Tom Warnke

Syntax and Semantics of a Multi-Level Modeling Language

The domain specific modeling and simulation language ML-Rules makes it possible to describe cell biological systems at different levels of organization. A model is formed by attributed and dynamically nested species, with reactions that are constrained by functions on attributes. In this paper, we extend ML-Rules to also support constraints using functions on multi-sets of species, i.e., solutions. Further, we present the formal syntax and semantics of ML-Rules, we define its stochastic simulator and we illustrate its expressiveness based on a model of the cell cycle and proliferation.

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

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.

Semantics and Efficient Simulation Algorithms of an Expressive Multilevel Modeling Language

The domain-specific modeling and simulation language ML-Rules is aimed at facilitating the description of cell biological systems at different levels of organization. Model states are chemical solutions that consist of dynamically nested, attributed entities. The model dynamics are described by rules that are constrained by arbitrary functions, which can operate on the entities’ attributes, (nested) solutions, and the reaction kinetics. Thus, ML-Rules supports an expressive hierarchical, variable structure modeling of cell biological systems. The formal syntax and semantics of ML-Rules show that it is firmly rooted in continuous-time Markov chains. In addition to a generic stochastic simulation algorithm for ML-Rules, we introduce several specialized algorithms that are able to handle subclasses of ML-Rules more efficiently. The algorithms are compared in a performance study, leading to conclusions on the relation between expressive power and computational complexity of rule-based modeling languages.

Population-based CTMCS and agent-based models

Currently, only few agent-based models are implemented with a continuous representation of time, although state-of-the-art agent-based modeling and simulation (ABMS) frameworks support continuous-time models and continuous time often allows for a more faithful capturing of reality. Intrigued by this discrepancy, we take a closer look at population-based Continuous-Time Markov Chains (CTMCs), their modeling and their simulation, on the one hand, and, how continuous-time agent-based models are currently realized in state-of-the-art ABMS frameworks, like Repast Simphony and Netlogo, on the other hand. Subsequently, we adopt and adapt concepts and algorithms of modeling and simulating population-based CTMCs. We propose a solution how to integrate those into contemporary ABMS frameworks which results in a more succinct description of continuous-time agent-based models.

Reusing simulation experiment specifications in developing models by successive composition — a case study of the Wnt/β-catenin signaling pathway

With the increasing size and complexity of models, developing models by composing existing ones becomes more important. We exploit the idea of reusing simulation experiments of individual models for composition to automatically generate experiments for the composed model. First, we illustrate the process of modeling based on composition and discuss the role simulation experiments can play in this process. Our focus is on semantic validation of the composed model. We explicitly specify simulation experiments in simulation experiment specification via a Scala layer, including the desired model behavioral properties and their required experiment set-ups. Models are annotated with experiment specifications, and upon composition, these specifications are adapted and automatically executed for the composed model. The approach is applied in a case study of developing a Wnt/β-catenin signaling pathway model by successively composing three individual models, where we exploit metric interval temporal logic to describe model behavioral properties and check averages of stochastic simulation results against these properties.

Reusing simulation experiment specifications to support developing models by successive extension

Model development is a successive process of validating, revising, and extending models, and requires iterative execution of simulation experiments. While developing a model by extension, executing similar simulation experiments to those performed with the original model reveals important behavioral insights into the extended model. An automatic generation and execution of these simulation experiments can provide valuable support in the process of developing models. A prerequisite is an explicit specification of simulation experiments. Therefore, we annotate models with simulation experiments that are specified in a declarative domain specific language SESSL (Simulation Experiment Specification via a Scala Layer). Based on experiment specifications of the original model, we introduce a mechanism to automatically generate and execute simulation experiments for the extended model with necessary adaptations. Furthermore, as we experiment with stochastic models, we exploit statistical model checking and specify the expected model behavioral properties, against which the simulation results are checked. Thereby, when a model is extended, the original experiment specifications are reused, adapted, and applied to the extended model. Accordingly, the generated simulation trajectories are probed to check whether the expected properties hold with a certain probability or not. Thus, more fast and frequent feedback during model development can be provided to the modeler. Based on a model of membrane related dynamics, we show how the developed approach can be used in successively extending models.

