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24.02.20: Important information regarding the COVID-19 outbreaks has been added. The full abstractbook is now available as a PDF download.

12.02.20: Additional places for fully booked courses have been added.

10.02.20: Detailed information and the registration for Blackboard Teaching, Computer Practicals & Workshops has been added.

07.01.20: The Programme grid has been added.

28.12.19: The Registration and abstract submission deadline has been extended to January 9th, 2020.

28.11.19: Information on the social event is updated.


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Information on Black Board Teaching, Computer Practicals and Workshops

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 Sunday, Monday and Wednesday afternoon from 16:45 to 18:45. Please chose one topic per day of the selection listed below. Please note, that all Computer Practicals and Blackboard Teachings will be held twice with the same content, except CP02 (Statistics with R) which has two different parts: Part 1 (Beginner) and Part 2 (Intermediate).

Last update: 12.02.2020: More Information on WS02 added.

Overview - Blackboard Teaching, Computer Practicals & Workshops per day

Sunday, Mar. 8, 2020
16:45 - 18:45
Monday, Mar. 9, 2020
16:45- 18:45
Wednesday, March 11, 2020
16:45- 18:45
Thursday, March 12, 2020
16:45- 17:45
CP01 Sven Sahle
CP02 Douwe Molenaar CP01 Sven Sahle WS01 Rune Linding
CP02 Douwe Molenaar CP03 Ronan Fleming CP03 Ronan Fleming WS02 Jingyi Hou
BB01 Frank Bruggemann BB01 Frank Bruggemann BB02 Maria Zimmermann  
BB03 Bas Teusink BB02 Maria Zimmermann BB03 Bas Teusink  
BB04 Franziska Matthaeus BB04 Franziska Matthaeus  BB05 Edda Klipp  
BB06 Miguel Valderrama-Gomez BB05 Edda Klipp BB06 Miguel Valderrama-Gomez  

Registration for the Blackboard Teaching and Computer Practicals

The registration for the Blackboard teaching, Computer Practicals and workshops 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 "Why genetically-identical single cells differ"

Frank Bruggemann (Vrije Universiteit Amsterdam)

The central dogma of molecular biology states that the genotype specifies the phenotype. And organism’s DNA is transcribed, the resulting message is translated, and proteins are made that specify an organism’s behaviour, its phenotype. It turn out that this view is only applicable on average — when you study cell populations. Inevitable chance events cause variation in the phenotypes of single cells that are genetically identical. How this happens, why this matters, how this can be modelled and studied experimentally, is the topic of my BlackBoard lecture. It is  supported by a handout.

BB02 "Machine learning and data analysis for systems biology"

Maria Zimmermann (EMBL Heidelberg)

Rapid advances in systems biology and systems medicine go hand in hand with the development of high-throughput -omics data acquisition and imaging technologies. Since experimental data is not a limiting factor anymore, research workflows switch from producing data to test a hypothesis to producing data to generate hundreds of new hypotheses. This switch requires new approaches to data analysis that tackle data mining, or identification of relevant information from a large dataset, and predictive modelling of the behaviour of biological systems. Machine learning has been gaining popularity in driving these analyses, since it can identify structure in data that are too big and too complex for the human brain to comprehend. Over the next decade machine learning and deep-learning, one of the most promising branches of artificial intelligence, will likely transform how we look for patterns in data and how research is conducted and applied in systems biology and human health.

This workshop will provide an overview of machine learning approaches in systems biology and discuss the specifics of the high-throughput -omics data and its preprocessing and preparation for further analysis. We will review successful cases of machine learning applications in systems biology (sequence analysis, protein structure and function prediction, biomarkers and predictive models in health and disease, image analysis, etc.), and discuss suitability of machine learning algorithms, potential pitfalls and best practices in building predictive models, selecting informative features, assessing the algorithm's performance and interpreting machine learning results.

BB03 "Constrained based modeling of metabolic networks and cellular growth"

Bas Teusink (Vrije Universiteit Amsterdam)

From a sequenced genome, the reconstruction of the metabolic network of an organism is the fastest path to increased understanding of its physiology (see ref [1] for a recent example). Such a reconstruction is an inventory of all metabolic reactions that an organism can potentially carry out – provided the bioinformatic inference of gene function was correct and the corresponding protein is expressed and active [2].

In this blackboard session, I will explain the basics of the reconstruction process, and then discuss the uses and limitations of popular constraint-based modeling techniques applied to the metabolic network, Flux Balance Analysis in particular. I will then explain the concept of resource allocation, and show how this can be incorporated into the constraint-based modeling format to improve model predictions. Depending on the interest of the audience, and time left, we can then continue the discussion to different applications or extensions of the approach.

