About
70
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863
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Introduction
I work as an engineer at Limbus Medical Technologies and have a research background in modeling & simulation, artificial intelligence, and software engineering.
See my homepage (http://ewald.es) for details.
Additional affiliations
April 2012 - March 2014
November 2010 - March 2012
January 2006 - November 2010
Universität Rostock
Position
- PhD Student
Publications
Publications (70)
An algorithm portfolio is a set of algorithms that are bundled together for increased overall performance. While being mostly applied to computationally hard problems so far, we investigate portfolio selection for simulation algorithms and focus on their application to adaptive simulation replication. Since the portfolio selection problem is itself...
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....
linVar aggregates information about genetic variants and their relation to human diseases and forms a valuable resource for clinical diagnostics. The assessment of variants, e.g., as being benign or pathogenetic, changes over time. We collected variant classification histories of ClinVar releases and used those to derive discrete-time Markov chains...
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...
Background: Deciphering the monogenetic causes of neurodevelopmental disorders (NDD) is an important milestone to offer personalized care. But the plausibility of reported candidate genes in exome studies often remains unclear, which slows down progress in the field.
Methods: We performed exome sequencing (ES) in 198 cases of NDD. Cases that remain...
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 p...
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 neura...
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 ref...
Simulation experiments involve various sub-tasks, e.g., parameter optimization, simulation execution, or output data analysis. Many algorithms can be applied to such tasks, but their performance depends on the given problem. Steady state estimation in systems biology is a typical example for this: several estimators have been proposed, each with it...
This article introduces SESSL (&lowbarS;imulation &lowbarE;xperiment &lowbarS;pecification via a &lowbarS;cala &lowbarL;ayer), an embedded domain-specific language for simulation experiments. It serves as an additional software layer between users and simulation systems and is implemented in Scala. SESSL supports multiple simulation systems and off...
Analyzing simulation algorithm performance is cumbersome: execute some runs, observe a performance metric, and analyze the results. Often, the results motivate follow-up experiments, which in turn may lead to additional experiments, and so on. This time-consuming and error-prone process can be automated with planning approaches from artificial inte...
Standardized model exchange formats give practitioners the freedom to choose the most suitable tool and facilitate both cross-validation and reproduction of simulation results. On the other hand, standardization necessarily implies a compromise between the capabilities of individual modeling languages and a common ground of concepts and underlying...
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 run...
In component-based simulation systems, simulation runs are usually executed by combinations of distinct components, each solving a particular sub-task. If multiple components are available for a given sub-task (e.g., different event queue implementations), a simulation system may rely on an au-tomatic selection mechanism, on a user decision, or — i...
Setting up simulation experiments is hard, even more so as simulation systems usually offer only custom interfaces for this task (e.g., a graphical user interface or a programming interface). This steepens the learning curve for experimenters, who have to get accustomed with the idiosyncrasy of each simulation system they want to experiment with. I...
Simulation algorithms often expose various numerical parameters, e.g., to control the size of auxiliary data structures or to configure certain heuristics. While this allows to fine-tune a simulator to a given model, it also makes simulator configuration more complex. For example, determining suitable default parameters from a multi-dimensional par...
Demographic heterogeneity, i. e. differing mortality and fertility among subpopulations, is an important issue in stochastic demographic forecasting. Common approaches typically use the variables age and sex to construct subpopulations, but this might be insufficient and induce projection error. Many studies show significant differences in mortalit...
Automated transformation between modeling languages is often useful, e.g., to make tools (like simulators) based on one language applicable to models defined in other languages. However, several problems arise when the expressive powers of the modeling languages differ. We consider the automated transformation between models specified in the system...
Even the most carefully configured simulation algorithm may perform badly unless its configuration is adapted to the dynamics of the model. To overcome this problem, we apply methods from reinforcement learning to continuously re-configure an ML-Rules simulator at runtime. ML-Rules is a rule-based modeling language primarily targeted at multi-level...
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 prob...
The general challenges of experimenting with simulation algorithms have already been discussed in chapter 3 (p. 93). This chapter builds up on that by briefly outlining how these challenges are currently met by the experimentation layer of JAMES II (sec. 7.1). Section 7.2 introduces a simple yet powerful algorithm selection technique that can be pl...
While the case study on SSAs (ch. 9, p. 273) ought to show the benefits and limitations of the developed methods for simulation algorithm selection, the following study shall mainly illustrate that these methods are not restricted to SSAs. They rather provide a general toolkit to analyze simulation algorithm performance and thereby allow us to solv...
Chapter 2 already illustrated why only an empirical approach to algorithm selection is likely to succeed in the foreseeable future. Now that monitoring mechanisms are in place to feed observations into a dedicated performance database, as described in chapter 5, it has to be discussed how this data can be analyzed so that suitable selection mapping...
Chapter 2 illustrates that the search for a good selection mapping depends on reliable performance data. To collect such data efficiently and to ensure that it is indeed trustworthy involves a whole range of additional techniques, which will be briefly discussed in the following: “Unfortunately, as many researchers have already discovered, the fiel...
