Often, no suitable data are available for validating and calibrating simulation models. Stylized facts are empirical and domain-specific insights that can be used to express expected model behavior. They are already being widely used to validate simulation models in specific application domains. The use of stylized facts for the automatic calibration of simulation models raises several challenges: the design of a domain-specific language that permits a succinct, intuitive, and unambiguous representation of a broad range of stylized facts; the formulation of a quantitative metric measuring the satisfaction of stylized facts to steer the calibration; and the effective communication of both the language semantics and the results. We discuss first approaches to address these challenges.

Non-pharmaceutical interventions (NPIs) are commonly used tools for controlling infectious disease transmission when pharmaceutical options are unavailable. Yet, identifying effective interventions that minimize societal disruption remains challenging. Agent-based simulation is a popular tool for analyzing the impact of possible interventions in epidemiology. However, automatically optimizing NPIs using agent-based simulations poses a complex problem because, in agent-based epidemiological models, interventions can target individuals based on multiple attributes, affect hierarchical group structures (e.g., schools, workplaces, and families), and be combined arbitrarily, resulting in a very large or even infinite search space. We aim to support decision-makers with our Agent-based Infectious Disease Intervention Optimization System (ADIOS) that optimizes NPIs for infectious disease simulations using Grammar-Guided Genetic Programming (GGGP). The core of ADIOS is a domain-specific language for expressing NPIs in agent-based simulations that structures the intervention search space through a context-free grammar. To make optimization more efficient, the search space can be further reduced by defining constraints that prevent the generation of semantically invalid intervention patterns. Using this constrained language and an interface that enables coupling with agent-based simulations, ADIOS adopts the GGGP approach for simulation-based optimization. Using the German Epidemic Micro-Simulation System (GEMS) as a case study, we demonstrate the potential of our approach to generate optimal interventions for realistic epidemiological models

No abstract available.

Validation and documentation of rationale are central to simulation studies. Most current approaches focus only on individual simulation artifacts—most typically simulation models—and their validity rather than their contribution to the overall simulation study. Approaches that aim to validate simulation studies as a whole either impose structured processes with the implicit assumption that this will ensure validity, or they rely on capturing provenance and rationale, most commonly in natural language, following accepted documentation guidelines. Inspired by dialectic approaches for developing mathematical proofs, we propose a vision of capturing validity and rationale information as a study unfolds through agent dialogues that also generate the overall simulation-study argument, as a novel approach to documenting and conducting simulation studies. We illustrate the key ideas in an example simulation study, highlight potential benefits of this novel approach, and identify key next steps towards making the vision a reality.

Increasing effort is put into the development of methods for learning mechanistic models from data. This task entails not only the accurate estimation of parameters but also a suitable model structure. Recent work on the discovery of dynamical systems formulates this problem as a linear equation system. Here, we explore several simulation-based optimization approaches, which allow much greater freedom in the objective formulation and weaker conditions on the available data. We show that even for relatively small stochastic population models, simultaneous estimation of parameters and structure poses major challenges for optimization procedures. Particularly, we investigate the application of the local stochastic gradient descent method, commonly used for training machine learning models. We demonstrate accurate estimation of models but find that enforcing the inference of parsimonious, interpretable models drastically increases the difficulty. We give an outlook on how this challenge can be overcome.

Synchronization algorithms for parallel simulation struggle to attain speedup if the simulation entities are tightly coupled and their interactions are difficult to predict. Window Racer is a novel parallel synchronization algorithm for shared-memory architectures specifically targeted toward attaining speedup in these challenging cases. The key idea is to speculatively process sequences of dependent events even across partition boundaries through fine-grained locking and low-overhead rollbacks, while negotiating a global synchronization window that rules out transitive rollbacks. In performance measurements using a variant of the PHold benchmark model, Window Racer outperforms an established implementation of the Time Warp algorithm in model configurations where events are often scheduled with near-zero delay. In an ablation study, we pinpoint the performance impact of our algorithm’s individual features by reducing Window Racer to two existing algorithms. We further study the algorithm’s ability to attain speedup in simulations of bio-chemical reaction networks, a particularly challenging class of simulations with tightly coupled state transitions.

