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Tutorials Descriptions

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Descriptions (as of Aug 21, 2006)

Tutorial 1:
Engineering design principles for biologists

Tutors: Kyaw Tun1, Arun Krishnan2, Pawan K. Dhar3
1 Department of Chemical and Biomolecular Engineering, National University of Singapore
2 Institute for Advanced Biosciences, Tsuruoka, Keio University, Japan
3 Genomic Sciences Centre, RIKEN Yokohama Institute, Japan

Expected number of participants: 50
Time: 2 hours

The recent emergence of Synthetic Biology has led to the significant use of engineering concepts and terminologies in biology.  To bridge the gap between what used to be two stand alone disciplines, the tutorial aims to explain the fundamentals of electronic circuit design to biologists with no background in electronics and engineering.
The first part would comprise of a quick introduction to concepts in analog and digital circuitry e.g., resistor, transistor, logic gates, Kirchoff's laws, k-maps, modularity, hierarchy and abstraction as well as methods and tools for circuit analysis.
The second part would entail the discussion of  control theoretic concepts in electronic circuit design. Participants would learn how complex behavior can be achieved by using modular component reuse, feedback loops and parameter tuning.
Finally, in the third  part advantages and limitations of electronics approaches, recent biological examples and future developmental needs of the community would be examined.


Tutorial 2:
Structural and functional analysis of signaling networks
Steffen Klamt, Julio Saez-Rodriguez (Max Planck Institute Magdeburg, Germany)
Expected number of participants: 50
Time: 2 hours (45min. theory + 30min. break + 45min. hands-on exercises)

The aim of systems biology is a holistic understanding of biological networks. The probably most common approach is the construction and analysis of dynamic models. Therefor, not only information about the structure of the network is needed, but also a high amount of quantitative experimental data in order to estimate or measure the model parameters. While this approach is plausible for tackling single pathways in cellular networks, it becomes intractable for a holistic analysis of large networks with hundreds of players and interactions. Therefore, qualitative approaches relying solely on the often well-known network structure are of great interest.
Whereas the theory for structural (stoichiometric) analysis of metabolic networks is now established and extensively used by the scientific community, only few approaches have been employed for a structural analysis of signaling and regulatory/genetic networks in a similar way.
In this tutorial, we present a number of partially new approaches and techniques aiming to a functional and qualitative analysis of the topology of cellular signaling and regulatory networks. These methods have been embedded in our software CellNetAnalyzer (successor of FluxAnalyzer; freely available (for academic use) via: )


Tutorial 3:
New Mathematical Methods for Systems Biology
Eric Mjolsness (UC Irvine)
Time: 3 hours

Expectations and ambitions for the future of computational systems biology are ever growing, but several significant problems of applied mathematics and modeling stand in the way. These problems include the relations between stochastic and deterministic models and simulation algorithms, adequate models of molecular complexes, the role of spatial inhomogeneity at subcellular and multicellular scales, modeling biological graph structure and dynamics, inference from heterogeneous data sets, and the reuse and integration of modeling techniques across spatial scales from molecular to developmental and ecological.
Fortunately, there are relevant branches of applied mathematics that have been underexploited in attacking these problems, and it’s not too hard to understand their foundations. I suggest that the basic mathematical toolkit for systems biology will come to include not only such staples as differential equation and graphical probabilistic models, but also operator algebras, contextsensitive grammars, stochastic field theory of both particle-like and extended objects , partition functions, aspects of algebraic geometry, and dynamical systems defined on static and dynamic graphs. I will explain why, what, and how, and give examples from many spatial and temporal scales: bacterial metabolism, eukaryotic transcriptional regulation and signal transduction, developmental biology of plants including phyllotaxis, and population biology.


Tutorial 4:
Tutorial on the Systems Biology Toolbox for MATLAB
Henning Schmidt (Fraunhofer Chalmers Research Centre)

The Systems Biology Toolbox has been developed as a toolbox for MATLAB and aims at being a user-friendly and user extensible, software-based, mathematical analysis framework for biological and biochemical systems. The toolbox enables the user to access all data and data structures, resulting in full control over the tasks to be performed and the possibility of focusing on the tasks one is interested in, leading to a faster workflow and accelerated scientific advancement.
MATLAB ( is a de facto standard in many scientific areas and already widely used in Systems Biology. It provides numerous state-of-the-art mathematical and numerical methods and a user-programmable platform, using a simple, but powerful, high-level scripting language that avoids programming overhead usually present when developing stand-alone software applications. Users will find it easy to use the functionality of the Systems Biology Toolbox. In order for a user to extend the functionality of the toolbox, knowledge about the MATLAB scripting language is required. However, users that are not familiar with MATLAB in advance will find it much easier to learn this scripting language, than to learn how to program operating system dependent stand alone applications. The Systems Biology Toolbox is free software and can be downloaded at:


Tutorial 5:
Pathway Modeling with Teranode XDA
Mike Kellen, Eric Neumann, Zheng Li (Teranode)

Tutorial will focus on Teranode's system biology tools for managing information related to biological pathways that allow you to build open repositories to collaboratively manage data and knowledge around biological systems. As an example, we will discuss how our semantic web technology is helping to build Neurocommons, an open public repository of data related to neurological diseases from communities of scientists.


