Abstract

A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is divided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data; (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component; (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net–based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed.

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Acknowledgements

We are grateful to P. Digard and D. Hume for helpful discussions and advice; we thank A. Theocharidis for all his work on developing Graphia Professional. The Hedgehog signaling pathway model (Supplementary Data) was generated by M. Graute, a University of Edinburgh final-year B.Sc. student, as part of a 10-week elective course. We also thank the Biotechnology and Biological Sciences Research Council (BBSRC), which funded the development of BioLayout Express3D, the academic forerunner of Graphia Professional (BB/F003722/1 and BB/I001107/1). T.C.F. was supported by an Institute Strategic Program Grant on Transcriptomes, Networks and Systems (BBS/E/D/20211552).

Author information

Author notes

    • Alessandra Livigni
    •  & Laura O'Hara

    These authors contributed equally to this work.

Affiliations

  1. The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK.

    • Alessandra Livigni
    • , Laura O'Hara
    • , Tim Angus
    • , Derek W Wright
    •  & Tom C Freeman
  2. MRC Centre for Reproductive Health, Edinburgh, UK.

    • Laura O'Hara
    •  & Lee B Smith
  3. Clinical and Experimental Sciences, Sir Henry Wellcome Laboratories, Faculty of Medicine, University of Southampton, Southampton, UK.

    • Marta E Polak
  4. Institute for Life Sciences, University of Southampton, Southampton, UK.

    • Marta E Polak
  5. Faculty of Science, University of Newcastle, Callaghan, Australia.

    • Lee B Smith

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Competing interests

There is now a commercial and supported version of BioLayout Express3D called Graphia Professional, produced by Kajeka Ltd., (Edinburgh, UK) that possesses all the functionality described here for pathway modeling. T.C.F. is a founder and director of Kajeka, and T.A. is employed by the company as a software engineer. The other authors declared no competing financial interests.

Corresponding author

Correspondence to Tom C Freeman.

Supplementary information

Zip files

  1. 1.

    Supplementary Data

    mEPN palette for loading within yEd software; interferon-β signaling pathway components; primary network motifs drawn in Petri net style for testing the SPN algorithm; interferon-β signaling pathway; example of a more complex model, the Hedgehog signaling pathway; and examples of changing parameters—interferon-β signaling pathway.

Videos

  1. 1.

    Video of the interferon-β signaling pathway simulation run within BioLayout

    The video shows the process of model loading, running the simulation, inspecting token accumulation on specific components and watching the flow of tokens run through the model as an animation. See the Supplementary Data.

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DOI

https://doi.org/10.1038/nprot.2017.144

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