Translation from the Quantified Implicit Process Flow Abstraction in SBGN-PD Diagrams to Bio-PEPA Illustrated on the Cholesterol Pathway

  • Laurence Loewe
  • Maria Luisa Guerriero
  • Steven Watterson
  • Stuart Moodie
  • Peter Ghazal
  • Jane Hillston
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6575)


For a long time biologists have used visual representations of biochemical networks to gain a quick overview of important structural properties. Recently SBGN, the Systems Biology Graphical Notation, has been developed to standardise the way in which such graphical maps are drawn in order to facilitate the exchange of information. Its qualitative Process Description (SBGN-PD) diagrams are based on an implicit Process Flow Abstraction (PFA) that can also be used to construct quantitative representations, which facilitate automated analyses of the system. Here we explicitly describe the PFA that underpins SBGN-PD and define attributes for SBGN-PD glyphs that make it possible to capture the quantitative details of a biochemical reaction network. Such quantitative details can be used to automatically generate an executable model. To facilitate this, we developed a textual representation for SBGN-PD called “SBGNtext” and implemented SBGNtext2BioPEPA, a tool that demonstrates how Bio-PEPA models can be generated automatically from SBGNtext. Bio-PEPA is a process algebra that was designed for implementing quantitative models of concurrent biochemical reaction systems. The scheme developed here is general and can be easily adapted to other output formalisms. To illustrate the intended workflow, we model the metabolic pathway of the cholesterol synthesis. We use this to compute the statin dosage response of the flux through the cholesterol pathway for different concentrations of the enzyme HMGCR that is inhibited by statin.


Process Algebra Graphical Editor Species Component Biochemical Reaction Network Cholesterol Production 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Laurence Loewe
    • 1
  • Maria Luisa Guerriero
    • 1
  • Steven Watterson
    • 1
    • 2
  • Stuart Moodie
    • 3
  • Peter Ghazal
    • 1
    • 2
  • Jane Hillston
    • 1
    • 3
  1. 1.Centre for System Biology at Edinburgh, King’s BuildingsThe University of EdinburghEdinburghScotland
  2. 2.Division of Pathway MedicineThe University of EdinburghUK
  3. 3.School of InformaticsThe University of EdinburghUK

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