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data structures and software dependability

computer science department

brandenburg university of technology cottbus - senftenberg

BioModel Engineering — from Structure to Behaviour,
a Petri net perspective on systems and synthetic biology

Tutorial at Petri Nets 2009, Paris, 22 June 2009

Rainer Breitling1, David Gilbert2, Monika Heiner3
(1) Groningen Bioinformatics Centre, University of Groningen, Groningen, Netherlands
(2) School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex UB8 3PH, UK
(3) Computer Science Department, Brandenburg University of Technology, Cottbus, Germany


Quantitative models of biochemical networks are a central component of modern systems and synthetic biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted from theoretical computing science. In this tutorial we provide a general introduction to the field of BioModel Engineering, which emphasizes the intuitive biochemical basis of the modeling process, but is also accessible for an audience with a background in computing science and/or model engineering.

Please note, all tutorial material will be provided on a CD.

Tutorial level: Intermediate

Length: full day (4 x 1.5 h)



  • Introduction: Systems biology, synthetic biology [drg]
  • Conceptual modelling framework [drg]
  • BioModel Engineering [drg]
  • From cell biology to Petri nets (1) [rb]

10.30-11.00 Break


  • From cell biology to Petri nets (2) [rb]
  • Static Petri net analysis (qualitative) [mh]

12.30-14.00 Lunch


  • Conceptual modelling framework & paradigms in detail [mh] : (QPN), SPN, CPN
  • Model checking (1) [drg]: Analytical vs simulative model checking, model checking in the 3 worlds

15.30-16.00 Break


  • Model checking (2) [drg]: Model checking for BME: parameter fitting, database searching, model comparison
  • Open problems & challenges for PN community [all]
  • What we have not covered [all]

Expected audience: Suitable for computer scientists and engineers familiar with basic modelling approaches, as well as for biochemists with an understanding of biochemical networks. The target audience includes research students as well as researchers. The tutorial will illustrate the special challenges, which come with this application area, and also indicated some open problems which need to be solved. So the tutorial might be of interest to those looking for new challenges of their theoretical background.


Quantitative models of biochemical networks (signal transduction cascades, metabolic pathways, gene regulatory circuits) are a central component of modern systems biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted from theoretical computing science. The use of formal modelling for the description, analysis and engineering of molecular networks is both timely and highly required in the fields of Systems and Synthetic Biology.

This tutorial provides a general introduction to the field of formal modelling of biochemical networks, which emphasizes the intuitive biochemical basis of the modelling process, but is also accessible for an audience with a background in computing science and/or model engineering. Our structured approach for the engineering of biochemical network model applies qualitative, stochastic, as well as continuous Petri nets (ODEs) and corresponding analysis techniques, which are embedded into a general unifying framework. The tutorial will comprise demos of several related tools.

We show how signal transduction cascades can be modelled in a modular fashion, using both a qualitative approach – Qualitative Petri nets –, and quantitative approaches – Stochastic and Continuous Petri Nets. We review the major elementary building blocks of a cellular signalling model, discuss which critical design decisions have to be made during model building, and present a number of novel computational tools that can help to explore alternative modular models in an easy and intuitive manner. These tools, which are based on Petri net theory, offer convenient ways of composing hierarchical ODE models, and permit a qualitative analysis of their behaviour. We will show how the dynamic behaviour of such a pathway is related to its modular structure, and explore the use of stochastic versus deterministic techniques to generate behaviour traces. We will also introduce the use of simulative temporal logic model checking of the pathway to characterise behavioural properties.

Our approach is widely applicable to many kinds of biochemical networks. We illustrate the central concepts using signal transduction as our main example, and we will also show its applicability to metabolic pathways and gene regulation networks. The ultimate aim is to introduce a general approach that provides the foundations for a structured formal engineering of large-scale models of biochemical networks.

We will illustrate the construction of the models in Ordinary Differential Equations using the BioNessie workbench from the University of Glasgow. This SBML compatible tool permits model construction of the models with a biochemistry-oriented interface, parameter scanning, fitting, and model checking using linear temporal logic. We will also illustrate the use of MatLab and the SimBio toolbox for the construction of models, which permits the use of the powerful programming and analytical facilities of MatLab. Petri net approaches will be done using the Snoopy and Charlie tools, both from the Brandenburg University of Technology Cottbus. Snoopy supports qualitative as well as quantitative Petri nets, including continuous and stochastic Petri nets. Snoopy's export feature permits interfacing to various analysis tools devoted to standard Petri net theory, as well as a variety of model checkers. There is also an export allowing access to other tools such as BioNessie, MatLab and the Glasgow model checker permitting more detailed evaluations of continuous and stochastic Petri nets in addition to the standard algorithms of ODE solvers provided by Snoopy.


