Tutorial 1
Tutorial 2
Tutorial 3
Tutorial 4
Tutorial 5
Tutorial 6
Tutorial 7
Tutorial 8
Tutorial 1
Herbert Sauro, Vijay Chickarmane, Keck Graduate Institute
The Systems Biology Workbench (SBW) is the only extensible software framework specifically designed for Systems Biology. Originally developed under the Kitano ERATO project at Caltech, it has continued to be developed under the auspices of DARPA and the DOE.
This tutorial will introduce the basic concepts of SBW and in particular introduce modeling concepts using JDesigner and Jarnac.
A number of areas of interest to novice modelers will be introduced:
In this tutorial, intervals of lecture will be interspersed with demonstrations to highlight the relevant concepts. The instructors' website, www.sys-bio.org, will contain the tutorial material and downloadable software. Participants will find it very helpful to bring their own laptop computers.
The instructors will be Herbert Sauro and Vijay Chickarmane; software assistance will be provided by Frank Bergmann.
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Tutorial 2
Alexander van Oudenaarden, Massachusetts Institute of Technology
Biochemical reactions that involve small numbers of molecules are intrinsically noisy, being dominated by large concentration fluctuations. Although ignored in most genetic network models, the reality is that the level of gene expression of the same gene can vary enormously from one cell to another within a genetically identical population. This tutorial will focus on the mathematical tools that are available to model stochastic gene expression, and on recent experimental progress on revealing the origins of the main cellular noise sources.
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Tutorial 3
Nir Friedman, Hebrew University
This tutorial will give an introduction to graphical probabilistic models – a class of models including Bayesian Networks and Markov Random Fields. These are closely related to models, such as Hidden Markov Models and probabilistic phylogeny models, that are often used in molecular biology. I will describe the foundations of these models, discuss recent tools for inference and learning of such models, and survey applications of graphical models to address data analysis and discovery from high-throughput data. Finally, I will outline some of the outstanding technical and biological challenges.
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Tutorial 4
Numerica Technology, LLC
JACOBIAN is a comprehensive modeling environment to build, validate and deploy biological models written as ordinary and partial differential equations. Originally developed at MIT, the core of JACOBIAN software is a suite of very powerful computational technologies – parametric sensitivity analysis, parameter estimation, global dynamic optimization, robust and accurate solution of hybrid discrete/continuous problems, analysis of steady-state or equilibrium solutions – that have been successfully applied in systems biology modeling.
This tutorial will provide hands-on training in using JACOBIAN software to build, validate and deploy models using example models from signaling pathways from the literature (1-3). A brief outline of the tutorial is as follows:
Further description of JACOBIAN is available at www.numericatech.com ; for further information on this tutorial, email info@numericatech.com.
NOTE: This tutorial will be held in room 312, building 37 of the Massachusetts Institute of Technology, in Cambridge, Massachusetts. MAP to MIT, Bldg. 37
1. EGF endocytosis model from Lauffenburger, D. A. & Linderman, J. J. Receptors: Models for Binding, Trafficking, and Signaling, Ch. 3 (Oxford University Press, 1993).
2. Bentele, M. et al. Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. J Cell Biol. 166, 839-851 (2004).
3. Sarkar, C. A. & Lauffenburger, D.A. Cell-level pharmacokinetic model of granulocyte colony-stimulating factor: implications for ligand lifetime and potency in vivo. Molecular Pharmacology 63, 147-158 (2003).
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Tutorial 5
Stefan Hoops, Virginia Bioinformatics Institute, and Sven Sahle, EML Research
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 and parameter scanning. It runs on MS Windows, Linux, OS X, and Solaris SPARC. There are not many 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. Therefore, this tutorial is primarily aimed at experimentalists who are newcomers to the computational side of systems biology. We will also critically evaluate the limitations of different simulation methods, which should be useful for some of the more experienced computational scientists as well.