Domain Specific Languages (DSLs) provide an abstraction optimized for a specific class of problems. In Modelling and Simulation, DSLs can be used by domain experts to express a model using the concepts and rules from their domain. One challenge is to find efficient means of executing these models. Here we present our experience in realizing two different DSLs for two different application domains. The first, ML-Rules, uses a custom syntax of an external language to describe transitions in cell biological systems as chemical reactions. For the second, ML3, we have an internal language embedded in the Rust programming language. ML3 is designed for agent-based simulation. Both languages follow an event-driven Continuous-time Markov chain semantic. However, the challenges in efficient execution differ.

Continuous-time agent-based models often represent tightly-coupled systems in which an agent’s state transitions occur in close interaction with neighboring agents. Without artificial discretization, the potential for near-instantaneous propagation of effects across the model presents a challenge to parallelizing their execution. Although existing algorithms can tackle the largely unpredictable nature of such simulations through speculative execution, they are subject to trade-offs concerning the degree of optimism, the probability and cost of rollbacks, and the exploitation of locality. This paper is aimed at understanding the suitability of asynchronous and synchronous parallel simulation algorithms when executing continuous-time agent-based models with rate-driven stochastic transitions. We present extensive measurement results comparing optimized implementations under various configurations of a parametrizable simulation model of the epidemic spread of disease. Our results show that the amount of locality in the agent interactions is the decisive factor for the relative performance of the approaches. Based on profiling results, we identify remaining hurdles for higher simulation performance with the two classes of algorithms and outline potential refinements.

Among the parallel and distributed simulation field’s main subjects are the performance benefits of new methods and optimizations. However, performance evaluations of the various simulators often rely on custom models, parametrizations, and baseline implementations, which complicates direct comparisons. We present our vision and initial steps towards COMPADS, a benchmark model and repository for reproducibly comparing the performance of parallel and distributed simulators and their respective algorithms. COMPADS is short for COMparing Parallel And Distributed Simulators. The first results include a novel deterministic-by-design synthetic benchmark model inspired by PHOLD and La-pdes. The benchmark output is a checksum that attests to the correctness of an implementation and its execution. So far, implementations exist for the simulators ROOT-Sim and ROSS.

Agent-based simulations relying on synchronous state updates using a fixed time step size are considered attractive candidates for parallel execution in order to reduce simulation running times for large and complex scenarios. However, if the underlying models are formulated with respect to continuous time, a time-stepped execution may only approximate the strict model semantics. To simulate continuous-time agent-based models, parallel discrete event algorithms can be applied. Traditionally those are based on logical processes exchanging time-stamped events, which clashes with the properties of models in which tightly coupled agents frequently access each other's states. To illustrate the challenges of such models and to derive a solution, we consider the domain-specific modeling language ML3, which allows modelers to succinctly express transitions and interactions of linked agents based on a continuous-time Markov chain (CTMC) semantics. We propose an optimistic synchronization scheme tailored towards simulations of fine-grained interactions among tightly coupled agents in highly dynamic topologies. By restricting the progress per round to at most one state change per agent, the synchronization scheme enables efficient direct read and write accesses among agents. To maintain concurrency given actions that depend on dynamically updated macro-level properties, we introduce a simple relaxation scheme with guaranteed error bounds. Using an extended variant of the classical susceptible-infected-recovered network model, we demonstrate that the proposed synchronization scheme accelerates simulations even under challenging model configurations.

