TY - GEN A1 - Liu, Fei A1 - Chen, Siyuan A1 - Heiner, Monika A1 - Song, Hengjie T1 - Modeling Biological Systems with Uncertain Kinetic Data Using Fuzzy Continuous Petri Nets T2 - BMC Systems Biology Y1 - 2018 U6 - https://doi.org/10.1186/s12918-018-0568-8 SN - 1752-0509 VL - 12 IS - Supplement 4 SP - 64 EP - 74 ER - TY - GEN A1 - Herajy, Mostafa A1 - Liu, Fei A1 - Rohr, Christian A1 - Heiner, Monika T1 - Coloured Hybrid Petri Nets: an Adaptable Modelling Approach for Multi-scale Biological Networks T2 - Computational Biology and Chemistry KW - Coloured Petri nets KW - Hybrid Petri Nets KW - Multi-scale modelling KW - Bistable switch KW - Dendritic spines Y1 - 2018 U6 - https://doi.org/10.1016/j.compbiolchem.2018.05.023 SN - 1476-9271 VL - 76 SP - 87 EP - 100 ER - TY - GEN A1 - Liu, Fei A1 - Heiner, Monika A1 - Gilbert, David T1 - Fuzzy Petri nets for modelling of uncertain biological systems T2 - Briefings in Bioinformatics Y1 - 2018 U6 - https://doi.org/10.1093/bib/bby118 SN - 1477-4054 SN - 1467-5463 VL - 2018 ER - TY - GEN A1 - Assaf, George A1 - Heiner, Monika A1 - Liu, Fei ED - Bortolussi, Luca ED - Sanguinetti, Guido T1 - Biochemical reaction networks with fuzzy kinetic parameters in Snoopy T2 - Computational Methods in Systems Biology : 17th International Conference, CMSB 2019, Trieste, Italy, September 18–20, 2019, Proceedings Y1 - 2019 SN - 978-3-030-31303-6 SN - 978-3-030-31304-3 U6 - https://doi.org/http://dx.doi.org/10.1007/978-3-030-31304-3_17 SP - 302 EP - 307 PB - Springer ER - TY - GEN A1 - Liu, Fei A1 - Sun, Wujie A1 - Heiner, Monika A1 - Gilbert, David T1 - Hybrid modelling of biological systems using fuzzy continuous Petri nets T2 - Briefings in Bioinformatics N2 - Integrated modelling of biological systems is challenged by composing components with sufficient kinetic data and components with insufficient kinetic data or components built only using experts’ experience and knowledge. Fuzzy continuous Petri nets (FCPNs) combine continuous Petri nets with fuzzy inference systems, and thus offer an hybrid uncertain/certain approach to integrated modelling of such biological systems with uncertainties. In this paper, we give a formal definition and a corresponding simulation algorithm of FCPNs, and briefly introduce the FCPN tool that we have developed for implementing FCPNs. We then present a methodology and workflow utilizing FCPNs to achieve hybrid (uncertain/certain) modelling of biological systems illustrated with a case study of the Mercaptopurine metabolic pathway. We hope this research will promote the wider application of FCPNs and address the uncertain/certain integrated modelling challenge in the systems biology area. KW - systems biology KW - integrated modelling KW - fuzzy continuous Petri nets KW - uncertainties KW - hybrid simulation Y1 - 2019 U6 - https://doi.org/10.1093/bib/bbz114 VL - 22(2021) SP - 438 EP - 450 ER - TY - GEN A1 - Schwarick, Martin A1 - Rohr, Christian A1 - Liu, Fei A1 - Assaf, George A1 - Chodak, Jacek A1 - Heiner, Monika T1 - Efficient Unfolding of Coloured Petri Nets using Interval Decision Diagrams T2 - Application and Theory of Petri Nets and Concurrency : 41st International Conference, PETRI NETS 2020, Paris, France, June 24–25, 2020, Proceedings N2 - We consider coloured Petri nets, qualitative and quantitative ones alike, as supported by our PetriNuts tool family, comprising, among others, Snoopy, Marcie and Spike. Currently, most analysis and simulation techniques require to unfold the given coloured Petri net