This final chapter of the thesis’ methodological part covers concrete steps towards automated simulation algorithm selection. The chapter describes a prototypical extension of the JAMES II registry, so that automated algorithm selection is supported in a way that does not affect the existing code base. It describes the management and exploitation o...
This chapter shows how the methods developed in chapter 4 to 8 can be applied to stochastic simulation algorithms (SSA), which have already been discussed briefly in section 1.3.1 (p. 7). JAMES II offers several kinds of simulators for the field of computational systems biology (see [308]). Some of them–e.g., those for models expressed in stochasti...
This chapter describes how the ASP entities discussed in section 2.1.4 (p. 32) can be recorded and stored, i.e., it covers the parts of the simulation algorithm selection framework that are highlighted in figure 5.1. Storing performance data in some common, readily accessible format is one of the major premises for reproducible and comparable perfo...
The background part surveyed methods for automated algorithm selection (ch. 2) and detailed the major challenges and techniques of empirically analyzing the performance of simulation algorithms (ch. 3). This part tackles the central issue of the thesis, namely the automatic selection of simulation algorithms. It mainly treats the construction of a...
The research domains that are concerned with forms of algorithm selection are diverse and highly differentiated. A reason for this might be the fundamental importance of the problem in many fields of computer science, another one the divergence between theoretical research and practical implementations in the various application areas: “Many algori...
Since the publication of Gillespie’s direct method, diverse methods have been developed to improve the performance of stochastic simulation methods and to enter the spatial realm. In this paper we discuss a spatial τ-leaping variant (Sτ) that extends the basic leap method. Sτ takes reaction and both outgoing and incoming diffusion events into accou...
To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments, w...
Current and upcoming architectures of desktop and high performance computers offer increasing means for parallel execution. Since the computational demands induced by ever more realistic models increase steadily, this trend is of growing importance for systems biology. Simulations of these models may involve the consideration of multiple parameter...
Predicting future populations and their structure is a central theme in demography. It is related to public health issues, political decision-making, or urban planning. Since these predictions are concerned with the evolution of a complex system, they exhibit a considerable uncertainty. Accounting for this inherent uncertainty is crucial for subseq...
The notion of logical processes is a widely used modeling paradigm in parallel and distributed discrete-event simulation. Yet, the comparison among different simulation algorithms for LP models still remains difficult. Most simulation systems only provide a small subset of available algorithms, which are usually selected and tuned towards specific...
Simulation algorithm implementations are usually evaluated by experimental performance analysis. To conduct such studies is a challenging and time-consuming task, as various impact factors have to be controlled and the resulting algorithm performance needs to be analyzed. This problem is aggravated when it comes to comparing many alternative implem...
The increase and diversity of simulation methods bears witness of the need for more efficient discrete event simulations in computational biology-but how efficient are those methods, and how to ensure an efficient simulation for a concrete model? As the performance of simulation methods depends on the model, the simulator, and the infrastructure, g...
Simulation replication is a necessity for all stochastic simulations. Its efficient execution is particularly important when additional techniques are used on top, such as optimization or sensitivity analysis. One way to improve replication efficiency is to ensure that the best configuration of the simulation system is used for execution. A selecti...
While simulationists devise ever more efficient simulation algorithms for specific applications and infrastructures, the problem of automatically selecting the most appropriate one for a given problem has received little attention so far. One reason for this is the overwhelming amount of performance data that has to be analyzed for deriving suitabl...
The notion of logical processes (LPs) is a widely used modeling paradigm in parallel and distributed discrete-event simulation (PDES). Nevertheless the comparison among different simulation algorithms for LP models still remains difficult: there are too many combinations of algorithms to be explored, often simulation systems only provide a small su...
Keywords: DEVS, PDEVS, dynDEVS, dynPDEVS, dynNPDEVS, rho-DEVS, ML-DEVS, EPI-DEVS, paced, unpaced, abstract threaded, abstract sequential, hierarchical sequential, flat sequential, partitioning, load-balancing, event queues, requeue operation, JAMES II
This short tutorial shall guide developers through the core principles of extending the mod- eling and simulation framework James II . Knowledge of Java is assumed. The most important extension points are described and their interplay is explained briefly. The interested reader is referred to the user manual and the source code documentation for ad...
Simulations often depend heavily on random numbers, yet the impact of random number generators is recognized sel- dom. The generation of random numbers for simulations is not trivial, as the quality of each algorithm depends on the simulation scenario. Therefore, simulation environments for large-scale experimentation with safety-critical mod- els...
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 eff...
Distributed simulation has emerged as an important instrument for studying large-scale complex systems. Such systems inherently consist of a large number of components, which operate in a large shared state space interacting with it in highly dynamic and unpredictable ways. Optimising access to the shared state space is crucial for achieving effici...
Stochastic simulation algorithms (SSA) are popular methods for the simulation of chemical reaction networks, so that various enhancements have been introduced and evaluated over the years. However, neither theoretical analysis nor empirical comparisons of single implementations suffice to capture the general performance of a method. This makes choo...
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 servic...
Using modeling and simulation techniques in game development can look back on a comparably long history, starting in the early 1970s. In contrast to this long tradition of combining games and simulations, it is usually not made explicit which kind of simulation is used, which models are fundament of the simulation, and which role the simulation and...
No simulation algorithm will deliver best performance under all circumstances, so simulation systems often offer execution alternatives to choose from. This leads to another problem: how is the user supposed to know which algorithm to select? The need for an automated selection mechanism is often neglected, as many simulation systems are focused on...
The application of parallel and distributed simulation techniques is often limited by the amount of parallelism available in the model. This holds true for large-scale cell-biological simulations, a field that has emerged as data and knowledge concerning these systems increases and??biologists call for tools to guide wet-lab experimentation. A prom...
As data and knowledge about cell-biological systems increases so does the need for simulation tools to support a hypothesis driven wet-lab experimentation. Discrete event simulation has received a lot of attention lately, however, often its application is hampered by its lack on performance. One solution are parallel, distributed approaches, howeve...
Spatial dynamics receive increasing attention in Systems Biology and require suitable modeling and simulation approaches. So far, modeling formalisms have focused on population-based approaches or place and move individuals relative to each other in space. SpacePi extends the π calculus by time and space. π processes are embedded into a vector spac...
In computational biology there is an increasing need to combine micro and macro views of the system of interest. Therefore, explicit means to describe micro and macro level and the downward and upward causation that link both are required. Multi-Level-DEVS (or m^-DEVS) supports an explicit description of macro and micro level, information at macro...
Diverse modelling formalisms are applied in Computational Biology. Some describe the biological system in a continuous manner, others focus on discrete-event systems, or on a combination of continuous and discrete descriptions. Similarly, there are many simulators that support different formalisms and execution types (e.g. sequential, parallel-dist...
With Systems Biology, a promising new application area for modeling and simulation emerges. Today's biologists are facing huge amounts of data delivered at different levels of detail by a multitude of advanced experimentation techniques. The Systems Biology approach copes with this information by cycling through phases of forming hypothesis, constr...
Distributed simulation can speed up the execution of models significantly. We introduce a new simulation algorithm and present partitioning and load balancing techniques that are tailored to the efficient distributed execution of PDEVS. We base our elaborations on the idea of minimizing interprocessor communication, since this is a major bottleneck...
In computational biology there is an increasing need to combine micro and macro views of the system of interest. Therefore, explicit means to describe micro and macro level and the downward and upward causation that link both are required. Multi-Level-Devs (or ml-Devs) supports an explicit description of macro and micro level, information at macro...
The simulation system JAMES II is aimed at supporting a range of modeling formalisms and simulation engines. The partitioning of models is essential for distributed simulation. A suitable partition depends on model, hardware, and simulation algorithm characteristics. Therefore, a partitioning layer has been created in JAMES II which allows to plug...
Motivated by the requirements of molecular biological ap- plications, 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 �-DEVS. Dynamic ports and multi-couplings are introduced whose combination allows models to r...
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 who...
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 ubproblems of parallel and distributed simulation and whos...
Distributed simulation is an important instrument for studying multi-agent systems (MAS). Such large scale MAS simulations often have a large shared state space. Moreover, the shared state and the access pattern of agent simulations both are highly dynamic and unpredictable. Optimising access to the shared data is crucial for achieving efficient si...
Simulation has found widespread use for experimentation and exploration of the possible impacts of a variety of conditions on a system. In contrast, formal verification is concerned with proving or disproving the correctness of a system with respect to a certain property, using mathematical and logical methods. @InProceedings{batt_et_al:DSP:2006:72...
Distributed simulation is an important instrument for studying multi-agent systems (MAS). Such large scale MAS simulations often have a large shared state space. Moreover, the shared state and the access pattern of agent simulations both are highly dynamic and unpredictable. Optimising access to the shared data is crucial for achieving efficient si...
Projects
Projects (3)
The analysis of large stochastic simulation data sets is a challenging task, especially when spatial and temporal dependencies have to be investigated at different levels of granularity, as is the case in analyzing cell biological systems. We approach this problem by means of Visual Analytics. Our main focus will be intertwining interactive visualization methods and discrete event simulation processes to allow for a sustained visual feedback and user control. Therefore, innovative strategies will be developed in both fields:
>Visualization: This includes the multi-level visualization of multi-variate data in space and time in combination with their provenance and quality.
>Simulation: This refers to model partitioning strategies and the combination and (re-)configuration of simulators induced automatically and by user interaction.
These new strategies will help analyzing simulation data of cell biological systems in space and time, but also directly steering the simulation process, and thus the data generating process. Thereby, currently available approaches of Visual Analytics will be enriched by two important aspects:
> Use of cell biological data originating from a discrete-event simulated system (drylab) in contrast to data produced by a real system (wet-lab)
> Exploiting simulation methods as a new source for analytical reasoning
General framework for modeling and simulation (discrete, hybrid, continuous models - research with M&S, for M&S, and teaching)
