Syntax and Semantics of a Multi-Level Modeling Language

Warnke, Tom; Helms, Tobias; Uhrmacher, Adelinde M.

doi:10.1145/2769458.2769467

Cited by 12 publications

(8 citation statements)

References 31 publications

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“…binding the result of the function splitHalf to a pair of variables in a where block [46]. In the syntax of CSMMR, the rule would read as…”

Section: Rule-based Modeling and Compartmentsmentioning

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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“…binding the result of the function splitHalf to a pair of variables in a where block [46]. In the syntax of CSMMR, the rule would read as…”

Section: Rule-based Modeling and Compartmentsmentioning

confidence: 99%

Expressive modeling and fast simulation for dynamic compartments

Köster,

Henning,

Warnke

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Data Association (Schumitsch et al, 2005) (Huang et al, 2009a) (Huang et al, 2009b) (Huang et al, 2009c) (Jagabathula & Shah, 2011) (Kondor et al, 2007) (Barbuti et al, 2011) (Barbuti et al, 2012) (Krishnamurthy et al, 2004) (Warnke et al, 2015) (Bistarelli et al, 2003) (Oury & Plotkin, 2013) (Maus et al, 2011) Appendix D. List of Abbreviations…”

Section: Appendix a Lifted Inference Complexity Classesmentioning

confidence: 99%

State-Space Abstractions for Probabilistic Inference: A Systematic Review

Lüdtke¹,

Schröder²,

Bader³

et al. 2018

jair

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Tasks such as social network analysis, human behavior recognition, or modeling biochemical reactions, can be solved elegantly by using the probabilistic inference framework. However, standard probabilistic inference algorithms work at a propositional level, and thus cannot capture the symmetries and redundancies that are present in these tasks.Algorithms that exploit those symmetries have been devised in different research fields, for example by the lifted inference-, multiple object tracking-, and modeling and simulationcommunities. The common idea, that we call state space abstraction, is to perform inference over compact representations of sets of symmetric states. Although they are concerned with a similar topic, the relationship between these approaches has not been investigated systematically.This survey provides the following contributions. We perform a systematic literature review to outline the state of the art in probabilistic inference methods exploiting symmetries. From an initial set of more than 4,000 papers, we identify 116 relevant papers. Furthermore, we provide new high-level categories that classify the approaches, based on common properties of the approaches. The research areas underlying each of the categories are introduced concisely. Researchers from different fields that are confronted with a state space explosion problem in a probabilistic system can use this classification to identify possible solutions. Finally, based on this conceptualization, we identify potentials for future research, as some relevant application domains are not addressed by current approaches.

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“…As already found in the design of other modelling languages, the ability to define arbitrary functions on the attributes, structure, and components of models is an essential feature of expressive modelling languages (John et al 2011;Warnke, Helms et al 2015). Given different options, the functions are used to calculate the respective attractiveness to an agent of deciding on a particular option and to translate this attractiveness into corresponding transition times.…”

Section: Complex Decision Processes and Stochastic Racementioning

confidence: 99%

Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race

et al. 2017

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Individuals' decision processes play a central role in understanding modern migration phenomena and other demographic processes. Their integration into agent-based computational demography depends largely on suitable support by a modelling language. We are developing the Modelling Language for Linked Lives (ML3) to describe the diverse decision processes of linked lives succinctly in continuous time. The context of individuals is modelled by networks the individual is part of, such as family ties and other social networks. Central concepts, such as behaviour conditional on agent attributes, age-dependent behaviour, and stochastic waiting times, are tightly integrated in the language. Thereby, alternative decisions are modelled by concurrent processes that compete by stochastic race. Using a migration model, we demonstrate how this allows for compact description of complex decisions, here based on the Theory of Planned Behaviour. We describe the challenges for the simulation algorithm posed by stochastic race between multiple concurrent complex decisions.

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Syntax and Semantics of a Multi-Level Modeling Language

Cited by 12 publications

References 31 publications

Expressive modeling and fast simulation for dynamic compartments

Expressive modeling and fast simulation for dynamic compartments

State-Space Abstractions for Probabilistic Inference: A Systematic Review

Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race

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