Session 1: Physical Models and Emergent Properties in Biological Systems
Beat Fierz
Probing dynamic chromatin organization on the single-molecule scale
Beat Fierz was appointed Tenure Track Assistant Professor for the newly created Chaire Fondation Sandoz in Biophysical Chemistry of Macromolecules.
Prof. Fierz studied Molecular Biology at the Biozentrum of the University of Basel, where he also otained a Diploma in Biophysical Chemistry under the supervision of Prof. Thomas Kiefhaber in 2002.
Subsequently, he performed his PhD studies (2002-2006) in Prof. Kiefhaber’s lab at the Biozentrum, where he investigated the dynamics of synthetic polypeptides and protein secondary structure elements using ultrafast time-resolved spectroscopy.
In 2007 he joined the laboratory of Prof. Tom W Muir at the Rockefeller University, New York, and later Princeton University, New Jersey. There, he studied the folding behavior of chromatin fibers as well as the stability of nucleosome particles, depending on histone post-translational modifications.
The research of Prof. Fierz focuses on the study of the structure, dynamics and function of chromatin and related multi-protein complexes in vitro and in cells. These investigations require an interdisciplinary approach at the interface of chemistry, biology and biophysics.
Attila Becskei
The control of stochastic gene choice during neuronal differentiation
Attila Becskei is Professor of Synthetic Microbiology at the Biozentrum, University of Basel since 2011.
Prof. Becskei studied at the University of Szeged, Hungary, where he became a Doctor of Medicine with a minor in Physical Chemistry. He then moved to the European Molecular Biology Laboratory in Heidelberg, where he obtained his PhD in 2002. His doctoral work explored the dynamic behavior of simple gene networks by a mathematical-experimental approach. From 2002 to 2005 he was a Postdoctoral Assciate at the MIT in Cambridge, MA. He then became Assistant Professor at the Institute of Molecular Life Sciences at the University of Zurich.
Prof. Becskei’s lab combines experimental methods with mathematical modeling to understand cell differentiation, by investigating how genes in yeast and mammalian cells are wired up, and which mechanisms control these gene networks.
Niko Beerenwinkel
Learning tumor phylogenies from single-cell data
Niko Beerenwinkel has been Associate Professor of Computational Biology at ETH Zurich since April 2013.
Niko Beerenwinkel studied mathematics, biology, and computer science in Bayreuth, Valladolid, Bonn, and Saarbrücken. He received his diploma degree in mathematics from the University of Bonn in 1999 and his PhD in computer science from Saarland University in 2004. His thesis was honored by the Max Planck Society with the Otto Hahn Medal. Upon graduation, he was awarded the prestigious Emmy Noether fellowship which he used to pursue postdoctoral research at UC Berkeley between 2004 and 2006. He was affiliated with the Program for Evolutionary Dynamics at Harvard University before joining ETH Zurich in 2007.
Niko Beerenwinkel's research is concerned with developing mathematical models of complex biosystems and efficient Algorithms for analyzing high-throughput molecular data. His interests range from mathematical foundations of biostatistical models to clinical applications. Current research topics include graphical models, molecular evolution, HIV drug resistance, somatic evolution of cancer, and ultra-deep sequencing of virus populations.
Michel Milinkovitch
Patterning of the vertebrate skin through mechanical and Turing instabilities
Michel Milinkovitch is Full Professor in the Department of Genetics & Evolution at the University of Geneva (Switzerland). He is also a member of the Institute of Genetics and Genomics in Geneva (iGE3) since its foundation (2011) and a Group leader of the Swiss Institute of Bioinformatics (SIB) since 2014. As an evolutionary geneticist, he contributed significantly to quantitative analysis and modelling in Molecular Phylogenomics and Applied Evolutionary Genetics. He has developed concepts, analytical tools, and algorithms/models for multiple sequence alignments, phylogeny inference and haplotypic network building. His recent focus is on Evolutionary Developmental Genetics (Evo-Devo) and the Physics of Biology. He has published over 90 papers in international peer-reviewed journals for a cumulate impact factor of over 800. He has also published 7 book chapters. He has given over 160 talks all around the world. He has been reviewer for over 30 peer-reviewed international journals and for academic promotions, grant proposals, and PhD committees in Europe and the USA. He has served, and is still serving, on the Editorial Board of scientific journals and has supervised more than 15 PhD theses. He has served on the ERC (European Research Council) evaluation panel. Michel Milinkovitch obtained several prizes and awards both for his academic and popularisation work. He is co-founder of the spin-off Delphi Genetics (www.delphigenetics.com).
