introductionmentioning

confidence: 99%

PyBioNetFit and the Biological Property Specification Language

et al. 2019

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SummaryIn systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit, which in addition supports checking models against known system properties and solving design problems. PyBioNetFit introduces Biological Property Specification Language (BPSL) for the formal declaration of system properties. BPSL allows qualitative data to be used alone or in combination with quantitative data. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms that work directly with existing model definition standards: BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit's capabilities by solving various example problems, including the challenging problem of parameterizing a 153-parameter model of cell cycle control in yeast based on both quantitative and qualitative data. We demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of targeted drug interventions in autophagy signaling.

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TRuML: A Translator for Rule-Based Modeling Languages

Cited by 4 publications

References 11 publications

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

PyBioNetFit and the Biological Property Specification Language

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