ML3: a language for compact modeling of linked lives in computational demography

Agent-based modeling and simulation is widely used in computational demography. Although existing agent-based approaches allow modeling linked lives in a rather flexible manner, the resulting models, due to typically being implemented in a general-purpose programming language, often lack the compactness required to easily access the model. With ML3 (Modeling Language for Linked Lives) we present a compact and expressive domain-specific modeling language for continuous-time agent-based models in computational demography. The language combines elements from guarded commands, process algebras, and rule-based approaches. We discuss and present the individual features of the language and illuminate its compactness by presenting the specification of an entire agent-based model from recent literature.

Individual-based cod simulation with ML-Rules

A dramatic increase in malnourished cod can presently be observed in the Eastern Baltic. Simulation studies help unraveling possible reasons behind this. Particularly, individual-based modeling approaches are promising as they facilitate taking into account the heterogeneity of the cod population, where size, temperature etc. determine behavior patterns. Thus, we develop an individual-based model of cod implemented in the rule-based multi-level modeling language ML-Rules that allows to specify dynamically nested entities with attributes and complex multi-level reaction rules. By using this language, we are able to deal with several challenges when modeling such complex systems, e.g., dynamic structures, complex interaction rates and interdependencies. Here, we discuss the current state of our model that already represents a near realistic cod metabolism and we discuss how ML-Rules helped to solve emerged challenges.

Reproducible and flexible simulation experiments with ML-Rules and SESSL

Summary The modeling language ML‐Rules allows specifying and simulating complex systems biology models at multiple levels of organization. The development of such simulation models involves a wide variety of simulation experiments and the replicability of generated simulation results requires suitable means for documenting simulation experiments. Embedded domain‐specific languages, such as SESSL, cater to both requirements. With SESSL, the user can integrate diverse simulation experimentation methods and third‐party software components into an executable, readable simulation experiment specification. A newly developed SESSL binding for ML‐Rules exploits these features of SESSL, opening up new possibilities for executing and documenting simulation experiments with ML‐Rules models. Availability and implementation ML‐Rules is implemented in Java, SESSL and its bindings are implemented in Scala. The source code is available under open‐source licenses: ML‐Rules git.informatik.uni‐rostock.de/mosi/mlrules2 ML‐Rules Quickstart (Graphical Editor) git.informatik.uni‐rostock.de/mosi/mlrules2‐quickstart SESSL git.informatik.uni‐rostock.de/mosi/sessl and sessl.org SESSL Quickstart (Experiment Template) git.informatik.uni‐rostock.de/mosi/sessl‐quickstart Furthermore, Maven‐compatible compiled packages of ML‐Rules, SESSL, and the SESSL bindings are available from the Maven Central Repository at maven.org (org.sessl:* and org.jamesii: mlrules). Supplementary information Supplementary data are available at Bioinformatics online.

A DSL for continuous-time agent-based modeling and simulation

Most state-of-the-art agent-based modeling and simulation (ABMS) frameworks offer a way to describe agent behavior in a programming language. Whereas these frameworks support easy development of time-stepped models, continuous-time models can only be implemented by manually scheduling and retracting events as part of the agent behavior. To facilitate a separation of concerns into model- and simulation-specific code for continuous-time ABMS, we propose an embedded domain-specific language, which allows describing agent behavior concisely, and corresponding simulation algorithms, which allow executing continuous-time models. The language style and the algorithms are adapted from rule-based modeling languages for Continuous-Time Markov Chains and Stochastic Simulation Algorithm variants. We implemented prototypes of the modeling language and simulation algorithms based on Repast Simphony.