TY - GEN A1 - Colombo, Riccardo A1 - Damiani, Chiara A1 - Gilbert, David A1 - Heiner, Monika A1 - Mauri, Giancarlo A1 - Pescini, Dario T1 - Emerging ensembles of kinetic parameters to identify experimentally observed phenotypes T2 - BMC bioinformatics KW - Ensembles KW - Fluxes KW - Kinetic parameters KW - Mechanistic simulations KW - Metabolism KW - ODEs KW - Steady state KW - Systems biology Y1 - 2018 UR - https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2181-7 U6 - https://doi.org/10.1186/s12859-018-2181-7 SN - 1471-2105 VL - 19 IS - Suppl. 7 SP - 46 EP - 59 ER - 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 -