BACKGROUND: Clinical diagnostics of whole-exome and whole-genome sequencing data requires geneticists to consider thousands of genetic variants for each patient. Various variant prioritization methods have been developed over the last years to aid clinicians in identifying variants that are likely disease-causing. Each time a new method is developed, its effectiveness must be evaluated and compared to other approaches based on the most recently available evaluation data. Doing so in an unbiased, systematic, and replicable manner requires significant effort. RESULTS: The open-source test bench "VPMBench" automates the evaluation of variant prioritization methods. VPMBench introduces a standardized interface for prioritization methods and provides a plugin system that makes it easy to evaluate new methods. It supports different input data formats and custom output data preparation. VPMBench exploits declaratively specified information about the methods, e.g., the variants supported by the methods. Plugins may also be provided in a technology-agnostic manner via containerization. CONCLUSIONS: VPMBench significantly simplifies the evaluation of both custom and published variant prioritization methods. As we expect variant prioritization methods to become ever more critical with the advent of whole-genome sequencing in clinical diagnostics, such tool support is crucial to facilitate methodological research.

For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.

Succinct, declarative, and domain-specific modeling languages have many advantages when creating simulation models. However, it is often challenging to efficiently execute models defined in such languages. We use code generation for model-specific simulators. Code generation has been successfully applied for high-performance algorithms in many application domains. By generating tailored simulators for specific simulation models defined in a domain-specific language, we get the best of both worlds: a succinct, declarative and formal presentation of the model and an efficient execution. We illustrate this based on a simple domain-specific language for biochemical reaction networks as well as on the network representation of the established BioNetGen language. We implement two approaches adopting the same simulation algorithms: one generic simulator that parses models at runtime and one generator that produces a simulator specialized to a given model based on partial evaluation and code generation. Akin to profile-guided optimization we also use dynamic execution of the model to further optimize the simulators. The performance of the approaches is carefully benchmarked using representative models of small to mid-sized biochemical reaction networks. The generic simulator achieves a performance similar to state of the art simulators in the domain, whereas the specialized simulator outperforms established simulation algorithms with a speedup of more than an order of magnitude. Both implementations are available online to the community under a permissive open-source license.

Bike sharing systems are a popular form of sustainable and affordable transport that has been introduced to cities around the world in recent years. Nevertheless, designing these systems to meet the requirements of the operators and also satisfy the demand of the users, is a complex problem. In this paper we focus on the recently introduced bike sharing system in the city of Edinburgh and use data analytics combined with formal modelling approaches to investigate the current behaviour and possible future behaviour of the system. Specifically we use a spatio-temporal logic, SSTL (the signal spatio-temporal logic), to formally characterise properties of the captured system, and through this identify potential problems as user demand grows. In order to investigate these problems further we use the CARMA modelling language and tool suite to construct a stochastic model of the system to investigate possible future scenarios, including decentralised redistribution. This model is parameterised and validated using data from the operational system.

Valid models are central for credible simulation studies. If those models do not exist, they need to be developed. In fact, entire simulation studies are often aimed at developing valid models. Thereby, successive model refinement and execution of diverse simulation experiments are closely intertwined. Whereas software-based support for individual simulation experiments exists, the intricate interdependencies and the diversity of tasks that govern simulation studies have prevented a more comprehensive support. To achieve the required flexibility while adhering to the constraints that apply between individual tasks, we adopt a declarative, artifact-based workflow approach. Therefore, central products of these simulation studies are identified and specified as artifacts: the conceptual model (with a focus on formally defined requirements), the simulation model, and the experiment. Each artifact is characterized by stages the artifact moves through to reach certain milestones and which are guarded by conditions. Thereby, the relations and constraints between artifacts become explicit. This is instrumental to check and ensure the consistency between conceptual model and simulation model, to automatically execute simulation experiments to probe the specified requirements, and to develop plans to provide goal-directed guidance to the user. We demonstrate the approach by using it to repeat an existing simulation study.