Tutorial 6:
Analyzing Biochemical Systems using the E-Cell System
Nathan Addy, Satya Arjunan, Bin Hu, Yuri Matsuzaki, Martin Robert, Takeshi Sakurada Koichi Takahashi (Keio University)
Expected number of participants: 20
Time: 3 hours

Bifurcation and sensitivity analysis can be used to elucidate the relationship between the dynamics of a nonlinear system in biology and the parameters of the system. The bifurcation program in E-Cell numerically computes the stable states of the system, such as the stable or oscillating point, with graphical representation of results. Elasticity coefficients with respect to amplitude and frequency, which indicate the robustness of the oscillation are also represented. Participants will experiment with these features hands-on using a simple oscillation model – the Drosophila circadian cycle model [1].
Metabolic control analysis can demonstrate how fluxes and intermediate concentrations in a metabolic pathway are regulated by the enzymes that constitute the system. The analysis encompasses structural analysis, elasticity coefficients and the sensitivity of metabolites to small changes in individual parameters such as in enzyme concentrations or kinetic parameters. Flux and concentration control coefficients are some of the outcomes of metabolic control analysis. Participants will use metabolic control analysis to evaluate the Kuchel's erythrocyte model [2].

[1] Goldbeter, A. (1995). A model for circadian oscillations in the Drosophila period protein (PER). Proc. Biol. Sci. 261 (1362): 319-24
[2] Mulquiney, P. J. and Kuchel, P. W. (1999). Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: equations and parameter refinement. Biochem J. 342 (3): 581-96.


Tutorial 7:
Computational Cell Biology with the Virtual Cell
Ion I. Moraru and James C. Schaff (University of Connecticut Health Center)
Time: 3 hours

The Virtual Cell ( is a unique software environment for computational cell biological research developed at the Richard D. Berlin Center for Cell Analysis and Modeling (CCAM) at the University of Connecticut Health Center. CCAM is a NIH Technology Center for Networks and Pathways and a NIH-designated National Research Resource. The center integrates new microscope technologies for making quantitative in vivo live cell measurements with new physical formulations and computational tools that will produce spatially realistic quantitative models of intracellular dynamics.
The latter are being made available for the use of researchers worldwide through their gradual integration into the public, web-accessible, Virtual Cell framework. The Virtual Cell has been continuously and rapidly growing in capabilities and complexity over the past several years. A wide range of applications of the Virtual Cell are being developed as in-house research projects (e.g. calcium dynamics in neuronal cells and RNA trafficking in oligodendrocytes) as well as external collaborations. Additionally, to date, more than 1,000 independent users worldwide have created and run simulations with the Virtual Cell.


Tutorial 8:
Modeling, simulating, and analyzing biochemical systems with Copasi
Tutorial 9:
Advanced model analysis with Copasi
Pedro Mendes (Virginia Bioinformatics Institute),
Sven Sahle, Ralph Gauges, Juergen Pahle and Ursula Kummer (EML Research, Heidelberg)
Expected number of participants: 30
Time: 3 hours

Tutorial 8: Modeling, simulating, and analyzing biochemical systems with Copasi
Copasi (Complex Pathway Simulator) is a software application for simulation and analysis of biochemical networks. It is developed jointly by the groups of Pedro Mendes (Virginia Bioinformatics Institute, USA) and Ursula Kummer (EML Research, Germany), and is freely available for academic use.
• Copasi's current features include stochastic and deterministic time course simulation, steady-state analysis (including stability), metabolic control analysis, elementary mode analysis, mass conservation analysis, import and export of SBML level 2, optimization, parameter scanning and parameter fitting. It runs on MS Windows, Linux, OS X, and Solaris SPARC. So, it is one of the few computational tools in systems biology that are OS X compatible.
• We will use Copasi to explain how the modelling, simulation and computational analysis of biochemical systems works. We will also critically evaluate the limitations of different simulation methods.

