TY - GEN A1 - Connolly, Shannon A1 - Gilbert, David A1 - Heiner, Monika T1 - From Epidemic to Pandemic Modelling T2 - Frontiers in Systems Biology N2 - 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. KW - SIR model KW - coloured Petri nets KW - stochastic Petri nets KW - continuous Petri nets KW - ODEs KW - simulation KW - geographic spatio-temporal modelling KW - multiscale models Y1 - 2022 UR - https://www.frontiersin.org/articles/10.3389/fsysb.2022.861562/full U6 - https://doi.org/10.3389/fsysb.2022.861562 SN - 2674-0702 VL - 2 SP - 1 EP - 23 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 -