Due to the increasing complexity of simulation models, the experiments conducted with these models become more complex as well. To date, software support for reproducible complex simulation experiments is constrained to specific application domains and experiment types. As a step towards a one-size-fits-all solution, we express simulation experiments as pure functions. With random number generation wrapped in the state monad, we achieve bit-level reproducibility of simulation results even for complex experiment setups. Additionally, we show how simulation runs can be executed in parallel without jeopardizing reproducibility. While the approach is independent of concrete simulation backends, we illustrate it by using NetLogo in various complex simulation experiments, utilizing design of experiments, simulation-based optimization, and statistical model checking.

Provenance comprises information about how a product has been generated in a process. Thus, provenance information about an entire simulation study would support the interpretation and reuse of the developed simulation model and simulation experiments. However, current approaches only support to capture parts of the provenance information of a simulation study, i.e., the provenance information of the simulation data generated by individual simulation experiments. In this work, we extend a declarative, artifact-based workflow to capture provenance information about an entire simulation by observing the user in the study process. The workflow relates the building processes of central products of a simulation study, such as the conceptual model, requirements, input data, simulation model, and simulation experiments. Additionally, the workflow guides the modeler through the simulation study process while ensuring the consistency between its products. Further, we also develop different strategies to report the captured provenance information. These enable the user to respectively understand the simulation study at different levels of abstractions.

Agent- or individual-based models which are based on a continuous-time Markov chain semantics are increasingly receiving attention in simulation. To reduce computational cost, model aggregation techniques based on Markov chain lumping can be leveraged. However, for models with nested, attributed agents, and arbitrary functions determining their dynamics it is not trivial to find a partition that satisfies the lumpability conditions. Thus, we exploit the potential of the so-called super-individual approaches where sub-populations of agents are approximated by representatives based on some criteria for similarity, and propose a round-based execution scheme to balance speed and accuracy of the simulations. For realization we use an expressive rule-based modeling and simulation framework, evaluate the performance using a fish habitat model, and discuss open questions for future research.

BACKGROUND: To study cell biological phenomena which depend on diffusion, active transport processes, or the locations of species, modeling and simulation studies need to take space into account. To describe the system as a collection of discrete objects moving and interacting in continuous space, various particle-based reaction diffusion simulators for cell-biological system have been developed. So far the focus has been on particles as solid spheres or points. However, spatial dynamics might happen at different organizational levels, such as proteins, vesicles or cells with interrelated dynamics which requires spatial approaches that take this multi-levelness of cell biological systems into account. RESULTS: Based on the perception of particles forming hollow spheres, ML-Force contributes to the family of particle-based simulation approaches: in addition to excluded volumes and forces, it also supports compartmental dynamics and relating dynamics between different organizational levels explicitly. Thereby, compartmental dynamics, e.g., particles entering and leaving other particles, and bimolecular reactions are modeled using pair-wise potentials (forces) and the Langevin equation. In addition, forces that act independently of other particles can be applied to direct the movement of particles. Attributes and the possibility to define arbitrary functions on particles, their attributes and content, to determine the results and kinetics of reactions add to the expressiveness of ML-Force. Its implementation comprises a rudimentary rule-based embedded domain-specific modeling language for specifying models and a simulator for executing models continuously. Applications inspired by cell biological models from literature, such as vesicle transport or yeast growth, show the value of the realized features. They facilitate capturing more complex spatial dynamics, such as the fission of compartments or the directed movement of particles, and enable the integration of non-spatial intra-compartmental dynamics as stochastic events. CONCLUSIONS: By handling all dynamics based on potentials (forces) and the Langevin equation, compartmental dynamics, such as dynamic nesting, fusion and fission of compartmental structures are handled continuously and are seamlessly integrated with traditional particle-based reaction-diffusion dynamics within the cell. Thereby, attributes and arbitrary functions allow to flexibly describe diverse spatial phenomena, and relate dynamics across organizational levels. Also they prove crucial in modeling intra-cellular or intra-compartmental dynamics in a non-spatial manner, and, thus, to abstract from spatial dynamics, on demand which increases the range of multi-compartmental processes that can be captured.

