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Cellular systems biology

Plenary lecture 1

Saturday, 10 September, 18:30 - 20:00

Room: Musensaal (Saal 2.0)

Jan Ellenberg DE

EMBL, Heidelberg

Systems biology of human cell division using light microscopy
Human cells contain over 20 000 different genes and essential functions of life, such as cell division, require several hundreds of these to be expressed. Using systematic gene silencing by RNA interference and subsequent phenotyping by high throughput microscopy we have defined close to 600 proteins that are needed for a human cell to divide normally. These proteins have to be precisely orchestrated in space and time to drive the faithful segregation of the genome and the cleavage of one cell into two. Understanding how this dynamic network of mitotic proteins drives one of the most dramatic morphological and functional changes cells can undergo, will require to map their interactions in space and time. To address this challenge, we have established an integrated systems biology workflow, consisting of genome editing, imaging and computational modeling to map the mitotic network in live dividing human cells. After homozygous genome editing to tag all endogenous copies of a given mitotic protein fluorescently, we image its absolute abundance and subcellular distribution by calibrated 4D imaging relative to spatio-termporal landmarks of cell division. Computational image analysis and modeling then allows us to align the dynamic cell morphology in space and time to obtain a standard mitotic cell into which we can integrate the data of all proteins imaged. Using image parameterization and machine learning, we can measure the dynamic subcellular localization of mitotic proteins as well as fluxes between subcellular compartments and structures. This allows us to predict protein clusters, the chronological order of their formation and disassembly and the abundance of their subunits. To validate the predicted network behavior, we then perform high-throughput fluorescence cross correlation spectroscopy (HT-FCCS) of fluorescently tagged pairs of binding partners during division. Our integrated computational and experimental method is generic and makes many dynamic cellular processes amenable to dynamic protein network analysis.”

Jan Ellenberg is Head of the Cell Biology & Biophysics unit and senior scientist at the European Molecular Biology Laboratory (EMBL) in Heidelberg, Germany. He graduated from the University of Hamburg and received his diploma in Biology in 1994. In 1998 he obtained his PhD in Biochemistry from the Free University of Berlin, during which time he trained primarily at the National Institute of Health (NIH) in the laboratory of Jennifer Lippincott‐Schwartz. In 1999 he started his own group at EMBL in the Gene Expression and Cell Biology & Biophysics unit. He subsequently became coordinator of the Gene Expression unit in 2006 and now, since 2010, Head of the Cell Biology & Biophysics unit. Jan’s research interests are wide‐spread, but the overall goal of his group is to systematically elucidate the mechanisms underlying cell division and nuclear organisation. He develops and applies a broad‐range of fluorescence‐based, cutting‐edge imaging techniques including automated high‐throughput microscopy and spectroscopy, super‐resolution and light‐ sheet microscopy. Jan is also the EMBL delegate in the Euro‐BioImaging, a pan‐European research infrastructure project for imaging technologies.


Matthias Mann DE

MPI of Biochemistry, Munich

Technologies and applications of high-resolution mass spectrometry in biology and medicine

Developments over the last few years have steadily increased the scope of MS-based studies in molecular biology but important challenges remain, chief among them the lack of comprehensiveness compared to oligonucleotide based systems. However, this limitation is now falling away, as I will show with deep proteomic analysis of human cancer cell lines. More than 10,000 different proteins can now be identified in such systems, in a relatively short time, shedding new light on similarities and differences to each other and to in vivo cells. Developments in sample preparation make these capabilities available in clinically relevant material as well, such as formalin-fixed, paraffin embedded samples. This included streamlined and highly efficient sample preparation, analysis with very high sequencing speed using modern mass spectrometers and bioinformatic analysis using the MaxQuant and Perseus platforms. Efforts in our group have focused on ‘single shot’ analysis and we demonstrate very high coverage in this mode (Mann et al., Mol. Cell, 2013). We have also extended this concept to the analysis of cellular interactomes (Hein et al. Cell 2015) and transcription factor complexes. For post-translational modifications such as phosphorylation, the ‘EasyPhos’ method now allows acquiring large numbers of phosphoproteomes, for instance for the analysis of in vivo signaling (Humphrey et al. Nat. Biotech, 2015). Together such developments make proteomics increasingly relevant to translational research as I will discuss in this talk using examples such as the recent discovery of an in-vivo substrate of the Parkinson’s kinase LRRK2.

