a Petri net perspective on systems and synthetic biology
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)
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.
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.