When expressing concerns about the credibility of simulation studies, simulation data have been traditionally in the focus. However, what about another and, some might argue, even more central product of simulation studies, i.e., the simulation model itself? How can the credibility of a simulation model be assessed? Therefore, information about the process of generating a simulation model is needed. This provenance relates entities (or artifacts) and activities involved in the generating process. Based on simulation studies we will illuminate how the provenance of a simulation model relates the refinement, extension, composition, calibration and validation of simulation models to the diverse sources used in these processes. To exploit this information, unambiguously means for specifying entities play a central role. For example, a formal domain-specific language for modeling facilitates assessing and reusing simulation models. Similarly, a declarative domain-specific language for specifying simulation experiments, helps utilizing simulation experiments done with earlier models for future models. Thus, provenance, information about the past, does not only allow to understand the present, but also to design the future, in opening up new avenues for generating and analyzing simulation models.

Methods combining deterministic and stochastic concepts present an efficient alternative to a purely stochastic treatment of biochemical models. Traditionally, those methods split biochemical reaction networks into one set of slow reactions that is computed stochastically and one set of fast reactions that is computed deterministically. Applying those methods to multi-level models with dynamic nestings requires coping with dynamic reaction networks changing over time. In addition, in case of large populations of nested entities, stochastic events can still decrease the runtime performance significantly, as reactions of dynamically nested entities are inherently stochastic. In this paper, we apply a hybrid simulation algorithm combining deterministic and stochastic concepts to multi-level models including an approximation control. Further, we present an extension of this simulation algorithm applying an additional approximation by executing multiple independent stochastic events simultaneously in one simulation step. The algorithm has been implemented in the rule-based multi-level modeling language ML-Rules. Its impact on speed and accuracy is evaluated based on simulations performed with a model of Dictyostelium discoideum amoebas.

Reaction selection is a major and time consuming step of stochastic simulation algorithms. Current approaches focus on constant sets of reactions. However, in the case of multiple agents whose behaviors are governed by diverse reactions at multiple levels, where the number and structure of agents and the number of reactions varies during simulation. Therefore, we equip different variants of stochastic simulation algorithms with strategies to handle dynamic sets of reactions. We implement the next reaction method with a heap and the direct reaction method with two tree-based selection strategies, compare their performance, and discuss open questions for future research.

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 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-Rulesgit.informatik.uni-rostock.de/mosi/mlrules2ML-Rules Quickstart (Graphical Editor)git.informatik.uni-rostock.de/mosi/mlrules2-quickstartSESSLgit.informatik.uni-rostock.de/mosi/sessl and sessl.orgSESSL 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 Material: The supplementary material contains a more complex case study that exemplifies the usage of the SESSL binding for ML-Rules. Contact: [email protected].

In the particle-based simulation of cell-biological systems in continuous space, a key performance bottleneck is the computation of all possible intersections between particles. These typically rely for collision detection on solid sphere approaches. The behavior of cell biological systems is influenced by dynamic hierarchical nesting, such as the forming of, the transport within, and the merging of vesicles. Existing collision detection algorithms are found not to be designed for these types of spatial cell-biological models, because nearly all existing high performance parallel algorithms are focusing on solid sphere interactions. The known algorithms for solid sphere intersections return more intersections than actually occur with nested hollow spheres. Here we define a new problem of computing the intersections among arbitrarily nested hollow spheres of possibly different sizes, thicknesses, positions, and nesting levels. We describe a new algorithm designed to solve this nested hollow sphere intersection problem and implement it for parallel execution on graphical processing units (GPUs). We present first results about the runtime performance and scaling to hundreds of thousands of spheres, and compare the performance with that from a leading solid object intersection package also running on GPUs.

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.

