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Information on Black Board Teaching and Computer Practicals

Please note that you will be able to visit one Blackboard Teaching Session (BB) or Computer Practical (CP) per day, which will take place on Monday, Wednesday and Thursday afternoon from 16:45 to 18:45. Please chose one topic per day of the selection listed below. All in all you have 10 different topics to choose from.

Overview - Blackboard Teaching and Computer Practicals per day

Sunday, Feb. 28, 2016
15:30- 17:30
Monday, Feb. 29, 2016
16:45- 18:45
Wednesday, March 2, 2016
16:45- 18:45
Thursday, March 3, 2016
16:45- 18:45
CP-02 Akira Funahashi CP-01 U.Kummer/S. Sahle/
P. Mendes
CP-01 U.Kummer/S. Sahle/P. Mendes CP-03 C. Kreutz/ B. Steiert
  CP-02 Akira Funahashi CP-03 C. Kreutz/ B. Steiert BB-01 Bas Teusink
  BB-01 Bas Teusink BB-02 Matthias Heinemann BB-02 Matthias Heinemann
  BB-03 Frank Bruggemann BB-03 Frank Bruggemann BB-04 Mike Savageau
  BB-04 Mike Savageau BB-05 Elad Noor BB-05 Elad Noor
  BB-06 Marcus Krantz BB-06 Marcus Krantz BB-07 Debora Marks

The registration for the Blackboard and Computer Practicals can be found in the User Profile (login required). The number of places for these courses are limited and will be assigned on a first come, first served basis.

For computer practicals, students are required to bring their own laptops with any required software preinstalled (preinstallation requirements listed at the end of each course).

BB-01 Genome-scale metabolic models, their construction and analysis

Bas Teusink

The post-genomics revolution has confronted mainstream biologists with the need of models for data integration, analysis, and - ultimately - understanding of the complexity of biological systems. Hence, if we want to make optimal use of functional genomics data, we need models of genome scale. Such genome-scale metabolic models are based on bioinformatics, comparison with other genome-scale models, literature, and experimental evidence for the activity of specific pathways. These models are now an important tool in the lab for data integration and visualisation, but also for predictions of metabolic phenotypes.

In this blackboard course we will first discuss the metabolic reconstruction process, i.e. issues related to the construction of a genome-scale model. Then we will explain and discuss several so-called constraint-based modelling techniques applied to such models, with a focus on their usefulness and limitations.

BB-02 Experimental methods for single cell analyses

Matthias Heinemann

For various reasons, cell populations can be heterogeneous. Multiple experimental tools are available to assess cellular phenotypes on the single cell level. In this black board sessions, I will cover different experimental techniques for single cell analyses, including the classical single cell analyses techniques, i.e. microscopy and flow-cytometry using fluorescence, but also more recent techniques such a microfluidics and mass-spec based methods of single cell omics analyses. If time permits, we will also have a look at different fluorescence-based sensors.

BB-03 Origins of stochasticity in single cells: theory and experimental illustrations

Frank Bruggemann

In this lecture series, I will discuss some of the amazing features of single prokaryotic and eukaryotic cells. How isogenic cells are different from one and the other. That the molecular content of daughter cells can deviate a lot from that of their mothers. How growth of single cells proceeds from cell birth to division. That much of this cell-to-cell variability arises from stochastic aspects of transcription, gene-regulatory circuits and cell division. I will discuss those phenomena by discussing several of the classical experiments that were done in the last fifteen years. I will illustrate how theory and small mathematical models are being used to quantify cell-to-cell variability and to understand principles of the stochastic behaviour of molecular circuits in living cells.