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Tutorial 6
Henning Schmidt and Mats Jirstrand, Fraunhofer-Chalmers Research Centre
In this tutorial, we will demonstrate a Systems Biology Toolbox for MATLAB, offering systems biologists an open and extensible environment in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological systems. Currently implemented functionality of the toolbox includes: model building, model import from SBML, model export functions (SBML, MATLAB m-file, textual description), simulation (both standard simulation and a more "in silico experiment" type of simulation), graphical user interfaces for model editing and visualization of results, analysis functions (steady-state, mass conservation, Jacobian+stability, stoichiometry), network identification, simple model reduction, bifurcation analysis, parameter sensitivity analysis (often called metabolic control analysis within systems biology), and localization of mechanisms leading to complex behaviors (for example, sustained oscillations and bistability). Furthermore, the toolbox contains the possibility of representing and handling experimental data, import from and to Excel sheets and text-files, use of these data for parameter estimation, network identification, and visualization. At the time of the tutorial, parameter estimation functionality will also be available in the toolbox.
The use of MATLAB as a basis allows the toolbox to be independent of the operating system. This means it can be used on Windows, Linux/Unix, and MAC OS. Secondly, it allows easy access to all data and data structures, enabling the user to do exactly what s/he wants to do, instead of being limited by the tool. Furthermore, since the toolbox is build in a modular way and does not require compilation, the user can add new functions or edit present functions without an effort.
The Systems Biology Toolbox is free software and can be downloaded at: www.fcc.chalmers.se/~henning/SBtoolbox
Tutorial Outline
The participants of the tutorial are invited to bring their own laptops to get some hands-on experience. All needed files will be provided on CD and the web. The toolbox requires at least MATLAB Release 14 to be installed.
Instructors
Henning Schmidt, PhD (developer of the toolbox), Fraunhofer-Chalmers Research Centre, Gothenburg, Sweden, henning@fcc.chalmers.se
Mats Jirstrand, Assoc. Prof., Fraunhofer-Chalmers Research Centre, Gothenburg, Sweden, matsj@fcc.chalmers.se
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Tutorial 7
Michael Reich and Ted Liefeld, Broad Institute of MIT and Harvard
GenePattern is a software package for the analysis of data from microarrays and other genomic platforms. It provides a wide variety of analyses and powerful capabilities to combine and extend them. GenePattern is freely available and compatible with MacOS, Windows, and Linux.
In this tutorial, participants will learn how to do microarray analysis using the features of GenePattern, including:
Participants are encouraged to download GenePattern from www.genepattern.org before the tutorial. Sample data will be provided, but users will get the most out of this tutorial if they bring their own data to analyze.
Top
Tutorial 8
Ernest Fraenkel and Kenzie MacIsaac, Whitehead Institute for Biomedical Research and Massachusetts Institute of Technology
The DNA-binding specificity of proteins can be modeled by DNA sequence motifs. In this tutorial, we address what motifs are, why they are interesting, and how to find them. We discuss the challenges associated with motif discovery, and approaches that have been taken to overcome these challenges. We present an overview of some commonly used motif discovery computer programs, comparing their performance, and we discuss how the performance of standard algorithms is improved through the use of phylogenetic conservation information. We stress the importance of validating the output of these algorithms and present a method for performing such validation. We also suggest a practical strategy for merging the results of several motif discovery tools, and emphasize the utility of such strategies. Finally, we discuss how motifs, in concert with phylogenetic conservation information, can be used to scan genomes for putative regulatory sites. Throughout the tutorial we focus on practical concerns, and illustrate the important steps of motif discovery by example.
Top
Morning Tutorials
Tutorial 1
The Systems Biology Workbench: model building and model analysis
Herbert Sauro, Vijay Chickarmane, Keck Graduate Institute
The Systems Biology Workbench (SBW) is the only extensible software framework specifically designed for Systems Biology. Originally developed under the Kitano ERATO project at Caltech, it has continued to be developed under the auspices of DARPA and the DOE.
This tutorial will introduce the basic concepts of SBW and in particular introduce modeling concepts using JDesigner and Jarnac.
A number of areas of interest to novice modelers will be introduced:
- Introducing the concept of time course and steady state behavior, understanding the different kinds of models, particular continuous and stochastic models (30 mins)
- Brief introduction to the control of enzyme pathways using Metabolic Control Analysis (30 mins)
- Understanding basic network motifs, such as bistable and oscillatory circuits (40 mins) in signaling networks, how to identify them in networks.
- Understanding the role of positive and negative feedback in modifying the behavior of networks (30 mins).
- Tips on how to build models when data is incomplete, strategies on how to fit models to data, approaches to model validation (40 mins) and model exchange using SBML.