Since simulation is a tool for generating data, a major task in executing simulations is to extract data from simulation runs. However, traditional methods of data extraction such as instrumenting the model code by hand or over-instrumenting the model and filtering data offline suffer from inflexibility and poor efficiency. To overcome these shortcomings, this paper promotes configurable targeted online data extraction, which also has special relevance in the field of real-time simulation. Nevertheless, there is no common terminology for the range of functions for targeted data extraction and there is no common concept for the implementation of flexible and efficient solutions. By decomposing the data extraction problem and by formalizing generalizable parts, this paper provides a conceptional framework for the assessment and implementation of language-based data extraction solutions. It turns out that data extraction can be decomposed into a sequential and a structural dimension, both of which having operations for selection, extraction, and windowed aggregation. As a proof of concept, the functionality of existing data extraction languages is analyzed using the proposed terminology.

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.

Canonical WNT/β-catenin signaling is a central pathway in embryonic development, but it is also connected to a number of cancers and developmental disorders. Here we apply a combined in-vitro and in-silico approach to investigate the spatio-temporal regulation of WNT/β-catenin signaling during the early neural differentiation process of human neural progenitors cells (hNPCs), which form a new prospect for replacement therapies in the context of neurodegenerative diseases. Experimental measurements indicate a second signal mechanism, in addition to canonical WNT signaling, being involved in the regulation of nuclear β-catenin levels during the cell fate commitment phase of neural differentiation. We find that the biphasic activation of β-catenin signaling observed experimentally can only be explained through a model that combines Reactive Oxygen Species (ROS) and raft dependent WNT/β-catenin signaling. Accordingly after initiation of differentiation endogenous ROS activates DVL in a redox-dependent manner leading to a transient activation of down-stream β-catenin signaling, followed by continuous auto/paracrine WNT signaling, which crucially depends on lipid rafts. Our simulation studies further illustrate the elaborate spatio-temporal regulation of DVL, which, depending on its concentration and localization, may either act as direct inducer of the transient ROS/β-catenin signal or as amplifier during continuous auto-/parcrine WNT/β-catenin signaling. In addition we provide the first stochastic computational model of WNT/β-catenin signaling that combines membrane-related and intracellular processes, including lipid rafts/receptor dynamics as well as WNT- and ROS-dependent β-catenin activation. The model's predictive ability is demonstrated under a wide range of varying conditions for in-vitro and in-silico reference data sets. Our in-silico approach is realized in a multi-level rule-based language, that facilitates the extension and modification of the model. Thus, our results provide both new insights and means to further our understanding of canonical WNT/β-catenin signaling and the role of ROS as intracellular signaling mediator.

Unambiguous experiment descriptions are increasingly required for model publication, as they contain information important for reproducing simulation results. In the context of model composition, this information can be used to generate experiments for the composed model. If the original experiment descriptions specify which model property they refer to, we can then execute the generated experiments and assess the validity of the composed model by evaluating their results. Thereby, we move the attention to describing properties of a model's behavior and the conditions under which these hold, i.e., its semantics. We illuminate the potential of this concept by considering the composition of Lotka-Volterra models. In a first prototype realized for JAMES II, we use ML-Rules to describe and execute the Lotka-Volterra models and SESSL for specifying the original experiments. Model properties are described in continuous stochastic logic, and we use statistical model checking for their evaluation. Based on this, experiments to check whether these properties hold for the composed model are automatically generated and executed.

Conducting simulation studies can mean to execute a multitude of parameter configurations, for each of these we may need to execute a vast number of replications, and each single replication may mean the need to process a significant amount of data. Here, we propose a stream-based architecture that aligns data processing and buffering with the actual data usage during simulation to make the most of available memory. This turns away from the first-write-then-read approach, often utilizing databases or plain files as temporary storage. Instead, data are processed on the fly. By introducing a processing graph, which distinguishes between buffering and processing nodes, a flexible analysis of simulation data is achieved. As the data are processed close to their generation, the developed architecture fits well to a distributed execution of simulation studies. We illustrate how the stream-based architecture integrates into simulation workflows.

