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Information on Black Board Teaching and Computer Practicals

Last Update: Feb. 24: Olga Krebs added to CP-04

Please note that you will be able to follow only 2 out of the 12 offered Blackboard (BB) and Computer Practicals (CP), one each from the parallel courses in the early (A) and late (B) sessions. Each course will be made up of 3 consecutive parts taught on Monday, Tuesday and Thursday respectively.

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

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).

Session A (Mon, Tue & Thu 16:30-17:30)

BB-01 Origins of stochasticity in single cells: theory

Frank Bruggeman

Spontaneous fluctuations in the activities of molecular processes cause heterogeneity in the molecular composition of cells with the same genome and growth history. Cell-to-cell variability has been observed in mRNA and protein levels, in the timing of molecular processes, and in cellular growth rates. I will emphasise the theoretical aspects of this stochasticity in cell biology. Prof Heinemann will focus on the experimental aspects.

The causes of molecular noise involve molecules occurring at low numbers per cell, such as transcription factors or mRNAs, that tend to show large, spontaneous deviations relative to their mean number within the cell population. These deviations (fluctuations) can be caused by cell division, or transient imbalances between molecular synthesis and degradation rates that occur spontaneously through thermal noise ('intrinsic noise') or due to fluctuations in the number of regulators ('extrinsic' noise). Extrinsic noise indicates that fluctuations can propagate through the entire molecular network of a cell. I will explain the basic underlying theory that we recently developed. This theory indicates how biochemical reactions, network circuitry, and cell growth/division jointly give rise to cell stochasticity. This theory can be used to disentangle different stochasticity sources.

Throughout the course, I will emphasize the cell-biological relevance of cellular stochasticity as it underlies heterogeneity in differentiation decisions, stress response magnitudes, and the survival prospects of cells after drug exposure.

I will supply handouts and a number of simple stochastic models for you to play with during the meeting using our recently published stochastic simulation software tool (Stochpy; http://stochpy.sourceforge.net).

BB-02 Logic models of signalling networks and training to phosphoproteomic data

Julio Saez Rodriguez

In my series of lectures, I will describe different approaches to model signal transduction networks. I will focus on 'causal' based methods, in particular logic-based formalisms, that do not get into biochemical details. These scale up better to large networks and require less knowledge of the system, at the price of providing less detailed insight. I will discussed data-driven vs. knowledge driven approaches, and different variants. I will then discuss how these models can be trained to expeimental data. I will include a summary of experimental approaches to look at singaling networks, with their pros and cons. Finally, I will discuss which type of insight can be gained with these models, in particular in the context of understanding the deregulation of signal transduction in human disease, and investigating the effect of therapies to treat these diseases.

BB-03 Experimental tools 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-cytometrie using fluoresence, 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.

CP-01 CellDesigner: A process diagram editor for gene-regulatory and biochemical networks

Akira Funahashi

CellDesigner is software for modeling and simulation of biochemical and gene regulatory networks. While CellDesigner itself is a sophisticated structured diagram editor, it also enables users to directly integrate various tools, such as built-in SBML ODE Solver, COPASI and SBW-powered simulation/analysis modules. CellDesigner runs on various platforms such as Windows, MacOS and Linux, and is freely available from http://www.celldesigner.org/ This course will explain how CellDesigner can be used from modeling perspectives. The first topic will feature network modeling using CellDesigner, and will show how to build a model from scratch, and examine simulations. This topic also includes an explanation of 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. The second session will demonstrate 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 it is possible to "import" an existing model from several third-party databases (e.g. BioModels.net, PANTHER database). This will be particularly useful for users who have obtained a published model, but do not have enough experience in building a mathematical model by hand.

Software: Please download and preinstall CellDesigner 4.3 for this practical.

CP-02 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.

Level: Basic/Intermediate

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

CP-03 Creating a transparent model workflow in JWS Online; model construction/validation/annotation and publication

Jacky Snoep

Not many mathematical models are published together with full reference to the experimental data that were used for model construction and model validation. This leads to lack of transparency in the modeling workflow, and usually makes it impossible to reproduce the process. We present a workflow for model construction and validation, with access to the complete data set, leading to annotated models and data sets in standard formats.

In the computer tutorial we will follow this workflow using as a case study the gluconeogenic pathway in the thermophile Sulfolobus solfataricus. Kinetic data for each of the enzymes will be used for model construction. Subsequent model validation will be made on in vitro reconstituted systems for the pathway.

Finally we will annotate the model and data sets, save them in standard formats and make the model and data available via on-line tools.

During the tutorial you will get a good understanding of: the link between experimental data and mathematical model; difference between model construction and model validation; model and data annotation (MIRIAM and RightField) and the concept of a reproducible model workflow (explicit data and model links in e.g. ISA structure).

