Stefan Rybacki

Rule-based multi-level modeling of cell biological systems

BackgroundProteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted attention due to enabling a concise and compact description of biochemical systems. In addition, it allows different methods for model analysis, since more than one semantics can be defined for the same syntax.ResultsMulti-level modeling implies the hierarchical nesting of model entities and explicit support for downward and upward causation between different levels. Concepts to support multi-level modeling in a rule-based language are identified. To those belong rule schemata, hierarchical nesting of species, assigning attributes and solutions to species at each level and preserving content of nested species while applying rules. Further necessities are the ability to apply rules and flexibly define reaction rate kinetics and constraints on nested species as well as species that are nested within others. An example model is presented that analyses the interplay of an intracellular control circuit with states at cell level, its relation to cell division, and connections to intercellular communication within a population of cells. The example is described in ML-Rules - a rule-based multi-level approach that has been realized within the plug-in-based modeling and simulation framework JAMES II.ConclusionsRule-based languages are a suitable starting point for developing a concise and compact language for multi-level modeling of cell biological systems. The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness. Rule schemata allow a concise and compact description of complex models. As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.

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.

Special Section on Uncertainty and Parameter Space Analysis in Visualization: Supporting the integrated visual analysis of input parameters and simulation trajectories

The visualization of simulation trajectories is a well-established approach to analyze simulated processes. Likewise, the visualization of the parameter space that configures a simulation is a well-known method to get an overview of possible parameter combinations. This paper follows the premise that both of these approaches are actually two sides of the same coin; since the input parameters influence the simulation outcome, it is desirable to visualize and explore both in a combined manner. The main challenge posed by such an integrated visualization is the combinatorial explosion of possible parameter combinations. It leads to insurmountably high simulation runtimes and screen space requirements for their visualization. The Visual Analytics approach presented in this paper targets this issue by providing a visualization of a coarsely sampled subspace of the parameter space and its corresponding simulation outcome. In this visual representation, the analyst can identify regions for further drill-down and thus finer subsampling. We aid this identification by providing visual cues based on heterogeneity metrics. These indicate in which regions of the parameter space deviating behavior occurs at a more fine-grained scale and thus warrants further investigation and possible re-computation. We demonstrate our approach in the domain of systems biology by a visual analysis of a rule-based model of the canonical Wnt signaling pathway that plays a major role in embryonic development. In this case, the aim of the domain experts was to systematically explore the parameter space to determine those parameter configurations that match experimental data sufficiently well.

WorMS- a framework to support workflows in M&S

Workflows are a promising mean to increase the quality of modeling and simulation (M&S) products such as studies and models. In exploiting workflows for M&S, requirements arise that need to be reflected in the structure and components of a workflow supporting framework, such as WORMS (WORkflows for Modeling and Simulation). In WORMS, we adapt concepts of business process modeling and scientific workflows. Particular attention is given to extensibility and flexibility which is supported by a plug-in based design and by selecting workflow nets as intermediate representation for workflows. The first application of WORMS has been realized for the modeling and simulation framework JAMES II. A small case-study illuminates the role of components and their interplay during evaluating a cell biological model.

Using workflows in M&S software

The usage of workflows to standardize processes, as well as to increase their efficiency and the quality of the results is a common technique. So far it has only been rarely applied in modeling and simulation. Herein we argue for employing this technique for the creation of various products in modeling and simulation. This includes the creation of models, simulations, modeling languages, and modeling and simulation software modules. Additionally we argue why roles should be incorporated into modeling and simulation workflows, provide a list of requirements for the workflow management system and sketch first steps in how to integrate workflows into the M&S framework JAMES II.

Automatic Runtime Adaptation for Component-Based Simulation Algorithms

The state and structure of a model may vary during a simulation and, thus, also its computational demands. Adapting simulation algorithms to these demands at runtime can therefore improve their performance. While this is a general and cross-cutting concern, only few simulation systems offer reusable 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’ performance during a simulation run. We evaluate our approach on a modeling formalism from computational biology and on a benchmark model defined in PDEVS, thereby investigating a broad range of options for improving its learning capabilities.

Template and Frame Based Experiment Workflows in Modeling and Simulation Software with WORMS

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.

Interactive visual exploration of simulator accuracy: a case study for Stochastic Simulation Algorithms

Visual Analytics offers various interesting methods to explore high dimensional data interactively. In this paper we investigate how it can be applied to support experimenters and developers of simulation software conducting simulation studies. In particular the usage and development of approximate simulation algorithms poses several practical problems, e.g., estimating the impact of algorithm parameters on accuracy or detecting faulty implementations. To address some of those problems, we present an approach that allows to relate configurations and accuracy visually and exploratory. The approach is evaluated by a brief case study, focusing on the accuracy of Stochastic Simulation Algorithms.

An explicit numerical scheme to efficiently simulate molecular diffusion in environments with dynamically changing barriers

Despite temporal changes in the quantities of molecules, the functioning of cells also depends on their distribution within cells and in their extracellular environment. The dynamics of molecules are often governed by diffusion in heterogeneous environments consisting of dynamically changing impenetrable barriers (excluded volumes). This provides a challenge for efficient simulations of cellular processes with large numbers of molecules. To model the diffusion of molecular mass in consideration of excluded volumes, we here present an explicit numerical scheme that approximates the diffusion equation by using cellular automata. Because this approach represents molecular diffusion at the macroscopic scale, it is more amenable for efficient simulations than comparable microscopic approaches that treat diffusing molecules individually. In contrast to implicit numerical schemes (macroscopic approach), our approach is capable of accounting for excluded volumes, even if those exhibit a dynamic of their own, without increasing computational costs. The presented approach can easily be integrated into certain types of spatio-temporal multiscale models, as demonstrated by an existing model investigating cancer progression. Thereby, it allows to take the spatial effects of a heterogeneous environment on diffusing molecules into account.