BB-04 Characterizing the genotype-phenotype map of biochemical systems using a new phenotype-centric modeling strategy

Michael Savageau

Although we now have a generic concept of 'genotype' provided by the detailed DNA sequence, there is no corresponding generic concept of 'phenotype'. Without a generic concept of phenotype there can be no rigorous framework for a deep understanding of the complex biochemical systems that link genotype to phenotype. I will contrast the traditional 'parameter-centric' modeling strategy with a new 'phenotype-centric' modeling strategy that inverts many of the key steps in the traditional approach. This new strategy provides a generic definition of phenotype, automatically identifies tractable nonlinear subsystems for their characterization, and displays the results graphically in a system design space. The qualitatively distinct phenotypes of a complex system can then be rigorously defined and counted, their fitness analyzed and compared, their global tolerances measured, and their biological design principles revealed. The boundaries between phenotypes in this design space are defined by the system itself. Thus, they provide an objective scale that distinguishes small from large mutant effects and thereby a resolution of the apparent robustness/evolvability paradox. A few simple applications will be used to illustrate how this approach elucidates the relationship between genotypically determined parameters, environmentally determined variables, and the qualitatively distinct phenotypes of biochemical systems. This approach provides quantitative understanding of evolution for a number of natural systems and a global perspective on the phenotypic repertoire that facilitates the directed evolution of synthetic gene circuits

BB-05 Efficiency and cost of metabolic enzymes

Elad Noor

Metabolic enzymes comprise many of the most highly expressed genes in all cell types, and altogether occupy roughly 20% to 50% of the proteome. Therefore, pathway efficiency, in terms of enzyme requirement, plays a major role both in evolution. We take advantage of this fact by assuming that almost every enzyme is highly efficient in at least one growth condition. Combining proteomic and flux data, a simple calculation can yield good predictions for in-vivo kcat values. Furthermore, the expression of enzymes should be optimized to minimize their total cost, while matching a given required flux. We develop a scalable and extensive framework for enzyme cost optimization and prove that enzymatic metabolite cost minimization (eMCM) is a convex optimization problem. Our results are facilitated by factoring the enzyme cost function into four terms representing maximal capacity, thermodynamic driving forces, enzyme saturation, and allosteric regulation. We use recently measured metabolite and protein levels in E. coli's central metabolism to validate eMCM. Finally, eMCM provides a direct and physically realistic way to combine considerations of enzyme kinetics and protein abundance with constraint-based metabolic models.

BB-06 Building and validating (large) mechanistic models of signal transduction

Marcus Krantz

Large scale reconstruction of metabolic networks is a well-established process, and a substantial number of genome wide metabolic models are available. The picture is very different for signal transduction networks. While we have a few large scale curation efforts, they are all more limited in scope and typically do no support validation via simulation. The reason for this discrepancy is the different nature of these two network types: Metabolic networks transfer mass between different pools of metabolites, where metabolites do little more than change in amount. In contrast, signalling networks transfer information between components, often without significant change in the amount of these. Instead, information is transferred by changing the state of the components, most often by tripping switches such as the phosphorylation status of specific residues. This has two major effects on how we work with large scale networks: First, mass-transfer based validation and simulation methods do not work. Second, the network becomes much more complex when we account for states of components, instead of only components (or metabolites). In this session, I will introduce you to the methods we developed to meet these challenges. This includes the reaction-contingency (rxncon) language for network definition, a bipartite Boolean modelling formalism that can be used to determine information transfer in the network, and a concept of gap-finding and gap-filling based on comparison to information paths known from genetic studies. The key feature of the rxncon language is the distinction between possible events (reactions; rxn) and the regulatory influence of previous events (contingencies; con). This results in a bipartite description, in which reactions produce or consume states, and states influence reactions via contingencies. Such a network does not suffer from the combinatorial complexity, as only the complexity known from empirical experiments are taken into account, and the information transfer can be visualised and simulated. Hence, it is suitable for large scale reconstruction of signal transduction networks.

BB-07 Statistical inference on genomic sequences to solve challenges in systems biology

Debora Marks

How will we analyze the super abundance of genetic information over the next 10 years to accelerate basic biological understanding and clinical discoveries? One recent example is the use of co-evolutionary constraints derived from genomic sequences that has been successful in predicting protein and RNA phenotypes. The substantial progress on this problem became possible because of the explosive growth in available sequences and the application of statistical methods that use a global probability model. In addition to three-dimensional structure of single proteins, RNA and DNA, this statistical analysis of evolutionary constraints can identify functional residues involved in ligand binding, biomolecule-interactions, alternative ensembles of conformations and even "invisible" tertiary states of disordered proteins, see evfold.org.
We have now started to develop this co-evolution approach to the quantitative effect of genetic variation on fitness, and compared predictions to hundreds of thousands of genetic variants measured by high throughput screening experiments.
In this workshop I will present both the statistical methodology, together with results that show the power of these inference methods as applied to accurate prediction of 3D structures and mutation effects. As importantly, I will discuss open challenges in those areas, in method development and ideas for applications in causes of complex disease, protein and RNA design, and drug mechanism.

