Coloured Hybrid Petri Nets: An adaptable modelling approach for multi-scale biological networks. (October 2018)

• Record Type: Journal Article
• Authors: Herajy, Mostafa; Liu, Fei; Rohr, Christian; Heiner, Monika
• Is Part Of: Computational biology and chemistry. Volume 76(2018)
• Page Start: 87
• Page End: 100
• Publication Date: 2018-10
• Subjects: Coloured Petri nets -- Hybrid Petri Nets -- Multi-scale modelling -- Bistable switch -- Dendritic spines; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.05.023
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Coloured Hybrid Petri Nets are proposed to model complex biological networks that exhibit multiple spatial and temporal scales. Hybrid simulation is shown to be a powerful tool to analyze biological models with multiple time scales. Two case studies illustrate the applicability of the approach for addressing the multiscale challenges of biological systems. Abstract: Coloured Petri nets are an excellent choice for exploring large biological models, particularly when there are repetitions of components. Such models can be easily adapted by slight modifications of parameter values related to colours. Similarly, multi-scale models could involve multiple spatial scales in addition to multiple time scales. Thus, they require the full interplay between stochastic as well as deterministic processes. In this paper we take these two aspects into account and present a modelling and simulation approach for multi-scale biochemical networks using Coloured Generalised Hybrid Petri Nets ( G H P N C ). G H P N C are a Petri net class that associates colours to Generalised Hybrid Petri Nets ( G H P N ), which incorporate discrete and continuous places in addition to stochastic and continuous transitions. Moreover, we present two case studies to illustrate typical applications taking advantage of such a Petri net class.