Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

A graphical and computational modeling platform for biological pathways

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2: Construction of a simple pathway model describing type-1 interferon signaling.
Figure 3: Visualization of token flow.
Figure 4: Influencing token flow along a linear pathway.
Figure 5: Effect of parameterization on activity of feedback loop.

Similar content being viewed by others

References

  1. O'Hara, L. et al. Modelling the structure and dynamics of biological pathways. PLoS Biol. 14, e1002530 (2016).

    Article  Google Scholar 

  2. Raza, S. et al. A logic-based diagram of signalling pathways central to macrophage activation. BMC Syst. Biol. 2, 36 (2008).

    Article  Google Scholar 

  3. Freeman, T.C., Raza, S., Theocharidis, A. & Ghazal, P. The mEPN scheme: an intuitive and flexible graphical system for rendering biological pathways. BMC Syst. Biol. 4, 65 (2010).

    Article  Google Scholar 

  4. Kitano, H., Funahashi, A., Matsuoka, Y. & Oda, K. Using process diagrams for the graphical representation of biological networks. Nat. Biotechnol. 23, 961–966 (2005).

    Article  CAS  Google Scholar 

  5. Kohn, K.W., Aladjem, M.I., Weinstein, J.N. & Pommier, Y. Molecular interaction maps of bioregulatory networks: a general rubric for systems biology. Mol. Biol. Cell 17, 1–13 (2006).

    Article  CAS  Google Scholar 

  6. Moodie, S.L., Sorokin, A., Goryanin, I. & Ghazal, P. A graphical notation to describe the logical interactions of biological pathways. J. Integr. Bioinform. 3, 11 (2006).

    Article  Google Scholar 

  7. Novere, N.L. et al. The systems biology graphical notation. Nat. Biotechnol. 27, 735–741 (2009).

    Article  Google Scholar 

  8. Lopez, C.F., Muhlich, J.L., Bachman, J.A. & Sorger, P.K. Programming biological models in Python using PySB. Mol. Syst. Biol. 9, 646 (2013).

    Article  Google Scholar 

  9. Beltrame, L. et al. The Biological Connection Markup Language: a SBGN-compliant format for visualization, filtering and analysis of biological pathways. Bioinformatics 27, 2127–2133 (2011).

    Article  CAS  Google Scholar 

  10. Calzone, L., Gelay, A., Zinovyev, A., Radvanyi, F. & Barillot, E. A comprehensive modular map of molecular interactions in RB/E2F pathway. Mol. Syst. Biol. 4, 173 (2008).

    Article  Google Scholar 

  11. Kuperstein, I. et al. Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps. Oncogenesis 4, e160 (2015).

    Article  CAS  Google Scholar 

  12. Oda, K. & Kitano, H. A comprehensive map of the toll-like receptor signaling network. Mol. Syst. Biol. 2, 2006.0015 (2006).

    Article  Google Scholar 

  13. Oda, K., Matsuoka, Y., Funahashi, A. & Kitano, H. A comprehensive pathway map of epidermal growth factor receptor signaling. Mol. Syst. Biol. 1, 2005.0010 (2005).

    Article  Google Scholar 

  14. Raza, S. et al. Construction of a large scale integrated map of macrophage pathogen recognition and effector systems. BMC Syst. Biol. 4, 63 (2010).

    Article  Google Scholar 

  15. Wentker, P. et al. An interactive macrophage signal transduction map facilitates comparative analyses of high-throughput data. J. Immunol. 198, 2191–2201 (2017).

    Article  CAS  Google Scholar 

  16. Matsuoka, Y., Funahashi, A., Ghosh, S. & Kitano, H. Modeling and simulation using CellDesigner. Methods Mol. Biol. 1164, 121–145 (2014).

    Article  Google Scholar 

  17. Demir, E. et al. The BioPAX community standard for pathway data sharing. Nat. Biotechnol. 28, 935–942 (2010).

    Article  CAS  Google Scholar 

  18. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012).

    Article  CAS  Google Scholar 

  19. Wu, G., Dawson, E., Duong, A., Haw, R. & Stein, L. ReactomeFIViz: a cytoscape app for pathway and network-based data analysis. F1000Res 3, 146 (2014).

    PubMed  PubMed Central  Google Scholar 

  20. Yamada, T., Letunic, I., Okuda, S., Kanehisa, M. & Bork, P. iPath2.0: interactive pathway explorer. Nucleic Acids Res. 39, W412–W415 (2011).

    Article  CAS  Google Scholar 

  21. Czauderna, T., Klukas, C. & Schreiber, F. Editing, validating and translating of SBGN maps. Bioinformatics 26, 2340–2341 (2010).