Work presented here is from members of the Marks lab @Harvard Medical School and in collaboration with Chris Sander

CP-01 Introduction to modeling (using COPASI)

Ursula Kummer, Sven Sahle, Pedro Mendes

This computer practical introduces basic modeling approaches as setting up models, simulating them, parameter fiitting and sensitivity analysis. The course will make use of the commonly used software COPASI to learn the topcis in a hand-on and applied way.

Software: Please download and preinstall Copasi (latest stable version) for this practical.

CP-02 CellDesigner 4.4: A process diagram editor for gene-regulatory and biochemical networks

Akira Funahashi

CellDesigner is a software package 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, COPASI, Simulation Core Libray and SBW-powered simulation/analysis modules. CellDesigner runs on various platforms such as Windows, MacOS X and Linux, and is freely available from our website at http://celldesigner.org/. In this course, I will explain how CellDesigner can be used from modeling 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. This topic also includes an explanation on how we build a biochemical network as a "map" which includes links to several existing databases, and how we build a mathematical model by the aspect of process-diagram based modeling. Once a model is described with appropriate mathematical equations and parameters, running a simulation on CellDesigner is quite straight forward. I will also explain how to easily tweak your model from CellDesigner's user-interface and observe some changes in the dynamics. Not just building a model from scratch, this course also introduces how we can "import" an existing model from several third-party databases (ex. BioModels.net, PANTHER database). This might be useful for users who actually read a paper and got interested in the model, but does not have enough experience on building a mathematical model by hand. This tutorial will cover both mathematical modeling and graphical modeling (create a model as a 'map') topics, and mainly focuses on mathematical modeling. Bringing your notebook PC with CellDesigner installed is highly recommended.

Software: Please download and preinstall CellDesigner 4.4 for this practical.

CP-03 Statistics and numerics for dynamical modeling

Bernhard Steiert, Clemens Kreutz

Motivation: A successful mathematical description of cell biological processes based on experimental data requires efficient and reliable numerical methods for parameter estimation as well as a suitable statistical methodology to reconstruct the underlying biochemical reaction networks.
Goal: In this computer practical, statistical and numerical aspects for dynamical modeling in Systems Biology are discussed and the computational implementation is demonstrated. One major focus is the assessment of uncertainties of both, parameters and model predictions, which is efficiently and intuitively judged by the profile likelihood.
In summary, the following aspects are discussed:
- appropriate numerical algorithms for parameter estimation
- judging the quality of experimental data
- model discrimination by the likelihood ratio tests
- identifiability analysis and confidence intervals for estimated parameters
- observability analysis and confidence intervals for model predictions
- selection of informative new experimental conditions
We present theoretical aspects of the statistical and numerical methodology but we also show how analyses are performed using a comprehensive software package for quantitative dynamic modeling, the D2D software. Real experimental data is analyzed to demonstrate numerical and statistical methods. The illustrated methodology has been awarded three times as "Best Performer" in parameter estimation challenges within the Dialogue for Reverse Engineering Assessment and Methods (DREAM) competitions.
Intended audience: The computer practical is given for scientists with a theoretical background (bioinformatics, physics, theoretical biology, statistics), and for biologists interested in statistics and numerics of modeling.
Prerequisites:  We offer the participants to try the D2D software within the computer practical. Therefore, participants may bring their own notebooks with MATLAB installed. However, if no appropriate license is available the audience can still easily follow the contents as all modeling steps are presented live on screen.

Speakers: The computer practical is given by Bernhard Steiert and Clemens Kreutz. They have long-term experience in data-based modeling, and published several new statistical approaches for modeling in Systems Biology, as well as applications with experimental collaborators.

Software: (Optional) Please download and preinstall MATLAB and the D2D software for this practical.