BB04 "Modeling spatio-temporal processes"

Franziska Matthaeus (Goethe University Frankfurt)

Biological components do not only interact, they are also subject to transport processes, such as diffusion or drift. Transport processes influence reaction kinetics, and lead to spatiotemporal phenomena like traveling waves or the formation of spatial patterns. A common approach to model spatiotemporal processes are partial differential equations. We will provide a basic introduction into mathematical modeling using partial differential equation on the example of diffusion and reaction-diffusion systems. We will derive, analyse and discuss approaches to model diffusion, advection, anomalous diffusion, reaction-diffusion equations and coupled systems of PDE’s. We will introduce properties of these processes, and often observed phenomena, like traveling waves or the formation of spatial pattern. Apart from the mathematical background we will present biological examples where these processes are of importance.

BB05 "Introduction into dynamic modeling"

Edda Klipp (Humboldt Universität zu Berlin)

The basics of dynamic modeling will be explained for beginners. Then we will use a number of instructive examples to develop a feeling for typical modes of behavior. In addition, we will introduce some analysis methods for dynamic systems, including calculation and analysis of steady states, classification of steady states, oscillations and the basics of bifurcation analysis.

BB06 "Design, analysis & modeling of biological systems without prior knowledge of mechanistic parameter values"

Miguel Valderrama-Gomez (University of California)

Although we have a well-defined concept of an organism’s genotype, its phenotypes – the biological functions implemented by its underlying biochemistry – are difficult to define and predict. Mechanistic models have the advantage of being biologically realistic and the potential to rigorously define and predict biochemical phenotypes; however, they also are limited by the large number of kinetic parameters whose values are largely unknown. Thus, current approaches to determining the phenotype focus first on estimating parameter values for the underlying biochemistry, typically through a mixture of ad-hoc experimentation and computationally inefficient high-dimensional numerical search, and then exploring the phenotypic repertoire of the model by simulation. While these strategies have been used to fully characterize small systems in the pre-genomic era, a mechanistic understanding of systems, even of moderate size, derived from genotype data remains elusive.

These lectures will present an alternative post-genomic approach that first analytically determines the space of possible phenotypes for a given network architecture, which can be inferred from high-throughput data, and then predicts parameter values for their realization, predictions that can guide experimentation and further numerical analysis. This ‘phenotype-centric’ paradigm combines four innovations with the potential to accelerate our understanding of complex biological systems: (1) a rigorous mathematical definition of biochemical phenotypes, (2) a method for enumerating the phenotypic repertoire based on the biomolecular network architecture, (3) an integrated suite of computational algorithms for the efficient prediction of parameter values and analysis of the phenotypic repertoire, and (4) a user-focused environment for navigating the resulting space of phenotypes and identifying biologically relevant design principles. These innovations will facilitate deterministic and stochastic simulations that require parameter values, will accelerate both hypothesis discrimination in systems biology and the design cycle in synthetic biology, and will enable investigators to achieve predictive understanding of biomolecular phenotypes from genotype.  I will describe recent progress toward the realization of this potential and some of the remaining challenges. 

Software:Participants are encouraged to download the freely available software that will be used during the workshop:

The Design Space Toolbox V3 (DST3) is available through Docker. To access the toolbox, first download and install Docker on your machine. Refer to the webpage: https://savageaulab.wordpress.com/installing-docker/ for instructions on how to install Docker for Windows and Mac.

Once Docker is running on your computer, refer to the instructions contained in the webpage: https://savageaulab.wordpress.com/docker-image-for-the-design-space-toolbox-v3/ to pull and run the docker image for the design space toolbox.

CP01 "Advanced modeling and parameter estimation with COPASI"

Sven Sahle (University of Heidelberg)

COPASI is a widely used tool for creating, simulating and analysing kinetic models of biochemical reaction networks. In this session we will focus on parameter estimation and identifyability analysis, which is one of the key features of COPASI and at the same time a crucial technique in computational systems biology. We will have practical demonstrations and exercises about setting up and running parameter estimation tasks with the software COPASI, including setups using data from multiple experiments under different conditions. We will discuss practical issues including the choice of optimisation algorithms (local/global optimisers). We will then cover how model fitting is used to answer qualitative and quantitative biological questions. (E.g. "Can my hypothesis explain the observed experimental data?“ and "Can I resolve the quantitative properties of biochemical processes from the given data?“). We will show how parameter identifiability can be analysed using COPASI. During the week of the course we will be available to answer questions on the features of COPASI that will not be covered in this session.