The state of a model may strongly vary during simulation, and with it also the simulation's computational demands. Adapting the simulation algorithm to these demands at runtime can therefore improve the overall performance. Although this is a general and cross-cutting concern, only few simulation systems offer re-usable support for this kind of runtime adaptation. We present a flexible and generic mechanism for the runtime adaptation of component-based simulation algorithms. It encapsulates simulation algorithms applicable to a given problem and employs reinforcement learning to explore the algorithms' suitability during a simulation run. We evaluate the approach by executing models from two modeling formalisms used in computational biology.

Abstract The π -calculus, in particular its stochastic version the stochastic π -calculus, is a common modeling formalism to concisely describe the chemical reactions occurring in biochemical systems. However, it remains largely unexplored how to transform a biochemical model expressed in the stochastic π -calculus back into a set of meaningful reactions. To this end, we present a two step approach of first translating model states to reaction sets and then visualizing sequences of reaction sets, which are obtained from state trajectories, in terms of reaction networks. Our translation from model states to reaction sets is formally defined and shown to be correct, in the sense that it reflects the states and transitions as they are derived from the continuous time Markov chain-semantics of the stochastic π -calculus. Our visualization concept combines high level measures of network complexity with interactive, table-based network visualizations. It directly reflects the structures introduced in the first step and allows modelers to explore the resulting simulation traces by providing both: an overview of a network’s evolution and a detail inspection on demand.

It is widely accepted that lipid rafts promote receptor clustering and thereby facilitate signaling transduction. The role of lipid rafts in inducing and promoting receptor accumulation within the cell membrane has been explored by several computational and experimental studies. However, it remains unclear whether lipid rafts influence the recruitment and binding of proteins from the cytosol as well. To provide an answer to this question a spatial membrane model has been developed based on cellular automata. Our results indicate that lipid rafts indeed influence protein receptor bindings. In particular processes with slow dissociation and binding kinetics are promoted by lipid rafts, whereas fast binding processes are slightly hampered. However, the impact depends on a variety of parameters, such as the size and mobility of the lipid rafts, the induced slow down of receptors within rafts, and also the dissociation and binding kinetics of the cytosolic proteins. Thus, for any individual signaling pathway the influence of lipid rafts on protein binding might be different. To facilitate analyzing this influence given a specific pathway, our approach has been generalized into LiRaM, a modeling and simulation tool for lipid rafts models.

The integration of workflows into modeling and simulation tools promises to provide easier reproduction and provenance of simulation data and its generating process. We present the use of workflow templates and frames realized in WORMS to support and document activities involved in executing simulation experiments. Thereby we make use of functionalities provided by the validation environment FAMVal and the plug-in-based modeling and simulation framework JAMES II. The role of workflows, templates, and frames in modeling and simulation research will be illuminated by a simple simulation study in which the amount of a chemical species in the equilibrium state shall be maximized.

Dry-lab experimentation is being increasingly used to complement wet-lab experimentation. However, conducting dry-lab experiments is a challenging endeavor that requires the combination of diverse techniques. JAMES II, a plug-in-based open source modeling and simulation framework, facilitates the exploitation and configuration of these techniques. The different aspects that form an experiment are made explicit to facilitate repeatability and reuse. Each of those influences the performance and the quality of the simulation experiment. Common experimentation pitfalls and current challenges are discussed along the way.

Stochastic simulations may require many replications until their results are statistically significant. Each replication corresponds to a standalone simulation job, so that these can be computed in parallel. This paper presents a grid-inspired approach to distribute such independent jobsover a set of computing resources that host simulation services, all of which are managed by a central master service. Our method is fully integrated with alternative ways of distributed simulation in JAMES II, hides all execution details from the user, and supports the coarse-grained parallel execution of any sequential simulator available in JAMES II. A thorough performance analysis of the new execution mode illustrates its efficiency.

