TY - CHAP A1 - Blätke, Mary-Ann A1 - Rohr, Christian A1 - Heiner, Monika A1 - Marwan, Wolfgang ED - Benner, Peter T1 - A Petri Net based Framework for Biomodel Engineering T2 - Large-Scale Networks in Engineering and Life Sciences KW - Automatic network reconstruction KW - Biomodel engineering KW - Dynamic systems modelling KW - Modular modelling KW - Petri nets KW - Molecular regulatory networks KW - Reverse engineering Y1 - 2014 SN - 978-3-319-08436-7 U6 - https://doi.org/10.1007/978-3-319-08437-4_6 SP - 317 EP - 366 PB - Springer CY - Heidelberg ER - TY - GEN A1 - Liu, Fei A1 - Assaf, George A1 - Chen, Ming A1 - Heiner, Monika T1 - A Petri nets-based framework for whole-cell modeling T2 - Biosystems N2 - Whole-cell modeling aims to incorporate all main genes and processes, and their interactions of a cell in one model. Whole-cell modeling has been regarded as the central aim of systems biology but also as a grand challenge, which plays essential roles in current and future systems biology. In this paper, we analyze whole-cell modeling challenges and requirements and classify them into three aspects (or dimensions): heterogeneous biochemical networks, uncertainties in components, and representation of cell structure. We then explore how to use different Petri net classes to address different aspects of whole-cell modeling requirements. Based on these analyses, we present a Petri nets-based framework for whole-cell modeling, which not only addresses many whole-cell modeling requirements, but also offers a graphical, modular, and hierarchical modeling tool. We think this framework can offer a feasible modeling approach for whole-cell model construction. KW - Whole-cell modeling KW - Systems biology KW - Petri nets KW - Modeling framework Y1 - 2021 UR - https://www.sciencedirect.com/science/article/abs/pii/S0303264721001738?via%3Dihub U6 - https://doi.org/10.1016/j.biosystems.2021.104533 SN - 0303-2647 SN - 1872-8324 VL - 210 ER - TY - THES A1 - Assaf, George T1 - Fuzzy coloured Petri nets for modelling biological systems with uncertain kinetic parameters N2 - 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. KW - modelling KW - Colouring fuzziness KW - fuzzy logic KW - Petri nets KW - Uncertain biological systems KW - systems biology KW - Software harmonisation KW - coloured Petri nets Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-58516 PB - BTU CY - Cottbus ER - TY - CHAP A1 - Liu, Fei A1 - Heiner, Monika ED - Chen, Ming ED - Hofestädt, Ralf T1 - Petri Nets for Modeling and Analyzing Biochemical Reaction Networks T2 - Approaches in Integrative Bioinformatics KW - Petri nets KW - Biochemical reaction networks KW - Unifying Petri net frame- work KW - Qualitative KW - Stochastic KW - Conti nuous and hybrid Petri nets KW - Colored Petri nets KW - Repressilator Y1 - 2014 SN - 978-3-642-41280-6 SN - 978-3-642-41281-3 U6 - https://doi.org/10.1007/978-3-642-41281-3_9 SP - 245 EP - 272 PB - Springer CY - Heidelberg ER - TY - THES A1 - Chodak, Jacek T1 - Spike – a tool for reproducible simulation experiments N2 - 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. KW - Parallel simulation KW - Parameter scanning KW - Petri nets KW - Reproducibility KW - Simulation of adaptive models Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-58509 PB - BTU CY - Cottbus ER -