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

CP02 "Statistics with R"

Douwe Molenaar (Vrije Universiteit Amsterdam)

R and Rstudio are popular tools for data wrangling, analysis and graphical display. Novel statistical analysis techniques are often published with implementations in R-packages, which makes R the perfect tool for scientific data analysis. The learning curve for R is a bit steep, but when starting with good general purpose tools and some focus, a useful level of R skills can be obtained in a relatively short period of time. This opens up the possibility to acquire active knowledge of advanced statistical techniques. The practical in this conference consists of two parts, one for beginners and one for those with intermediate level knowledge of R. A beginner should be able to follow both parts.

Beginner level: In this part we discuss the basic techniques for data analysis, project management and reporting. We'll show you techniques for proper data structuring, storage, retrieval, merging, filtering, summarizing, graphical display and document generation using Rstudio. You will practice these techniques.

Intermediate level: The data explosion in general and in particular that in biology has inspired the development of many novel statistical techniques. Some only applicable to a particular type of data, like RNA-seq, and others more generally applicable. In this tutorial we will show how some of the generally applicable machine learning techniques can be applied using R.

In both sessions we will discuss resources for further development of your skills.

Software: to be able to actively participate, please install R (https://www.r-project.org/) and Rstudio-Desktop (https://rstudio.com/). After installing R, also install the tidyverse packages by executing the command


in the R-console.

CP03 "The COBRA Toolbox - An introduction to the constraint-based reconstruction and analysis toolbox v3.0"

Ronan Fleming (University of Leiden)

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. The COBRA Toolbox version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This practical provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods. (https://opencobra.github.io/cobratoolbox/)

Programming: Anyone new to programming, or MATLAB, already start with, e.g., "Introduction to MATLAB® for Biologists" https://link.springer.com/book/10.1007/978-3-030-21337-4 before the course starts.

Matlab: Check if your institution has a Matlab license https://uk.mathworks.com/academia/tah-support-program/eligibility.html If not, here is the link to the academic Matlab licenses, https://uk.mathworks.com/pricing-licensing.html?prodcode=ML&intendeduse=student, or failing that, they could start with a free month trial so they still have a working Matlab version for the whole course.

COBRA Toolbox: Each participant must have the COBRA toolbox installed before the course starts: https://opencobra.github.io/cobratoolbox/stable/installation.html The open-source LP solver GLPK comes packaged with the COBRA Toolbox and will suffice, but, e.g., Gurobi also provide free academic licenses https://www.gurobi.com/free-trial/, but that is a separate installation.

Test your installation: Check that you can successfully initialize the COBRA toolbox and verify that you have a sufficient installation (https://opencobra.github.io/cobratoolbox/stable/installation.html ), for the COBRA practicals using only the open-source GLPK and PDCO solvers that come packaged within the COBRA toolbox.

WS01 "Career Paths in Systems Biology"

Rune Linding (Humboldt Universität zu Berlin)

In this thought-provoking session, we will discuss different career options/opportunities and obstacles one may encounter as a systems biologist. I will attempt to briefly give an introductory overview and then open the floor for dialog, discussion and questions.

  1. The basic requirements. Academia: 1. PhD 2. Postdoc abroad. Industry: 1. PhD 2. Postdoc/Researcher. Hybrid: Combined PhD industry/academic environments. Entrepreneur: PhD in Bio/Life/AI/Green/Engineering..finding your own path.
  2. Mobility, change of environment and going/living abroad.
  3. Systems biology is inherently multi-, inter- and intra-disciplinary what does this mean for your career/education?
  4. Obstacles: 1. Too many Junior Group Leaders (GLs) vs jobs ? 2. Discrepancy/Conflicts between interest of GL and PhD & Postdocs. 3. Merge the goals is best path, how to?
  5. Being PI in a university vs independent Research Institute (RI) vs industry.
  6. As new-PI how to manage: 1. interests of senior university/company/institute management. 2. Interests of funding agencies. 3. Interests of stakeholders versus academic/research freedom.
  7. Family life vs work (work-life balance). Work hard play hard? How to compete on global job market ?

WS02 "How to write a good abstract"

Jingyi Hou (EMBO Press, Heidelberg)

Participants are required to upload an abstract of their own for analysis and discussion during the workshop. You can upload an abstract for the workshop in PDF format on the course selection page. Each participant is expected to write an abstract that summarizes their research ahead of the time. On the day of the workshop, the participants need to bring their abstract printouts to the course. After a brief presentation that is given by the instructor, the participants will be split into small groups of 3-5 people. The participants will circulate their abstracts within the small groups and they will be asked to edit each other’s abstracts, following the abstract writing guidelines provided in the instructors’ presentation. Finally, 1-2 pre-selected “exemplary” abstracts will be displayed in the presentation (upon pre-consultation) for further discussion with the entire group.


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