In this tutorial, intervals of lecture will be interspersed with demonstrations to highlight the relevant concepts. The instructors' website, www.sys-bio.org, will contain the tutorial material and downloadable software. Participants will find it very helpful to bring their own laptop computers.
The instructors will be Herbert Sauro and Vijay Chickarmane; software assistance will be provided by Frank Bergmann.
Top
Tutorial 2
Stochastic gene expression: from theoretical models to experiments
Alexander van Oudenaarden, Massachusetts Institute of Technology
Biochemical reactions that involve small numbers of molecules are intrinsically noisy, being dominated by large concentration fluctuations. Although ignored in most genetic network models, the reality is that the level of gene expression of the same gene can vary enormously from one cell to another within a genetically identical population. This tutorial will focus on the mathematical tools that are available to model stochastic gene expression, and on recent experimental progress on revealing the origins of the main cellular noise sources.
Top
Tutorial 3
Graphical probabilistic models in systems biology
Nir Friedman, Hebrew University
This tutorial will give an introduction to graphical probabilistic models – a class of models including Bayesian Networks and Markov Random Fields. These are closely related to models, such as Hidden Markov Models and probabilistic phylogeny models, that are often used in molecular biology. I will describe the foundations of these models, discuss recent tools for inference and learning of such models, and survey applications of graphical models to address data analysis and discovery from high-throughput data. Finally, I will outline some of the outstanding technical and biological challenges.
Top
Tutorial 4
JACOBIAN software for model building, validation and deployment in systems biology
Numerica Technology, LLC
JACOBIAN is a comprehensive modeling environment to build, validate and deploy biological models written as ordinary and partial differential equations. Originally developed at MIT, the core of JACOBIAN software is a suite of very powerful computational technologies – parametric sensitivity analysis, parameter estimation, global dynamic optimization, robust and accurate solution of hybrid discrete/continuous problems, analysis of steady-state or equilibrium solutions – that have been successfully applied in systems biology modeling.
This tutorial will provide hands-on training in using JACOBIAN software to build, validate and deploy models using example models from signaling pathways from the literature (1-3). A brief outline of the tutorial is as follows:
- Model Building: import existing models in SBML, write new models with the graphical JACOBIAN Model Builder and the JACOBIAN Modeling Language, etc.
- Model Validation: identify dominant parameters by Sensitivity Analysis, fit selected model parameters by Parameter Estimation, analyze statistical results of the fit, etc.
- Model Deployment: deploy JACOBIAN model in other software like Excel, in-house software systems or Web for broad distribution of the model, conduct multi-variable grid-search of feasible regions of model solutions, etc.
Further description of JACOBIAN is available at www.numericatech.com ; for further information on this tutorial, email info@numericatech.com.
NOTE: This tutorial will be held in room 312, building 37 of the Massachusetts Institute of Technology, in Cambridge, Massachusetts. MAP to MIT, Bldg. 37
1. EGF endocytosis model from Lauffenburger, D. A. & Linderman, J. J. Receptors: Models for Binding, Trafficking, and Signaling, Ch. 3 (Oxford University Press, 1993).
2. Bentele, M. et al. Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. J Cell Biol. 166, 839-851 (2004).
3. Sarkar, C. A. & Lauffenburger, D.A. Cell-level pharmacokinetic model of granulocyte colony-stimulating factor: implications for ligand lifetime and potency in vivo. Molecular Pharmacology 63, 147-158 (2003).
Top
Afternoon Tutorials
Tutorial 5
Modeling, simulating and analyzing biochemical systems with Copasi
Stefan Hoops, Virginia Bioinformatics Institute, and Sven Sahle, EML Research
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 and parameter scanning. It runs on MS Windows, Linux, OS X, and Solaris SPARC. There are not many 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. Therefore, this tutorial is primarily aimed at experimentalists who are newcomers to the computational side of systems biology. We will also critically evaluate the limitations of different simulation methods, which should be useful for some of the more experienced computational scientists as well.
