Mostafa Herajy

Snoopy --- a unifying petri net tool

The tool Snoopy provides a unifying Petri net framework which has particularly many application scenarios in systems and synthetic biology. The framework consists of two levels: uncoloured and coloured. Each level comprises a family of related Petri net classes, sharing structure, but being specialized by their kinetic information. Petri nets of all net classes within one level can be converted into each other, while changing the level involves user-guided folding or automatic unfolding. Models can be hierarchically structured, allowing for the mastering of larger networks. Snoopy supports the simultaneous use of several Petri net classes; the graphical user interface adapts dynamically to the active one. Built-in animation and simulation (depending on the net class) are complemented by export to various analysis tools. Snoopy facilitates the extension by new Petri net classes thanks to its generic design.

A Steering Server for Collaborative Simulation of Quantitative Petri Nets

In this paper we present a Petri net simulation tool called Snoopy Steering and Simulation Server, S 4 for short, which works as a stand-alone extension of Snoopy. The server permits users to share and interactively steer quantitative Petri net models during a running simulation. Moreover, users can collaborate by controlling the execution of a model remotely from different machines (clients). S 4 is shipped with an Application Programming Interface (API) which enables user-defined extensions of the core functionalities. Stochastic, continuous and hybrid Petri nets are supported, both as low-level and coloured ones. S 4 is platform-independent and distributed free of charge for academic use.

Hybrid Petri Nets for Modelling the Eukaryotic Cell Cycle

System level understanding of the repetitive cycle of cell growth and division is crucial for disclosing many unknown principles of biological organisms. The deterministic or stochastic approach – when deployed separately – are not sufficient to study such cell regulation due to the complexity of the reaction network and the existence of reactions at different time scales. Thus, an integration of both approaches is advisable to study such biochemical networks. In this paper we show how Generalised Hybrid Petri Nets can be used to intuitively represent and simulate the eukaryotic cell cycle. Our model captures intrinsic as well as extrinsic noise and deploys stochastic as well as deterministic reactions. Additionally, marking-dependent arc weights are biologically motivated and introduced to Snoopy – a tool for animating and simulating Petri nets in various paradigms.

Petri Net-Based Collaborative Simulation and Steering of Biochemical Reaction Networks

Computational steering is an interactive remote control of a long running application. The user can adopt it, e.g., to adjust simulation parameters on the fly. Simulation of large-scale biochemical networks is often computationally expensive, particularly stochastic and hybrid simulation. Such extremely time-consuming computations necessitate an interactive mechanism to permit users to try different paths and ask “what-if-questions” while the simulation is in progress. Furthermore, with the progress of computational modelling and the simulation of biochemical networks, there is a need to manage multi-scale models, which may contain species or reactions at different scales. In this context, Petri nets are of special importance, since they provide an intuitive visual representation of reaction networks. In this paper, we introduce a framework and its implementation for combining Petri nets and computational steering for the representation and interactive simulation of biochemical networks. The main merits of the developed framework are: intuitive representation of biochemical networks by means of Petri nets, distributed collaborative and interactive simulation, and tight coupling of simulation and visualisation.

Snoopy’s hybrid simulator: a tool to construct and simulate hybrid biological models

BackgroundHybrid simulation of (computational) biochemical reaction networks, which combines stochastic and deterministic dynamics, is an important direction to tackle future challenges due to complex and multi-scale models. Inherently hybrid computational models of biochemical networks entail two time scales: fast and slow. Therefore, it is intricate to efficiently and accurately analyse them using only either deterministic or stochastic simulation. However, there are only a few software tools that support such an approach. These tools are often limited with respect to the number as well as the functionalities of the provided hybrid simulation algorithms.ResultsWe present Snoopy’s hybrid simulator, an efficient hybrid simulation software which builds on Snoopy, a tool to construct and simulate Petri nets. Snoopy’s hybrid simulator provides a wide range of state-of-the-art hybrid simulation algorithms. Using this tool, a computational model of biochemical networks can be constructed using a (coloured) hybrid Petri net’s graphical notations, or imported from other compatible formats (e.g. SBML), and afterwards executed via dynamic or static hybrid simulation.ConclusionSnoopy’s hybrid simulator is a platform-independent tool providing an accurate and efficient simulation of hybrid (biological) models. It can be downloaded free of charge as part of Snoopy from http://www-dssz.informatik.tu-cottbus.de/DSSZ/Software/Snoopy.

