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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.
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.
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.