Top
Tutorial 6
A Systems Biology Toolbox for MATLAB
Henning Schmidt and Mats Jirstrand, Fraunhofer-Chalmers Research Centre
In this tutorial, we will demonstrate a Systems Biology Toolbox for MATLAB, offering systems biologists an open and extensible environment in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological systems. Currently implemented functionality of the toolbox includes: model building, model import from SBML, model export functions (SBML, MATLAB m-file, textual description), simulation (both standard simulation and a more "in silico experiment" type of simulation), graphical user interfaces for model editing and visualization of results, analysis functions (steady-state, mass conservation, Jacobian+stability, stoichiometry), network identification, simple model reduction, bifurcation analysis, parameter sensitivity analysis (often called metabolic control analysis within systems biology), and localization of mechanisms leading to complex behaviors (for example, sustained oscillations and bistability). Furthermore, the toolbox contains the possibility of representing and handling experimental data, import from and to Excel sheets and text-files, use of these data for parameter estimation, network identification, and visualization. At the time of the tutorial, parameter estimation functionality will also be available in the toolbox.
The use of MATLAB as a basis allows the toolbox to be independent of the operating system. This means it can be used on Windows, Linux/Unix, and MAC OS. Secondly, it allows easy access to all data and data structures, enabling the user to do exactly what s/he wants to do, instead of being limited by the tool. Furthermore, since the toolbox is build in a modular way and does not require compilation, the user can add new functions or edit present functions without an effort.
The Systems Biology Toolbox is free software and can be downloaded at: www.fcc.chalmers.se/~henning/SBtoolbox
Tutorial Outline
- General introduction to the toolbox
- Installation
- Building models and simulation
- Equation-based modeling
- Editing of models
- Import/Export of models (SBML, textual description)
- Demonstration of the capabilities of the Systems Biology Toolbox, such as
- Steady-state analysis and stability
- Mass conservation and simple model reduction
- Bifurcation analysis
- Localization of mechanisms leading to oscillations and bistability
- Parameter sensitivity analysis (metabolic control analysis)
- In silico experiments and the representation of measurement data
- Network identification
- Parameter estimation
- How to use the toolbox's documentation
- How to write your own functions for the toolbox
- How to modify existing MATLAB models for use with the toolbox
The participants of the tutorial are invited to bring their own laptops to get some hands-on experience. All needed files will be provided on CD and the web. The toolbox requires at least MATLAB Release 14 to be installed.
Instructors
Henning Schmidt, PhD (developer of the toolbox), Fraunhofer-Chalmers Research Centre, Gothenburg, Sweden, henning@fcc.chalmers.se
Mats Jirstrand, Assoc. Prof., Fraunhofer-Chalmers Research Centre, Gothenburg, Sweden, matsj@fcc.chalmers.se
Top
Tutorial 7
GenePattern: A platform for integrative genomics
Michael Reich and Ted Liefeld, Broad Institute of MIT and Harvard
GenePattern is a software package for the analysis of data from microarrays and other genomic platforms. It provides a wide variety of analyses and powerful capabilities to combine and extend them. GenePattern is freely available and compatible with MacOS, Windows, and Linux.
In this tutorial, participants will learn how to do microarray analysis using the features of GenePattern, including:
- a comprehensive repository of clustering, prediction, preprocessing, and visualization modules for analysis of microarray data
- a pipeline environment that allows users to chain tasks together to create and share methodologies
- a task integration environment that allows rapid, code-free integration of new tools
- an intuitive graphical user interface for users at all levels of computational sophistication
- a programming environment that allows users to access GenePattern modules from the Java, MATLAB, and R programming languages
Participants are encouraged to download GenePattern from www.genepattern.org before the tutorial. Sample data will be provided, but users will get the most out of this tutorial if they bring their own data to analyze.
Top
Tutorial 8
Practical strategies for motif discovery
Ernest Fraenkel and Kenzie MacIsaac, Whitehead Institute for Biomedical Research and Massachusetts Institute of Technology
The DNA-binding specificity of proteins can be modeled by DNA sequence motifs. In this tutorial, we address what motifs are, why they are interesting, and how to find them. We discuss the challenges associated with motif discovery, and approaches that have been taken to overcome these challenges. We present an overview of some commonly used motif discovery computer programs, comparing their performance, and we discuss how the performance of standard algorithms is improved through the use of phylogenetic conservation information. We stress the importance of validating the output of these algorithms and present a method for performing such validation. We also suggest a practical strategy for merging the results of several motif discovery tools, and emphasize the utility of such strategies. Finally, we discuss how motifs, in concert with phylogenetic conservation information, can be used to scan genomes for putative regulatory sites. Throughout the tutorial we focus on practical concerns, and illustrate the important steps of motif discovery by example.
Top