Tutorial 9: Advanced model analysis with Copasi
• Copasi (Complex Pathway Simulator) is a software application for simulation and analysis of biochemical networks. It is developed jointly by the groups of Pedro Mendes (Virginia Bioinformatics Institute, USA) and Ursula Kummer (EML Research, Germany), and is freely available for academic use.
• Besides doing deterministic and stochastic time course simulation, Copasi has a number of advanced methods for the analysis of biochemical reaction networks.
• In this course we will explain how Copasi can be used to do parameter scans and parameter estimation.
• Another topic will be how users can use Copasi to optimize certain model aspects as for example the flux through a certain reaction, via the optimization task.
• Although Copasi has a large number of predefined output definitions that the user can choose from, it is sometimes desirable to define your own custom output. In this course we will show how the user can define his/her own complex reports with Copasi's powerful report widget.


Tutorial 10:
Akira Funahashi (The Systems Biology Institute/JST)

CellDesigner is a software for modeling and simulation of biochemical and gene regulatory networks, originally developed by the Systems Biology Institute in Japan. While CellDesigner itself is a sophisticated structured diagram editor, it enables users to directly integrate various tools, such as built-in SBML ODE Solver and SBW-powered simulation/analysis modules.
In this course, we will explain how CellDesigner can be used from both modeling and software development perspectives. The first topic will feature network modeling using CellDesigner, and will show how she/he could build a model from scratch, and examine simulations. The second topic will feature plugin development of CellDesigner, which allows users to manipulate network diagram in many ways (for example changing the color/size of node, reflecting experimental data etc.).
This tutorial will cover both modeling and software development topics, thus both CellDesigner users and software developers are encouraged to join. Bringing your notebook PC is highly recommended.


Tutorial 11:
Application of Experimental Design and Model Selection to Signal Transduction Pathway Modeling
Thomas Maiwald, Marcel Schilling, Sebastian Bohl (University of Freiburg, German Cancer Research Center )
Expected number of participants: 40
Time: 1 day

Based on the new user-friendly Matlab parameter estimation and model selection framework PottersWheel

A major challenge in Systems Biology is to evaluate the feasibility of a relevant biological research agenda prior to its realization. Since experiments are animals-, cost- and time-consuming, approaches allowing researchers to discriminate alternative hypotheses with a minimal set of experiments are highly desirable. In a close collaboration of theoreticians and experimentalists we have developed an experimental design approach for the modeling of signal transduction pathways. It can be applied on model selection and parameter estimation tasks and considers laboratory constraints, like limited number of measurable players or data points, noise level and cell type specific experimentally realizable stimulations. All concepts have been implemented in the user-friendly Matlab software 'PottersWheel', allowing modelers and experimentalists to study their systems and to obtain an optimal experimental design for a given research question. Due to its clear concept and graphical user interface PottersWheel is easy to use and does not require experience in Matlab or another programming language. It is therefore well suited fo scientists with basic computer skills as well as those with profound knowledge.

The workshop will start with an introduction to the software package presenting the main features of PottersWheel as well as key concepts of experimental design. This is followed by hands-on experience allowing the participants to use PottersWheel to improve their individual skills in experimental design in a realistic setting. We will present four SBML compatible ODE models describing the activation and deactivation of a hypothetical cell membrane receptor by stimulation with an external ligand. The participants shall evaluate the different models, identify the correct one and estimate its parameter values based on quantitative experimental data as accurate as possible. This should be achieved with a minimum effort, i.e., a minimum number of required data points. The realistic situation helps to transfer the acquired methods to the individual modeling challenges of each participant.


Tutorial 12:
The Systems Biology Markup Language (SBML) Level 2 Version 2
Michael Hucka (California Institute of Technology)

The Systems Biology Markup Language (SBML) is a machine-readable model representation language for software tools in computational systems biology. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. SBML is by no means a perfect format, but it has achieved widespread acceptance as a de facto standard. It is supported worldwide by over 100 software systems (both open-source and commercial). The broad acceptance of a common, open format for exchanging models between software tools is a crucial step towards wider use of quantitative modeling in biology, because it allows researchers to build upon each other's work with greater ease and accuracy.

SBML can encode models consisting of biochemical entities (species) linked by reactions to form networks. An important principle is that models are decomposed into explicitly-labeled constituent elements, the set of which resembles a verbose rendition of chemical reaction equations. The representation deliberately does not cast the model directly into a set of differential equations or other specific interpretation of the model. The formalisms in SBML allows a wide range of biological phenomena to be modeled, including metabolism, cell signaling, gene regulation, and more. Significant flexibility and power comes from the ability to define arbitrary formulae for the rates of change of variables as well as the ability to express other constraints mathematically.

This tutorial will cover the latest edition of SBML, which is Level 2 Version 2, currently being finalized this year. Topics to be covered include the basic common principles in SBML as well the changes introduced in Level 2 Version 2. We will also discuss software tools for programmers, in particular libSBML.



How to register

All the tutorial registrataions are handled by the tutorial organizers independently. Please check tutorial websites/organizers for registration information.

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