FG Datenstrukturen und Softwarezuverlässigkeit
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Institute
BTU
Over the last twenty years, Petri nets have been increasingly adopted for modelling and simulating biological systems, as they offer an intuitive and graphical approach for this purpose. Their usability convenience comes from the fact that they offer many types of elements to describe systems in a qualitative and quantitative way. Coloured Petri nets are particularly useful to model systems with repeated components in a compact fashion. Our tool Snoopy for modelling and simulating Petri nets is one of the most well-known tools supporting a family of related Petri net classes comprising stochastic, continuous and hybrid Petri nets, and covering uncoloured and coloured Petri nets alike. However, kinetic information of a biological system, i.e. kinetic parameters may be uncertain, due to many reasons, e.g. environmental factors. Besides, coloured Petri nets as they were previously supported in Snoopy suffered from some inconsistencies. Due to these inconsistencies, exploring the model behaviour using different sizes (scaleability) was not feasible. Both challenges call for a new and more powerful approach integrating the modelling of uncertainties together with modelling features supporting repeated structures in a compact and scalable way.
This thesis comprises two major contributions: Firstly, we introduce the definition and present the simulation algorithm for both uncoloured and coloured fuzzy Petri nets, by extending the existing quantitative uncoloured and coloured Petri nets in Snoopy. This includes discretising the uncertain kinetic parameters to crisp values by using sampling strategies. Secondly, we harmonise coloured Petri nets in Snoopy with their uncoloured counterparts and we extend the Snoopy’s coloured Petri nets by all the features, which are supported by the coloured abstract net description language - an exchange format of coloured Petri nets in our PetriNuts tool family.
By performing fuzzy simulation, one can obtain two kinds of output: fuzzy bands of each output variable and their corresponding timed-membership functions. Each fuzzy band describes the uncertainties associated with the input, whereas membership functions give more accurate information about the associated uncertainties. The most important features that we obtain by harmonising coloured Petri nets are to develop scaleable models, by defining scaling factors as constants and unifying the usage of coloured Petri nets with the other tools in our PetriNuts tool family.
Fuzzy Petri nets
(2021)
This document explains the procedure of modelling and simulating FPN
and FPNC in Snoopy; please compare Figure 1 . Please note that the same
steps for one net class can be equally applied to the other classes, just differentiate between uncoloured Petri nets (PN) and coloured Petri nets (PNC).
Furthermore, we give more details about Latin Hybercube Sampling strategies supported by Snoopy's FPN.
Snoopy is a powerful modelling and simulation tool for various types of Petri nets, which have been applied to a wide range of biochemical reaction networks. We present an enhanced version of Snoopy, now supporting coloured and uncoloured stochastic, continuous and hybrid Petri Nets with fuzzy kinetic parameters. Colour helps to cope with modelling challenges imposed by larger and more complex networks. Fuzzy parameters are specifically useful when kinetic parameter values can not be precisely measured or estimated. By running fuzzy simulation we obtain output bands of the variables of interest induced by the effect of the fuzzy kinetic parameters. Simulation is always done on the uncoloured level. For this purpose, coloured fuzzy Petri nets are automatically unfolded to their corresponding uncoloured counterparts. Combining the power of fuzzy kinetic parameters with the modelling convenience of coloured Petri nets provides a new quality in user support with sophisticated modelling and analysis features.
Membrane systems are a very powerful computational modelling formalism inspired by the internal organisation of living cells. Modelling of membrane systems is challenged by composing many structurally similar components, which may result in very large models. Furthermore, some components may suffer from a lack of precise kinetic parameters. Coloured fuzzy Petri nets combine coloured Petri nets with fuzzy kinetic parameters, and thus offer an approach to address these challenges. In this paper, we use coloured fuzzy Petri nets to model and simulate membrane systems which are enriched by fuzzy kinetic parameters. We also introduce a methodology and workflow utilising coloured fuzzy Petri nets for modelling and simulating general biological systems which have to cope with incomplete knowledge of their kinetic data.
Modeling biological gradient formation: combining partial differential equations and Petri nets
(2016)
BioModelKit: Spatial Modelling of Complex Multiscale Molecular Biosystems based on Modular Models
(2018)
Reproducibility of simulation experiments is still a significant challenge and has attracted considerable attention in recent years. One cause of this situation is bad habits of the scientific community. Many results are published without data or source code, and only a textual description of the simulation set-up is provided. Other causes are: no complete simulation set-up, no proper output data analysis and inconsistency of published data, which makes it impossible to compare results.