Session 2: Artificial Intelligence / Machine Learning
Manfred Claassen
(Un-)supervised learning of cell population structure from single-cell snapshot data
Manfred Claassen is Assistant Professor at the Institute of Molecular Systems Biology of ETH Zurich.
He carried out parallel studies in Biochemistry and Computer Science at the University of Tübingen and was awarded a Diploma in Biochemistry in 2004 and a Diploma in Computer Science in 2006. In 2010 he obtained his PhD from ETH Zurich. During his doctoral studies he developed statistical methods to design and validate proteome measurements. In 2011 he moved on for postdoctoral training with Daphne Koller at Stanford University, where he focused on inferring informative network models from single cell resolved perturbation studies.
His research aims to elucidate the composition of heterogeneous cell populations and how these implement function in the context of cancer and immune biology by jointly evaluating single cell and genome wide measurements. The Claassen group builds on concepts from statistics, machine learning and mathematical optimization to develop probabilistic approaches to describe biological systems, learn these descriptions from data and to design experiments to validate hypotheses following from computational analyses.
Enkelejda Miho
Artificial intelligence uncovers signatures of antibodies
Enkelejda Miho is a Professor of Digital Life Sciences and a group leader of artificial intelligence in health at the Institute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland. She holds a master in pharmaceutical chemistry and technology from the University of Bologna, a diploma of advanced studies in pharmaceutical medicine from the European Center of Pharmaceutical Medicine (ECPM) of the University of Basel, and a doctorate in Biotechnology from ETH Zurich. As a pioneer fellow, she founded aiNET – the immuno-informatics ETH spin-off for therapeutic antibody and T-cell discovery.
María Rodríguez Martínez
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María Rodríguez Martínez is a Research Staff Member at IBM Research Zurich since November 2013.
Dr. Rodríguez did her undergraduate studies in Physical Sciences at Universidad Complutense de Madrid. She then obtained a PhD in Theoretical Cosmology at the Institut d’Astrophysique de Paris. Her PhD research focused on developing cosmological models of the early evolution of the universe with additional spatial dimensions. After completing her PhD, she moved to the Hebrew University in Jerusalem, where she focused on setting astrophysical bounds to theories that break Lorentz symmetry using the high-energy emissions from Gamma-Ray Bursts, extremely powerful explosions of gamma rays coming from outside our galaxy.
In 2007, she transitioned into the field of Systems Biology as a postdoc at the Weizmann Institute of Science in Rehovot (Israel). Her research was devoted to the development of quantitative descriptions of biological networks and the complex interactions within. In 2009, she moved to Columbia University where she developed quantitative models to understand cancer gene dysregulation.
Her research at IBM focuses on integrating different high-throughput molecular datasets in order to build comprehensive molecular models of disease that can help clinicians to provide better diagnoses and suggest personalized therapies.
Michael Stadler
Mammalian chromatin remodelers selectively mediate transcription factor binding
Michael Stadler is Head of Computational Biology at the Friedrich Miescher Institute for Biomedical Research. He studied immunology and bioinformatics at the Universities of Bern, Lausanne and Geneva. During his postdoctoral training, he developed computational approaches for allergenicity prediction in Bern, and to infer splicing regulatory sequences in the lab of Chris Burge at MIT, Cambridge, USA.
At the FMI, the Stadler group studies gene regulation through the analysis and modelling of genome-wide datasets. In close collaboration with experimental researchers, he analyses data from various biological contexts including cancer progression, chromatin biology and cellular differentiation. His research aims to better understand how the different layers of epigenetic, transcriptional and post-transcriptional regulation interact and contribute to the control of gene expression.