Accelerated Simulation of Hybrid Biological Models with Quasi-Disjoint Deterministic and Stochastic Subnets

Computational biological models are indispensable tools for in silico hypothesis testing. But with the increasing complexity of biological systems, traditional simulators become inefficient to tackle emerging computational challenges. Hybrid simulation, which combines deterministic and stochastic parts, is a promising direction to deal with such challenges. However, currently existing algorithms of hybrid simulation are impractical for implementing real and complex biological systems. One reason for such limitation is that the performance of hybrid simulation not only relies on the number of stochastic events, but also on the type as well as the efficiency of the deterministic solver. In this paper, a new method is proposed for improving the performance of hybrid simulators by reducing the frequent reinitialisation of the deterministic solver. The proposed approach works well with models that contain a substantial number of stochastic events and higher numbers of continuous variables with limited connections between the deterministic and stochastic regimes. We tested these improvements on a number of case studies and it turns out that, for certain examples, the amended algorithm is ten times faster than the exact method.

Modeling and simulation of multi-scale environmental systems with Generalized Hybrid Petri Nets

Predicting and studying the dynamics and properties of environmental systems necessitates the construction and simulation of mathematical models entailing different levels of complexities. Such type of computational experiments often require the combination of discrete and continuous variables as well as processes operating at different time scales. Furthermore, the iterative steps of constructing and analyzing environmental models might involve researchers with different background. Hybrid Petri nets may contribute in overcoming such challenges as they facilitate the implementation of systems integrating discrete and continuous dynamics. Additionally, the visual depiction of model components will inevitably help to bridge the gap between scientists with distinct expertise working on the same problem. Thus, modeling environmental systems with hybrid Petri nets enables the construction of complex processes while keeping the models comprehensible for researchers working on the same project with significantly divergent education path. In this paper we propose the utilization of a special class of hybrid Petri nets, Generalized Hybrid Petri Nets (GHPN), to model and simulate environmental systems exposing processes interacting at different time-scales. GHPN integrate stochastic and deterministic semantics as well as other types of special basic events. Moreover, a case study is presented to illustrate the use of GHPN in constructing and simulating multi-timescale environmental scenarios.

Coloured Hybrid Petri Nets: An adaptable modelling approach for multi-scale biological networks

Coloured Petri nets are an excellent choice for exploring large biological models, particularly when there are repetitions of components. Such models can be easily adapted by slight modifications of parameter values related to colours. Similarly, multi-scale models could involve multiple spatial scales in addition to multiple time scales. Thus, they require the full interplay between stochastic as well as deterministic processes. In this paper we take these two aspects into account and present a modelling and simulation approach for multi-scale biochemical networks using Coloured Generalised Hybrid Petri Nets (GHPNC). GHPNC are a Petri net class that associates colours to Generalised Hybrid Petri Nets (GHPN), which incorporate discrete and continuous places in addition to stochastic and continuous transitions. Moreover, we present two case studies to illustrate typical applications taking advantage of such a Petri net class.

Semantics-based parallelization for the stochastic simulation of complex cell cycle regulations

Stochastic simulation of biological systems becomes widely used, since it can intuitively account for the fluctuation of species with a few number of molecules. However, for bigger models and/or models with mixed abundance of molecules, stochastic simulation fails to produce the required results in reasonable time. Parallel simulation can offer a solution for this challenge. Nevertheless, currently available parallel software tools either provide a coarse-grained parallelization or a general-purpose fine-grained parallel simulation of the well-known stochastic simulation algorithm (SSA). The former can only take advantage of parallel processing if multiple runs have to be performed, while the latter requires extensive synchronization and communication between the different processing nodes each time a reaction is to fire. In this paper, a fine-grained parallelization approach is presented that takes advantage of the underlying model semantics to improve the simulator performance. The proposed method is applied to the yeast cell cycle regulation, which is an example of biological models that requires extensive investigation.