The progress of computational modelling, amount of data and complexity of models requires designing experiments in such a way that ensures reproducibility. A textual description does not provide all the needed details. A computer code is more reliable than a textual description. It is the precise specification that describes a simulation configuration, model, etc. When computer code, data, models and all parameters are provided, the simulation results become reproducible.
The main goal of this thesis is to develop a tool that ensures reproducibility and efficient execution of simulation experiments, often involving many individual simulation runs. The tool should support a wide range of application scenarios, where the typical scenario is simulation of biochemical reaction networks, which are represented as (coloured) Petri nets interpreted in the stochastic, continuous or hybrid paradigm. The model to be simulated can be given in various formats, including SBML.
The result is a command line tool called Spike, which can be used for various scenarios, including benchmarking, simulation of adaptive models and parameter optimization. It builds on a human-readable configuration script SPC, supporting the efficient specification of multiple model configurations as well as multiple simulator configurations in a single configuration file.
Spike - a command line tool for continuous, stochastic & hybrid simulation of (coloured) Petri nets
(2018)
We present a methodology for systematically extending epidemic models to multilevel and multiscale spatio-temporal pandemic ones. Our approach builds on the use of coloured stochastic and continuous Petri nets facilitating the sound component-based extension of basic SIR models to include population stratification and also spatio-geographic information and travel connections, represented as graphs, resulting in robust stratified pandemic metapopulation models. The epidemic components and the spatial and stratification data are combined together in these coloured models and built in to the underlying expanded models. As a consequence this method is inherently easy to use, producing scalable and reusable models with a high degree of clarity and accessibility which can be read either in a deterministic or stochastic paradigm. Our method is supported by a publicly available platform PetriNuts; it enables the visual construction and editing of models; deterministic, stochastic and hybrid simulation as well as structural and behavioural analysis. All models are available as Supplementary Material, ensuring reproducibility. All uncoloured Petri nets can be animated within a web browser at https://www-dssz.informatik.tu-cottbus.de/DSSZ/Research/ModellingEpidemics, assisting the comprehension of those models. We aim to enable modellers and planners to construct clear and robust models by themselves.
We present a methodology for systematically extending epidemic models to multilevel and multiscale spatio-temporal pandemic ones. Our approach builds on the use of coloured stochastic and continuous Petri nets facilitating the sound component-based extension of basic SIR models to include population stratification and also spatio-geographic information and travel connections, represented as graphs, resulting in robust stratified pandemic metapopulation models. This method is inherently easy to use, producing scalable and reusable models with a high degree of clarity and accessibility which can be read either in a deterministic or stochastic paradigm. Our method is supported by a publicly available platform PetriNuts; it enables the visual construction and editing of models; deterministic, stochastic and hybrid simulation as well as structural and behavioural analysis. All the models are available as supplementary material, ensuring reproducibility.
Spatial quorum sensing modelling using coloured hybrid Petri nets and simulative model checking
(2019)
A Case Study in Design and Verification of Manufacturing Systems with Hierarchical Petri Nets
(1999)
A Case Study in Developing Control Software of Manufacturing Systems with Hierarchical Petri Nets
(1996)
Abstract. There is an increasing demand to certify the security of systems according to the Common Criteria (CC). The CC distinguish several evaluation assurance levels (EALs), level EAL7 being the highest and requiring the application of formal techniques. We present a method for requirements engineering an (semi-formal and formal) modeling of systems to be certified according to the higher evaluation assurance levels of the CC. The method is problem oriented, i.e. it is driven by the environment in which the system will operate and by a mission statement. We illustrate our approach by an industrial case study, namely an electronic purse card (EPC) to be implemented on a Java Smart Card. As a novelty, we treat the mutual asymmetric authentication of the card an the terminal into which the card is inserted.
Petri Nets
(2008)
On Integration of Qualitative and Quantitative Analysis of Manufacturing Systems Using Petri Nets
(1997)
Modeling and Simulation of Multi-scale Environmental Systems with Generalized Hybrid Petri Nets
(2015)
Coloured Hybrid Petri Nets: an Adaptable Modelling Approach for Multi-scale Biological Networks
(2018)
A Graphical Approach for Hybrid Simulation of 3D Diffusion Bio-Models via Coloured Hybrid Petri Nets
(2020)
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 () 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, models are also scalable. This paper shows how 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 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.