Session 3: Systems Biomedicine
Paola Picotti
Probing protein structural changes on a proteome-wide scale
Paola Picotti has been an Assistant Professor (SNSF Professorship) of the Biology of Protein Networks at the Institute of Biochemistry (IBC) in the Department of Biology of ETH Zurich since 2011.
Paola Picotti studied pharmaceutical chemistry and technology at the University of Padua in Italy and completed a PhD in the molecular mechanisms of protein folding at the same university’s CRIBI Biotechnology Center in 2006.
After a brief research stint at the MATI Center of Excellence at the University of Udine in 2007, she joined ETH Zurich as a Marie Curie post-doctoral student in Professor Rudolf Aebersold’s group at the Institute of Molecular Systems Biology. She developed a quantitative method of targeted proteomic analysis that enables the behavior of cellular protein networks to be studied under different conditions. In 2011 she took up a position as an independent group leader at ETH Zurich’s Institute of Biochemistry.
Prof. Picotti’s team uses advanced targeted proteomic and phosphoproteomic methods to study protein misfolding and aggregation in cells.
Patrick Matthias
Towards a mechanistic and quantitative framework for virus uncoating
Patrick Matthias is Senior Group Leader at the Friedrich Miescher Institute for Biomedical Research since 1997 and Professor at the University of Basel since 2004.
After doing his undergraduate studies at the University of Geneva, Prof. Matthias went on to obtain his PhD at the University of Heidelberg. He moved to Zurich to carry out a postdoc at the University of Zurich, after which he spent two years at the Whitehead Institute for Biomedical Research at MIT as a Research Fellow. In 1991 he joined the FMI as Junior Group Leader. Patrick Matthias serves as President of the Forum for Genetic Research, Swiss Academy of Natural Sciences
The Matthias group at FMI studies transcriptional and epigenetic networks and function of histone deacetylases in mammals, with a focus on the molecular mechanisms underlying cellular plasticity and cell-specific gene expression in lymphoid (B) cell development.
Uwe Sauer
Metabolic coordination through metabolite-protein interactions
Uwe Sauer is Associate Professor for Systems Biology in the Institute for Molecular Systems Biology at the ETH Zurich.
Prof. Sauer studied biology at the University of Göttingen, where he obtained his PhD in microbiology with Prof. P. Dürre. After two years of postdoctoral research at the Institute of Biotechnology (ETH Zurich), he headed the Microbial Metabolic Engineering group under the supervision of Prof. Bailey at the same Institute. Starting in 2000, he developed the independent research group Applied Systems Biology and Metabolic Engineering at the Institute of Biotechnology.
His research interests are complex metabolic networks in microbes and higher cells. The particular focus is on technology development for global and quantitative experimental methods in the area of metabolomics and fluxomics, and on data integration within computational models. He is a member of various international Editorial Boards and elected member in scientific steering and advisory committees of international organizations in systems biology and biotechnology.
Bart Deplancke
The genetic and molecular determinants of circadian variation in D. melanogaster
Bart Deplancke is Associate Professor and Vice-Dean of Innovation at EPFL.
Prof. Deplancke graduated as a Biochemical Engineer at Ghent University (Belgium, 1998) after which he pursued doctoral studies in Immunobiology at the University of Illinois (USA). He then engaged in postdoctoral, regulatory genomics work in the laboratories of Marc Vidal and Marian Walhout (Harvard and UMass Medical School respectively) during which he developed a now patented, high-throughput protein-DNA interaction screening approach. When he established his lab at EPFL in 2007, he decided to continue developing new experimental and computational approaches to answer questions related to the biology of the genome.
The Deplancke Lab has built a sizeable toolkit, involving microfluidics, high-throughput sequencing, and single cell genomics, to address questions pertaining to the origin, diversity and function of stromal cells in metabolic tissues as well as to how genomic variation affects molecular and organismal diversity with a specific focus on metabolic phenotypes. In 2013, he also became a Swiss Institute of Bioinformatics Group Leader and in 2017, he was elected to the National Research Council of the Swiss National Science Foundation.