Matthias Mann studied physics and mathematics at Göttingen University in Germany and obtained his PhD in chemical engineering at Yale University. Here he was decisively involved in the development of electrospray ionization, which has become a key technology of the life sciences. As a post-doctoral fellow and later as a professor for bioinformatics at the University of Southern Denmark in Odense, he developed, amongst others techniques, the first bioinformatic search algorithms for peptide fragmentation data and SILAC, a new method of quantitative proteomics and a breakthrough in the mapping of protein interactions. In 2005, he took up a director position at the Max-Planck Institute of Biochemistry in Munich. Here his group continues to address a wide range of biological questions using proteomic technology, as well as to develop this technology. The group is also heavily involved in providing proteomic methods and tools to the community. Most importantly in this regard, they have provided the MaxQuant suite of computational proteomics algorithms; this software promises to significantly advance the state of the field. More recently his group used the SILAC technology in conjunction with MaxQuant to described the first comprehensive identification and quantification of a proteome. In 2009 he was additionally appointed director of the proteomics department of the Novo Nordisk Foundation Center for Protein Research in Copenhagen. He has authored and co-authored more than 580 publications with a total citation count of more than 100,000, making him one of the most highly cited researchers worldwide, has been elected to membership of the European Molecular Biology Organization, Royal Danish Academy of Arts and Sciences and the Leopoldina German National Academy of Sciences as well as to a visiting professorship at Harvard Medical School. He has received two honorary degrees from Utrecht University and the University of Dundee, respectively. In 2012 he was awarded the Leibniz Prize from the German Research Foundation, the Ernst Schering Prize, the Louis-Jeantet Foundation Prize for Medicine and the Körber European Science Prize. In 2015 he has been awarded the Barry L. Karger Medal in Bioanalytical Chemistry and the Theodor Bücher Lecture and Medal.


Søren Brunak DK

University of Copenhagen, Denmark

Disease trajectories, gene pleiotropy and protein multi-functionality

Compared to the initial expectation human beings are gene-poor organisms. Many genes and pathways are likely to play a role in more than one disease, and numerous examples of gene pleiotropy and protein multi-functionality presumably await discovery.  This situation contributes to the recent interest in clinical healthcare sector data and their accounts of fine-grained multi-morbidities. Patient record data remain a rather unexplored, but potentially rich data source for discovering correlations between diseases, drugs and genetic information in individual patients. A fundamental question in establishing biomarker-phenotype relationships is the basic definition of phenotypic categories. As an alternative to the conventional case-control, single disease model the talk will describe attempts to create phenotypic categories and patient stratification based on longitudinal data covering long periods of time. We carry out temporal analysis of clinical data in a more life-course oriented fashion. We use data covering 6-7 million patients from Denmark collected over a 20 year period and use them to “condense” millions of individual trajectories into a smaller set of recurrent ones. This set of trajectories can be interpreted as re-defined phenotypes representing a temporal diseaseome as opposed to a static one computed from non-directional comorbidities only. We present examples, including one from the area of inflammatory disease area where five diseases seem to co-occur primarily due to shared loci rather than follow-on disease etiologies.  This type of work can potentially gain importance in projects involving population-wide genome sequencing in the future.

Søren Brunak, Ph.D., is professor of Disease Systems Biology at the University of Copenhagen and professor of Bioinformatics at the Technical University of Denmark. He is Research Director at the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen Medical School. He leads a research effort where molecular level systems biology data are combined with the analysis of phenotypic data from the healthcare sector, such as electronic patient records, registry information and biobank questionnaires. A major aim is to understand the network basis for comorbidities and discriminate between treatment related disease correlations and other comorbidities, thereby stratifying patients not only from their genotype, but also phenotypically based on the clinical descriptions in their medical records. Prof. Brunak started work within bioinformatics in the mid-1980ies, and was in 1993 the founding Director of the Center for Biological Sequence Analysis at DTU, which was formed as a multi-disciplinary research group of molecular biologists, biochemists, medical doctors, physicists, and computer scientists. The center offers a wide range of services at its web site,, including bioinformatics tools developed over the past 25 years.