Jan Himmelspach

Flexible experimentation in the modeling and simulation framework JAMES II—implications for computational systems biology

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.

Experiments with Single Core, Multi-core, and GPU Based Computation of Cellular Automata

Cellular automata are a well-known modeling formalismexploited in a wide range of application areas. In many ofthose, the complexity of models hampers a thorough analysis ofthe system under study. Therefore, efficient simulation algorithmsare required. We present here a comparison of seven different simulation algorithms for cellular automata: the classical “full” simulator, the classical “discrete event” simulator, a threaded (multi core) variant of each of these, an adaptable threadedvariant, and a GPU based algorithm with and without readbackof calculated states. The comparison is done based on the M&Sframework JAMES II by using a set of well-known models.

A flexible and scalable experimentation layer

Modeling and simulation frameworks for use in different application domains, throughout the complete development process, and in different hardware environments need to be highly scalable. For achieving an efficient execution, different simulation algorithms and data structures must be provided to compute a concrete model on a concrete platform efficiently. The support of parallel simulation techniques becomes increasingly important in this context, which is due to the growing availability of multi-core processors and network-based computers. This leads to more complex simulation systems that are harder to configure correctly. We present an experimentation layer for the modeling and simulation framework JAMES II. It greatly facilitates the configuration and usage of the system for a user and supports distributed optimization, on-demand observation, and various distributed and non-distributed scenarios.

A component-based simulation layer for JAMES

If a model shall be executed in a parallel, distributed instead of a sequential manner, typically the entire simulation engine has to be exchanged. To adapt the simulation layer more easily to the requirements of a concrete model to be run in a specific environment a component based simulation layer has been developed for JAMES. A set of different simulator components demonstrates that a component-based design facilitates the exchange of simulators and their combination.

A Grid-Inspired Mechanism for Coarse-Grained Experiment Execution

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 jobs over 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.

Mic-core: a tool for microsimulation

Microsimulation is an increasingly popular tool in the social sciences. Individual behavior is described by a (commonly stochastic) model and subsequently simulated to study outcomes on the aggregate level. Demographic projections are a prominent area of application. Despite numerous available tools often new software is designed and implemented for specific applications. In this paper we describe how a modeling and simulation framework, JAMES II, was used to create a specialized tool for population projections, the Mic-core. Reusing validated and well-tested modeling and simulation functionality significantly reduced development time while keeping performance levels high. We document how the Mic-core was built as plug-ins to JAMES II and illustrate the performance of the resulting tool. We demonstrate how the concept of a modeling and simulation framework enabled successful software reuse of available functionality and briefly report of future work.

Introducing variable ports and multi-couplings for cell biological modeling in DEVS

Motivated by the requirements of molecular biological applications, we are suggesting an extension of the DEVS formalism. Starting with DYNDEVS a reflective variant of DEVS which supports dynamic behavior, composition, and interaction pattern, we develop rho-DEVS. Dynamic ports and multi-couplings are introduced whose combination allows models to reflect significant state changes to the outside world and enabling or disabling certain interactions at the same time. An abstract simulator describes the operational semantics of the developed formalism, and the Tryptophan operon model illustrates the developed ideas and concepts

A Discussion on Experimental Model Validation

Model validation is essential in modeling and simulation. It “finalizes” the modeling process, and provides the base for reliable experiments with the model, and thus to gain trustworthy insights of the system under study. Diverse techniques have been developed addressing different needs and are used during different phases in the modeling and simulation life cycle. Experimental model validation depends on the execution of the model. Thus, the peculiarities of the simulation engine might influence the results of experiments and thus have to be taken into account. Execution also underlies some techniques of software validation which can be adopted for experimental model validation. New approaches apply model checking techniques for trace inspections, and emphasize the importance of an explicit description of requirements. All of this implies new requirements for systems intended to support experimental model validation.

Toward a language for the flexible observation of simulations

Simulation studies typically imply the generation and interpretation of data. Collecting, storing, and filtering data can be expensive. Therefore, it is important to allow a user to specify these processes flexibly depending on the modeling language, the model, and the objective of the simulation study. An instrumentation language is presented and applied to collect, aggregate, store, and filter data generated during experimentation with models specified in ML-Rules, a rule-based multilevel modeling language for cell biological systems.

Processing dynamic PDEVS models

Structural changes, i.e. the creation and deletion of components, and the change of interactions are salient features of adaptive systems. To model and specify these systems, variable structure models are required, i.e. models that entail in their own description the possibility to change their structure. To execute these models, a simulator with a clear semantic of intertwining structural and non-structural changes is required. In JAMES (Java-based agent modeling environment for simulation), different simulator components, e.g., for paced, unpaced, sequential, and parallel simulation, support the continuous use of models and simulation from specification to testing and a composition of the simulation engine on demand. Two types of simulator components for variable structure models are developed and integrated into the simulation layer; the implications are discussed.