into its corresponding plain, uncoloured Petri net representation. This unfolding step is rather straightforward for finite discrete colour sets, but tends to be time-consuming due to the potentially huge number of possible transition bindings. We present an unfolding approach building on a special type of symbolic data structures, called Interval Decision Diagram, and compare its runtime performance with an unfolding engine employing an off-the-shelf library to solve constraint satisfaction problems. For this comparison we use the 22 scalable coloured models from the MCC benchmark suite, complemented by a few from our own collection. KW - Coloured Petri KW - Unfolding KW - Symbolic data structures KW - Interval decision diagrams Y1 - 2020 SN - 978-3-030-51830-1 SN - 978-3-030-51831-8 U6 - https://doi.org/10.1007/978-3-030-51831-8_16 SP - 324 EP - 344 PB - Springer CY - Cham ER - TY - RPRT A1 - Assaf, George A1 - Heiner, Monika A1 - Liu, Fei T1 - Fuzzy Petri nets N2 - 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. KW - Fuzzy Petri Nets coloured fuzzy Petri nets Snoopy Y1 - 2021 UR - https://www-dssz.informatik.tu-cottbus.de/publications/btu-reports/fpn_manual.pdf ER - TY - GEN A1 - Liu, Fei A1 - Heiner, Monika A1 - Gilbert, David T1 - Hybrid modelling of biological systems: current progress and future prospects T2 - Briefings in Bioinformatics N2 - Integrated modelling of biological systems is becoming a necessity for constructing models containing the major biochemical processes of such systems in order to obtain a holistic understanding of their dynamics and to elucidate emergent behaviours. Hybrid modelling methods are crucial to achieve integrated modelling of biological systems. This paper reviews currently popular hybrid modelling methods, developed for systems biology, mainly revealing why they are proposed, how they are formed from single modelling formalisms and how to simulate them. By doing this, we identify future research requirements regarding hybrid approaches for further promoting integrated modelling of biological systems. KW - biological modelling KW - hybrid modelling KW - modelling formalisms Y1 - 2022 UR - https://academic.oup.com/bib/article/23/3/bbac081/6555400 U6 - https://doi.org/10.1093/bib/bbac081 SN - 1477-4054 SN - 1467-5463 VL - 23 IS - 3 SP - 1 EP - 15 ER - TY - GEN A1 - Assaf, George A1 - Heiner, Monika A1 - Liu, Fei T1 - Coloured fuzzy Petri nets for modelling and analysing membrane systems T2 - Biosystems N2 - 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. KW - Fuzzy membrane systems KW - Parameter uncertainties KW - Coloured fuzzy stochastic Petri nets KW - Coloured fuzzy continuous Petri net KW - Coloured fuzzy hybrid Petri net KW - Modelling KW - Fuzzy simulation Y1 - 2022 UR - https://www.sciencedirect.com/science/article/pii/S0303264721002318?via%3Dihub U6 - https://doi.org/10.1016/j.biosystems.2021.104592 SN - 0303-2647 VL - 212 SP - 1 EP - 10 ER - TY - GEN A1 - Assaf, George A1 - Heiner, Monika A1 - Liu, Fei T1 - Colouring Fuzziness for Systems Biology T2 - Theoretical Computer Science N2 - 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. KW - fuzzy logic KW - Fuzzy kinetic parameters KW - Coloured fuzzy continuous, stochastic and hybrid Petri nets KW - Modelling and simulation KW - Modelling uncertainty Y1 - 2021 UR - https://www.sciencedirect.com/science/article/abs/pii/S0304397521002152?via%3Dihub U6 - https://doi.org/10.1016/j.tcs.2021.04.011 SN - 1879-2294 SN - 0304-3975 VL - 875 SP - 52 EP - 64 ER -