  1. David Gilbert, Rainer Breitling, Monika Heiner, Robin Donaldson: (2009). An Introduction to BioModel Engineering, Illustrated for Signal Transduction Pathways. in Proc. WMC 2008, Springer LNCS 5391, pp. 13-28, 2009.
  2. Rainer Breitling, David Gilbert, Monika Heiner, Richard Orton (2008). A structured approach for the engineering of biochemical network models, illustrated for signalling pathways. Briefings Bioinformatics September 2008; 9: 404 - 421 [preprint]
  3. Monika Heiner, David Gilbert, and Robin Donaldson (2008), Petri Nets for Systems and Synthetic Biology. In M Bernardo, P Degano, and G Zavattaro (Eds.): SFM 2008, Springer LNCS 5016, pp. 215-264, 2008.
  4. Xuan Liu, Jipu Jiang, Oluwafemi Ajayi, Xu Gu, David Gilbert, Richard Sinnott (2008). BioNessieG - A Grid Enabled Biochemical Networks Simulation Environment. Accepted for HealthGrid 2008. [preprint]
  5. David Gilbert, Monika Heiner and Sebastian Lehrack (2007). A Unifying Framework for Modelling and Analysing Biochemical Pathways Using Petri Nets In proceedings CMSB 2007 (Computational Methods in Systems Biology), Editors: M.Calder and S.Gilmore, Springer LNCS/LNBI 4695, pp. 200-216.
  6. David Gilbert and Monika Heiner, (2006). From Petri Nets to Differential Equations - an Integrative Approach for Biochemical Network Analysis, 27th International Conference on Application and Theory of Petri Nets and other models of concurrency (ATPN06), Turku, Finland, June 26-30, 2006. Proceedings; Editors: Susanna Donatelli, P. S. Thiagarajan; LNCS 4024 / 2006, pp. 181-200, ISBN: 3-540-34699-6 DOI: 10.1007/11767589
  7. David Gilbert, Hendrik Fuß, Xu Gu, Richard Orton, Steve Robinson, Vladislav Vyshemirsky, Mary Jo Kurth, C. Stephen Downes and Werner Dubitzky. (2006) Computational methodologies for modelling, analysis and simulation of signalling networks, Briefings in Bioinformatics 2006 7(4):339-353; doi: 10.1093/bib/bbl043 Special Issue: Computational Methodologies for Systems Biology. [Abstract], [Full Text], [PDF]




Tutorial URL: https://www-dssz.informatik.tu-cottbus.de/BME/PetriNets2009



The School of Information Science, Computing and Mathematics at Brunel University in London, UK is strengthening its activities in Bioinformatics and Systems Biology, as well as establishing new activities in Synthetic Biology.
There are several open positions for talented and able postdoctoral researchers and PhD students in the area of Computational Systems and Synthetic Biology at Brunel.
For more details, see David Gilbert's website.

The Groningen Bioinformatics Centre at the University of Groningen, The Netherlands, is looking for creative bioinformaticians with an interest in Systems Biology, Metabolomics, Proteomics, Quantitative Genetics, Network Reconstruction, Dynamic Modelling... to expand its young and successful team.
Several PhD and Postdoc positions are available immediately. For more information on our recent work, have a look at Rainer Breitling's website. There is also a partial list of GBiC vacancies at the GBiC homepage.
If you are interested in a career in Groningen, please try to talk to Rainer.

The International Max Planck Research School for Analysis, Design and Optimization in Chemical and Biochemical Process Engineering in Magdeburg/Germany (IMPRS), offers young researchers a stimulating and challenging atmosphere for education and research in a highly international and interdisciplinary environment. For open positions (PhD students and postdoctoral researchers) see Monika Heiner's website and of her IMPRS cooperation partner Wolfgang Marwan.


The work presented in this tutorial is partially funded under the 6th Framework Programme by the European Commission (Integrated biomedical information for better health), within the context of the SIMAP project, and under the Medical Systems Biology Framework (MedSys) by the German Federal Ministry of Education and Research (BMBF), within the context of the Modelling Pain Switches project.


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Any comments or questions are welcome. Please direct them to monika.heiner@b-tu.de Privacy Policy