Matthias Jeschke

Combining micro and macro-modeling in DEVS for computational biology

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 level can be accessed from micro level and vice versa, micro models can be synchronously activated by the macro model and also the micro models can trigger the dynamics at macro level. To link both levels, different methods are combined, to those belong, value coupling, synchronous activations, variable ports, and invariants. The execution semantic of the formalism is given by an abstract simulator and its use is illustrated based on an small extract of the Wnt pathway.

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.

Exploring the performance of spatial stochastic simulation algorithms

Since the publication of Gillespies 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 @t-leaping variant (S@t) that extends the basic leap method. S@t takes reaction and both outgoing and incoming diffusion events into account when calculating a leap candidate. A performance analysis shall reveal details on the achieved success in balancing speed and accuracy in comparison to other methods. However, performance analysis of spatial stochastic algorithms requires significant effort - it is crucial to choose suitable (benchmark) models and to carefully define model and simulation setups that take problem and simulation design spaces into account.

Large-Scale Design Space Exploration of SSA

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 choosing an appropriate algorithm very hard for anyone who is not an expert in the field, especially if the system provides many alternative implementations. We argue that this problem can only be solved by thoroughly exploring the design spaces of such algorithms. This paper presents the results of an empirical study, which subsumes several thousand simulation runs. It aims at exploring the performance of different SSA implementations and comparing them to an approximation via ?-Leaping, while using different event queues and random number generators.

Parallel and Distributed Spatial Simulation of Chemical Reactions

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, afield that has emerged as data and knowledge concerning these systems increases and biologists call for tools to guide wet-lab experimentation. A promising approach to exploit parallelism in this domain is the integration of spatial aspects, which are often crucial to a models validity. We describe an optimistic, parallel and distributed variant of the Next-Subvolume Method (NSM), a method that augments the well-known Gillespie Stochastic Simulation Algorithm (SSA) with spatial features. We discuss requirements imposed by this application on a parallel discrete event simulation engine to achieve efficient execution. First results of combining NSM and the grid-inspired simulation system AURORA are shown.

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.

Multi-resolution spatial simulation for molecular crowding

Spatial phenomena attract increasingly interest in computational biology. Molecular crowding, i.e. a dense population of macromolecules, is known to have a significant impact on the kinetics of molecules. However, an in-detail inspection of cell behavior in time and space is extremely costly. To balance between cost and accuracy, multi-resolution approaches offer one solution. Particularly, a combination of individual and lattice-population based algorithms promise an adequate treatment of phenomena like macromolecular crowding. In realizing such an approach, central questions are how to specify and synchronize the interaction between population and individual spatial level, and to decide what is best treated at a specific level, respectively. Based on an algorithm which combines the next subvolume method and a simple, individual-based spatial approach, we will present possible answers to these questions, and will discuss first experimental results.

A parallel and distributed discrete event approach for spatial cell-biological simulations

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 of performance. One solution are parallel, distributed approaches, however, their application is limited by the amount of parallelism available in the model. Recent studies have shown that spatial aspects are crucial for cell biological dynamics and they are also a promising candidate to exploit parallelism. Promises and specific requirements imposed by a spatial simulation of cell biological systems will be illuminated by a parallel and distributed variant of the Next-Subvolume Method (NSM), which augments the Stochastic Simulation Algorithm (SSA) with spatial features, and its realization in a grid-inspired simulation system called Aurora.

One Modelling Formalism & Simulator Is Not Enough! A Perspective for Computational Biology Based on James II

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-distributed) of one and the same model. The latter is often done to increase efficiency, sometimes at the cost of accuracy and level of detail. James II has been developed to support different modelling formalisms and different simulators and their combinations. It is based on a plug-in concept which enables developers to integrate spatial and non-spatial modelling formalisms (e.g. stochastic i¾? calculus , Beta binders , Devs , space- i¾?), simulation algorithms (e.g. variants of Gillespies algorithms (including Tau Leaping and Next Subvolume Method ), space- i¾?simulator, parallel Beta binders simulator) and supporting technologies (e.g. partitioning algorithms, data collection mechanisms, data structures, random number generators) into an existing framework. This eases method development and result evaluation in applied modelling and simulation as well as in modelling and simulation research.

VioNeS - Visual Support for the Analysis of the Next Sub-volume Method

Computational simulation is an established method to gain insight into cellular processes. As the resulting data sets are usually large and complex, visualization can play a significant role in data analysis. In this paper, we focus on the visualization of simulation output from the Next Sub-volume Method, a spatial simulation algorithm. In addition to the spatial context of the simulation output, its heterogeneous data types, multiple variables, and the temporal context make high demands on the visualization. To cope with these challenging characteristics, we systematically explore possible visualization concepts with respect to these characteristics. From these findings, we derive our specific solution to visualize the data from the Next Sub-volume Method, using a framework of multiple coordinated views that emphasize the spatial context of the data. Combining these views with a highly interactive user interface, the user is able to adapt the visualization to his current analysis goals and explore the data in its complexity.