You will use the following software tools: JWS Online simulator, JWS OneStop, JWS Plug'nPlay, RightField, SEEK, semanticSBML, and work with MIRIAM, SBML, and SBGN standard formats.

Software: only wireless Internet connection required.

 

Session B (Mon, Tue & Thu 17:45-18:45)

BB-04 Genotype to phenotype: Phenotypic deconstruction of complex biological systems

Mike Savageau

Achieving predictive understanding of complex nonlinear systems, such as those manifested at various levels of biological organization, represents an enormous challenge.  The task could be facilitated if such systems could be generically decomposed into a series of tractable subsystems and the results of their analysis reassembled to provide insight into the original system.  These lectures will describe an approach in which the subsystems are integrated into a system design space that allows qualitatively distinct phenotypes of the complex system to be rigorously defined and counted, their relative fitness to be analyzed and compared, their global tolerance to be measured, and their biological design principles to be identified. I will illustrate the approach in the context of the ‘genotype-phenotype’ question for a couple of well-studied systems. I will show how this new approach complements traditional methods of nonlinear analysis, and I will point out a number of mathematical issues that need to be further explored and extended.  The three lectures will be organized roughly as follows:

Day 1: Overall Goal, Modeling Strategies, Mathematical Representations
Day 2: Methods of Analysis, System Design Space, Global Tolerances
Day 3: Mathematically Controlled Comparisons, Evolution, Design Principles

BB-05 Efficient Communication of Interdisciplinary Research

Olaf Wolkenhauer

Science is not just about facts but foremost characterised by the communication of facts; Results do not speak for themselves but require an argument. Effective communication is therefore very important for a successful career and yet it is something we are usually not prepared for and which we have to continue practicing throughout our career.

In this seminar you will learn to get your message across in scientific publications, conference posters, for grant proposals, on your web-page and through oral presentations. The technique taught in this course is simple and effective at the same time, exploiting the patttern that are used by successful communicators.

Using a range of examples, you will learn to analyse scientific publications for their composition, the structure of an argument, and the use of English. We will also pay attention to the specific challenges non-native speakers of the English language face. 

BB-06 Modeling signaling networks

Edda Klipp

Cellular signaling pathways convey the information about external cues or internal changes to the points of cellular decision making such as transcription factors regulating gene expression or post-translational protein modifications affecting metabolic fluxes.

Wiring and dynamics of signaling pathways has often been described with sets of ordinary differential equations. We will introduce into this concept for different types of signaling pathways. We will analyze how structural motifs and the choice of parameters influence the dynamics of signaling pathways. Different measures for the behavior of signaling pathways in isolation or as part of a network will be introduced and discussed. Finally, we take into account that pathways are embedded in the cellular environment and ask: what turns them on, what turns them off?

BB-07 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. These modelling techniques all aim to predict or interpret flux distributions through the metabolic network. The most important ones are flux balance and flux variability analysis.

In the last session we will discuss applications of stoichiometric analysis, thermodynamic constraints and sensitivity analysis.

CP-04 Data mangement in practice

Wolfgang Müller, Olga Krebs

Sharing well-annotated research data helps the systems biology process.

This practical is about how to easily structure your experimental data such that it becomes well annotated, standard compliant, reproducible, and re-usable (also for others). While much big data travels from machine to machine without human intervention, we will focus on exchange of small data describing experiments which is (to our experience) mostly handled via MS Excel and similar tools. First we give an outline talk about (potentially) deadly sins to avoid when describing data for later use with computers. Then we will build (together) an Excel templates for use with an experimental data of different types. Finally we will introduce you to off-the-shelf tools applied to realistic experimental data to improve its quality before uploading it to the data management system SEEK.

We target experimental biologists, no prior informatics training is assumed, own data can be used in this course

Tools to be used:

  1. http://www.seek4science.org/
  2. https://www.sysmo-db.org/rightfield
  3. http://openrefine.org/

Software: Please download and preinstall Rightfield and OpenRefine for this practical.

CP-05 Constraint based modelling with FAME

Olivier Brett

Constraint based modeling is a widely accepted methodology used to analyze and study biological networks on both a small and whole organism (genome) scale. Typically, these models are underdetermined and constraint based methods (e.g. linear optimization) are used to optimize specific model properties.

Perhaps the most well known and widely used analysis method, Flux Balance Analysis (FBA) makes use of Genome Scale Reconstructions (GSR's) to optimize a target flux (e.g. maximizing biomass or product production) while other fluxes are bound to simulate a selected growth environment or metabolic state.

In this practical we will use the web-based constraint based modelling system FAME (http://f-a-m-e.org) to manipulate, analyze and visualize FBA analyses and introduce the constraint based modelling platform, used by FAME, CBMPy (http://cbmpy.sourceforge.net). In addition, we will discuss the latest format used to encode and annotate constraint based models - the SBML Flux Balance Constraints Package.

Software: only wireless Internet connection required.