Roland Ewald

A Spatial Extension to the π Calculus

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 @p calculus by time and space. @p processes are embedded into a vector space and move individually. Only processes that are sufficiently close can communicate. The operational semantics of SpacePi defines the interplay between movement, communication, and time-triggered events. A model describing the phototaxis of the Euglena micro-organism is presented as a practical example. The formalisms use and generality is discussed with respect to the modeling of molecular biological processes like diffusion, active transportation in cell signaling, and spatial structures.

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.

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.

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.

SESSL: A domain-specific language for simulation experiments

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 offers various features (e.g., for experiment design, performance analysis, result reporting, and simulation-based optimization). It supports “cutting-edge” experiments by allowing to add custom code, enables a reuse of functionality across simulation systems, and improves the reproducibility of simulation experiments.

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.

Discrete event modelling and simulation in systems biology

With Systems Biology, a promising new application area for modelling and simulation emerges. Todays 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 hypotheses, constructing models, experimenting with or analysing these models, and validating the findings by wet-lab experiments. A crucial point is therefore the way in which the knowledge about a system is formalized, that is, how a biological system is described, as this constrains the perception of the system as well as the scope of possible answers the model might provide. In this article, we compare different discrete event modelling formalisms (PETRI NETS, Stochastic π-CALCULUS, STATECHARTS, and DEVS) regarding their applicability to a cell biological system of current research interest, the Wnt signalling pathway. We then introduce the popular Gillespie algorithm, which is the foundation of many discrete event simulators for molecular-biological systems, and elaborate on some interesting extensions.

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.