Snoopy computational steering framework - user manual version 1.0

In this manual we discuss the use of Snoopy’s computational steering framework to simulate and interactively steer (stochastic, continuous, hybrid) Petri nets, e.g., biochemical network models. In a typical application scenario, a user constructs a model using a Petri net editing tool (e.g., Snoopy). Afterwards, the Petri net model is submitted to one of the running servers to quantitatively simulate it. Later, other users can adapt their steering GUIs to connect to this model. One of the connected users initialises the simulation while others could stop, pause, or restart it. When the simulator initially starts, it uses the current model settings to run the simulation. Later, other users can remotely join the running simulation and change on the fly parameters and the current marking

Computational Steering von biochemischen Netzwerken mit unterschiedlicher Skalierung

Computational Steering ist eine interaktive Fernsteuerung von Applikationen mit langer Laufzeit. Der Nutzer kann sie einsetzen, um Paramater "on-the-fly" einzustellen. Die stochastische und hybride Simulation von biochemischem Netzwerk ist sehr rechenintensiv. Derart aufwendige Berechnungen erfordern interaktive Techniken, die es Nutzern ermöglichen, unterschiedliche Ausführungen während der Berechung zu testen. Durch die rasant fortschreitende Entwicklung der rechnergestützten Modellierung und Simulation biochemischer Netzwerke besteht zunehmender Bedarf, Modelle, in denen Substanzen und Reaktionen unterschiedlicher Skalierung (multi-scale models) auftreten, zu verwalten. Dabei sind Petrinetze von besonderer Bedeutung, da sie eine sehr intuitive visuelle Darstellung von Reaktionsnetzwerken erlauben. Die vorliegende Arbeit liefert folgenden Beitrag: Zunächst werden verallgemeinerte hybride Petrinetze (GHPNbio) und deren Simulation vorgestellt, um sogenannte "steife" (engl. stiff) biochemische Netzwerke zu modellieren und zu simulieren. Schnelle Reaktionen werden dabei kontinuierlich behandelt, langsame Reaktionen dagegen werden stochastisch behandelt. Durch die Kombination der Eigenschaft von kontinuierlichen Petrinetzen (CPN) und erweiterten stochastischen Petrinetzen (XSPN) bieten GHPNbio ein hohes Maß an Ausdruckstärke hinsichtlich Modellierung und Simulation. Die Zuordnung der Transitionen zu kontinuierlichen oder stochastischen (Paritionierung) kann dabei sowohl statisch als auch dynamisch während der Simulation vorgenommen werden. Darüber hinaus wird ein neues Framework vorgestellt, das Petrinetze und Computational Steering zum Zweck der Darstellung und interaktiven Simulation biochemischer Netzwerke zusammenfährt. Die wesentlichen Besonderheiten sind: die enge Kopplung zwischen Simulation und Visualisierung, die verteilte; kooperative; und die interaktive Simulation und die intuitive Repräsentation biochemischer Netze. Zusammen stellen verallgemeinerte hybride Petrinetze und Computational Steering für Systembiologen ein nützliches Werkzeug dar, das helfen kann, komplexe Zusammenhänge auf Systemebene zu verstehen. GHPNbio können dazu verwendet werden, die Simulation biochemischer Netze ohne Genauigkeitsverlust zu beschleunigen. Computational Steering erlaubt es Benutzern mit unterschiedlichem fachlichem Hintergrund biochemische Modelle gemeinsam zu bearbeiten und zu simulieren. Das vorgeschlagene Framework wurde in unserem Modellierungswerkzeug Snoopy implementiert.Computational steering is an interactive remote control of a long running application. The user can adopt it to adjust the simulation parameters on the fly. Correspondingly, simulation of large scale biochemical networks is computationally expensive, particularly stochastic and hybrid simulation. Such extremely intensive computations necessitate an interactive mechanism to permit users to try different paths and ask simultaneously "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 (called also stiff systems). In this context, Petri nets are of considerable importance in the modelling and analysis of biochemical networks, since they provide an intuitive visual representation of reaction networks. The contributions of this thesis are twofold: firstly, we introduce the definition and present simulation algorithms of Generalised Hybrid Petri Nets (GHPNbio) to represent and simulate stiff biochemical networks where fast reactions are represented and simulated continuously, while slow reactions are carried out stochastically. GHPNbio provide rich modelling and simulation functionalities by combining all features of Continuous Petri Nets (CPN) and Extended Stochastic Petri Nets (XSPN), including three types of deterministic transitions. Moreover, the partitioning of the reaction networks can either be done off-line before the simulation starts or on-line while the simulation is in progress. Secondly, we introduce a novel framework which combines Petri nets and computational steering for the representation and interactive simulation of biochemical networks. The main merits of the framework proposed in this thesis are: the tight coupling of simulation and visualisation, distributed; collaborative; and interactive simulation, and intuitive representation of biochemical networks by means of Petri nets. Generalised hybrid Petri nets and computational steering will together provide an invaluable tool for systems biologists to help them to obtain a deeper system level understanding. GHPNbio speed up the simulation and simultaneously preserve accuracy, while computational steering enables users of different background to share, collaborate and interactively simulate biochemical models. Finally, the implementation of the proposed framework is given as part of Snoopy - a tool to design and animate/simulate hierarchical graphs, among them qualitative, stochastic, continuous and hybrid Petri nets