Compartments play an important role in molecular and cell biology modeling, which motivated the development of BETA-BINDERS, a formalism which is an extension of the pi-CALCULUS. To execute BETA-BINDERS models, sophisticated simulators are required to ensure a sound and efficient execution. Parallel and distributed simulation represents one means to achieve the later. However, stochastically scheduled events hamper the definition of look aheads for a conservative parallel synchronization scheme, while an optimistic parallel simulation implies expensive rollback operations due to the dynamic structures of BETABINDERS models. Therefore, a time-bounded window approach is suggested, which allows the different logical processes to proceed optimistically up to a barrier. Rollbacks are thus temporally constrained. In addition, the dynamic structure of BETA-BINDERS models requires a special state handling. BETA-BINDERS models and states are represented as tree structures to facilitate state updates and rollbacks by the simulation engine.

Smart environments are characterized by dynamic ensembles of devices that offer individual services to the user in an unobtrusive manner. For more advanced services often the cooperation of devices is required which can be accomplished by composition of services. To keep the composition process as unobtrusive as possible it must be fast and ressource saving. AI planning is one possibility to realize service composition. Four different planners have been evaluated referring to execution time. The evaluation has been based on an abstract simulation model that reflects some key characteristics of services in smart environments, e.g. number of services and distribution of pre- and postconditions of these services. The evaluation results showed that planners like UCPOP, SGP, and particularly blackbox, are suitable for composing services in time in a typical smart environment. However, their run time behavior is irregular and additionally differ with respect to how fast they recognize that no plan exist. These cases require specific strategies to avoid unnecessary resource consumption.

Smart Environments are designed to proactively assist their users. One way to achieve this is by composing sequences of potential user actions and of devices' actions at run time. This requires the actions to be specified in a declarative manner. A favorable formalism for declarative descriptions are semantic web services. In this paper, we describe our approach to proactive assistance in smart environments and explain how semantic web services can be employed as the basic building blocks for such an environment. We provide a state-of-the-art review of existing semantic web service technologies and discuss how they should be enhanced to suit our needs.

Efficiently simulating discrete-event models in a parallel and distributed manner is a challenging endeavour. On one hand, various factors, such as hardware infrastructure or model characteristics, have to be considered. On the other hand, there is a wide variety of algorithms which address subproblems of parallel and distributed simulation and whose performance depends on the application at hand. We illustrate the resulting difficulties with respect to the development of parallel and distributed simulation systems and argue that the simulation of distributed simulation systems is a feasible approach to alleviate them. To underpin this, we introduce SIMSIM, a sequential simulator for parallel and distributed simulation systems. SIMSIM's pertinency is illustrated by the development of a load balancing algorithm for PDEVS. The algorithm's performance is analysed using SIMSIM and the predicted performance is compared to the performance of its implementation in the simulation system JAMES II

Summary form only given. Systems biology is aimed at analyzing the behavior and interrelationships of biological systems and is characterized by combining experimentation, theory, and computation. Multi-level models describe systems at different levels of organization and abstraction. To apply them in systems biology implies typically that concentration changes as well as the discrete behavior of single entities and their interactions need to be taken into account in modeling and simulation. A variety of approaches have been developed offering specific perspectives on cellular systems in modeling and simulation. We explore how far multi-level aspects are already supported and identify challenges yet to be met

Testing is an obligatory step in developing multiagent systems. For testing multiagent systems in virtual, dynamic environments, simulation systems are required that support a modular, declarative construction of experimental frames, that facilitate the embedding of a variety of agent architectures and that allow an efficient parallel, distributed execution. We introduce the system JAMES (a Java based agent modeling environment for simulation). In JAMES, agents and their dynamic environment are modeled as reflective, time-triggered state automata. Its possibilities to compose experimental frames based on predefined components, to express temporal interdependencies, to capture the phenomenon of proactiveness and reflectivity of agents are illuminated by experiments with planning agents. The underlying planning system is a general-purpose system, about which no empirical results exist besides traditional static benchmark tests. We analyze the interplay between heuristics for selecting goals, viewing range, commitment strategies, explorativeness, and trust in the persistence of the world and uncover properties of the the agent, the planning engine, and the chosen test scenario: TILEWORLD.