

Information on Black Board Teaching and Computer PracticalsPlease 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
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). BB01 Genomescale metabolic models, their construction and analysisBas TeusinkThe postgenomics 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 genomescale metabolic models are based on bioinformatics, comparison with other genomescale 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 genomescale model. Then we will explain and discuss several socalled constraintbased modelling techniques applied to such models, with a focus on their usefulness and limitations.BB02 Experimental methods for single cell analysesMatthias HeinemannFor 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 flowcytometry using fluorescence, but also more recent techniques such a microfluidics and massspec based methods of single cell omics analyses. If time permits, we will also have a look at different fluorescencebased sensors. BB03 Origins of stochasticity in single cells: theory and experimental illustrationsFrank BruggemannIn 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 celltocell variability arises from stochastic aspects of transcription, generegulatory 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 celltocell variability and to understand principles of the stochastic behaviour of molecular circuits in living cells. BB04 Characterizing the genotypephenotype map of biochemical systems using a new phenotypecentric modeling strategyMichael SavageauAlthough 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 'parametercentric' modeling strategy with a new 'phenotypecentric' 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 BB05 Efficiency and cost of metabolic enzymesElad NoorMetabolic 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 invivo 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 constraintbased metabolic models. BB06 Building and validating (large) mechanistic models of signal transductionMarcus KrantzLarge scale reconstruction of metabolic networks is a wellestablished 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, masstransfer 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 reactioncontingency (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 gapfinding and gapfilling 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. BB07 Statistical inference on genomic sequences to solve challenges in systems biologyDebora MarksHow 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 coevolutionary 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 threedimensional structure of single proteins, RNA and DNA, this statistical analysis of evolutionary constraints can identify functional residues involved in ligand binding, biomoleculeinteractions, alternative ensembles of conformations and even "invisible" tertiary states of disordered proteins, see evfold.org. CP01 Introduction to modeling (using COPASI)Ursula Kummer, Sven Sahle, Pedro MendesThis 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 handon and applied way. Software: Please download and preinstall Copasi (latest stable version) for this practical. CP02 CellDesigner 4.4: A process diagram editor for generegulatory and biochemical networksAkira FunahashiCellDesigner 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 builtin SBML ODE Solver, COPASI, Simulation Core Libray and SBWpowered 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 processdiagram 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 userinterface 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 thirdparty 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. CP03 Statistics and numerics for dynamical modelingBernhard Steiert, Clemens KreutzMotivation: 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. Software: (Optional) Please download and preinstall MATLAB and the D2D software for this practical. 