(Coloured) Hybrid Petri nets in Snoopy – user manual

Hybrid simulation of biological processes becomes widely used to overcome the limitations of the pure stochastic or the complete deterministic simulation. In this manual, we present easy-to-follow steps for constructing and executing hybrid models via Snoopy [HHL+12]. Snoopy is a tool to design and animate or simulate hierarchical graphs, i.e., qualitative, stochastic, continuous, and hybrid Petri nets. This manual is concerned with hybrid Petri nets (HPN) [HH12] as well as their coloured counterpart (HPNC) [HLR14]. HPN combine the merits of stochastic and continuous Petri nets into one single class. Moreover, HPN in Snoopy supports state of the art hybrid simulation algorithms (e.g., [HH16]) to execute the constructed HPN models. Simulating a model using Snoopy's hybrid simulation involves first constructing the reaction network via HPN notations and afterwards executing such model

A Graphical Approach for Hybrid Simulation of 3D Diffusion Bio-Models via Coloured Hybrid Petri Nets

Three-dimensional modelling of biological systems is imperative to study the behaviour of dynamic systems that require the analysis of how their components interact in space. However, there are only a few formal tools that offer a convenient modelling of such systems. The traditional approach to construct and simulate 3D models is to build a system of partial differential equations (PDEs). Although this approach may be computationally efficient and has been employed by many researchers over the years, it is not always intuitive since it does not provide a visual depiction of the modelled systems. Indeed, a visual modelling can help to conceive a mental image which eventually contributes to the understanding of the problem under study. Coloured Hybrid Petri Nets (HPNC) are a high-level representation of classical Petri nets that offer hybrid as well as spatial modelling of biological systems. In addition to their graphical representations, HPNC models are also scalable. This paper shows how HPNC can be used to construct and simulate systems that require three-dimensional as well as hybrid (stochastic/continuous) modelling. We use calcium diffusion in three dimensions to illustrate our main ideas. More specifically, we show that creating 3D models using HPNC can yield more flexible models as the structure can be easily scaled up and down by just modifying a few parameters. This advantage of convenient model configuration facilitates the design of different experiments without the need to alter the model structure

Estimation of saturation activities for activation experiments in CHARM and CSBF using Fluence Conversion Coefficients

As summer student at CERN, I have been working in the Radiation Protection group for 10 weeks. I worked with the \textsc{Fluka} Monte Carlo simulation code, using Fluence Conversion Coefficients method to perform simulations to estimate the saturation activities for activation experiments in the \textsc{CSBF} and the \textsc{Charm} facility in the East Experimental Area. The provided results will be used to plan a Monte Carlo benchmark in the \textsc{CSBF} during a beam period at the end of August 2017

Additional file 2 of Snoopy’s hybrid simulator: a tool to construct and simulate hybrid biological models

An example ℋ P N C ${\mathcal {HPN^{C}}}$ model. A Snoopy file implementing the calcium spatial dynamics using ℋ P N C ${\mathcal {HPN^{C}}}$ notations. (COLHPN 114 kb

Additional file 4 of Snoopy’s hybrid simulator: a tool to construct and simulate hybrid biological models

Description of the ATM/p53/NF-kB HPN model. A short description of how to open and simulate the Snoopy file of the ATM/p53/NF- ÎşB HPN model. (PDF 1248 kb