    Article  CAS  Google Scholar 

  22. Croft, D. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014).

    Article  CAS  Google Scholar 

  23. Joshi-Tope, G. et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 33, D428–432 (2005).

    Article  CAS  Google Scholar 

  24. Kuperstein, I. et al. NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps. BMC Syst. Biol. 7, 100 (2013).

    Article  Google Scholar 

  25. Mi, H. & Thomas, P. PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. Methods Mol. Biol. 563, 123–140 (2009).

    Article  CAS  Google Scholar 

  26. Hucka, M. et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003).

    Article  CAS  Google Scholar 

  27. Bause, F. & Kritzinger, P.S. Stochastic Petri Nets: An Introduction to the Theory (Vieweg & Teubner, 1996).

  28. Reddy, V.N., Mavrovouniotis, M.L. & Liebman, M.N. Petri net representations in metabolic pathways. Proc. Int. Conf. Intell. Syst. Mol. Biol. 1, 328–336 (1993).

    CAS  PubMed  Google Scholar 

  29. Bahi-Jaber, N. & Pontier, D. Modeling transmission of directly transmitted infectious diseases using colored stochastic Petri nets. Math. Biosci. 185, 1–13 (2003).

    Article  Google Scholar 

  30. Chaouiya, C. Petri net modelling of biological networks. Brief Bioinform. 8, 210–219 (2007).

    Article  CAS  Google Scholar 

  31. Heiner, M., Koch, I. & Will, R. Model validation of biological pathways using Petri nets – demonstrated for apoptosis. Biosystems 75, 15–28 (2004).

    Article  Google Scholar 

  32. Peleg, M., Rubin, D. & Altman, R.B. Using Petri net tools to study properties and dynamics of biological systems. J. Am. Med. Inform. Assoc. 12, 181–199 (2005).

    Article  Google Scholar 

  33. Taubner, C., Mathiak, B., Kupfer, A., Fleischer, N. & Eckstein, S. Modelling and simulation of the TLR4 pathway with coloured Petri nets. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1, 2009–2012 (2006).

    Article  CAS  Google Scholar 

  34. Balazki, P., Lindauer, K., Einloft, J., Ackermann, J. & Koch, I. MONALISA for stochastic simulations of Petri net models of biochemical systems. BMC Bioinform. 16, 215 (2015).

    Article  Google Scholar 

  35. Marwan, W., Rohr, C. & Heiner, M. Petri nets in Snoopy: a unifying framework for the graphical display, computational modelling, and simulation of bacterial regulatory networks. Bact. Mol. Netw.: Methods Protoc. 804, 409–437 (2012).

    Article  CAS  Google Scholar 

  36. Ramos, H. et al. The protein information and property explorer 2: gaggle-like exploration of biological proteomic data within one webpage. Proteomics 11, 154–158 (2011).

    Article  CAS  Google Scholar 

  37. Ruths, D., Muller, M., Tseng, J.T., Nakhleh, L. & Ram, P.T. The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks. PLoS Comput. Biol. 4, e1000005 (2008).

    Article  Google Scholar 

  38. Li, C. et al. Structural modeling and analysis of signaling pathways based on Petri nets. J. Bioinform. Comput. Biol. 4, 1119–1140 (2006).

    Article  CAS  Google Scholar 

  39. David, R. & Alla, H. Discrete, Continuous, and Hybrid Petri Nets, 2nd edn. (Springer, 2010).

  40. Theocharidis, A., van Dongen, S., Enright, A.J. & Freeman, T.C. Network visualization and analysis of gene expression data using BioLayout Express(3D). Nat. Protoc. 4, 1535–1550 (2009).

    Article  CAS  Google Scholar 

  41. Polak, M.E., Ung, C.Y., Masapust, J., Freeman, T.C. & Ardern-Jones, M.R. Petri net computational modelling of Langerhans cell interferon regulatory factor network predicts their role in T cell activation. Sci. Rep. 7, 668 (2017).

    Article  Google Scholar 

  42. Di Ventura, B., Lemerle, C., Michalodimitrakis, K. & Serrano, L. From in vivo to in silico biology and back. Nature 443, 527–533 (2006).

    Article  CAS  Google Scholar 

  43. de Jong, H. Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9, 67–103 (2002).

    Article  CAS  Google Scholar 

  44. Friesen, W.O. & Block, G.D. What is a biological oscillator? Am. J. Physiol. 246, R847–853 (1984).

    CAS  PubMed  Google Scholar 

  45. Pertsovskaya, I., Abad, E., Domedel-Puig, N., Garcia-Ojalvo, J. & Villoslada, P. Transient oscillatory dynamics of interferon beta signaling in macrophages. BMC Syst. Biol. 7, 59 (2013).

    Article  CAS  Google Scholar 

Download references

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

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tom C Freeman.

Ethics declarations

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.

Supplementary information

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. (ZIP 162 kb)

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. (MP4 3735 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Livigni, A., O'Hara, L., Polak, M. et al. A graphical and computational modeling platform for biological pathways. Nat Protoc 13, 705–722 (2018). https://doi.org/10.1038/nprot.2017.144

Download citation

  • Published:

  • Issue Date:

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

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing