Each session will consists of two plenary lectures, selected short presentations and deep discussions covering different metabolomics hot topics.
Wednesday, 20. 9.
19:00 – 22:00 – Get together evening session
„Thinking about metabolomics“
Chairman: to be confirmed
Thursday, 21. 9.
08:50 – 09:00 – Opening the Workshop
09:00 – 13:00 – I. From samples to peaks
technologies generating data – resolution sensitivity, technology challenges, design of experiment
From spectrometric data to metabolic networks: An integrated quantitative view of cell metabolism
Oscar Yanes received his Ph.D. degree in Biochemistry from the Autonomous University of Barcelona (Spain). In 2007 he became Research Associate in Gary Siuzdak’s lab at The Scripps Research Institute (La Jolla, USA). Since 2011 he coordinates the metabolomics platform of the Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), he is affiliated member at the IRB Barcelona and assistant professor at the Universitat Rovira i Virgili where he also leads his own research group (www.yaneslab.com). He has long experience in developing new technologies, methods and applications in mass spectrometry-based metabolomics. His lab now focuses on understanding metabolic dysregulations in disease through integrating MS and NMR-based metabolomics with other omic platforms.
Presentation will focus on major principles of metabolomics experimental design, strengths and weaknesses of metabolomics analytical approaches, and factors that impact upon subsequent analysis of the data. This will include discussing about current trends and challenges in metabolomics, with particular emphasis on experimental and computational approaches based on LC- and GC-mass spectrometry (MS) and nuclear magnetic resonance (NMR) to enable a comprehensive analysis of cellular metabolites.
I am a Senior Research Fellow and Head of Metabolomics at the University of Glasgow, with a focus on methodological and bioinformatic development of technologies for metabolomics and proteomics, especially in the context of bacterial adhesion and biofilm development. With a background in both biology and computing science, I found my niche in biological mass spectrometry, where my expertise in processing and interpreting large datasets in a biological context became invaluable in the rapidly developing field of proteomics, and, later, metabolomics, with several highly cited papers in the field. My personal research focuses primarily on improved quality control, assisted interpretation of complex ‘omics data and improved metabolome coverage. Highlights of this research include publication of a combined HILIC/RP method for increasing metabolome coverage and the Polyomics Metabolomics Pipeline (PiMP) software platform for metabolomic analysis. In light of the importance of ‘omics to modern biological research, I also have made significant contributions to: regenerative medicine in collaboration with Prof. Matt Dalby; host-pathogen interactions with Dr. Andy Roe and biochemical parasitology with Prof. Mike Barrett. My profile was recently highlighted in ‘The Analytical Scientist’.
My presentation will focus on biological interpretation of metabolomics datasets: from the perspective of a core facility, how do you interact with collaborators, how much interpretation can and do we provide, and how that interaction is affected by statistics, data and ambiguity, especially in the context of identification in untargeted analysis? I will focus primarily on LC-MS but with some context from NMR and GCMS.
13:00 – 14:00 – Lunch
14:00 – 18:00 – II. From peaks to numbers
from data processing and pre-processing up to data normalization
Steffen Neumann studied computer science and bioinformatics at Bielefeld University, and now his group focuses on the development of tools and databases for metabolomics and computational mass spectrometry. The group develops algorithms for data processing of metabolite profiling experiments, which are available in several Open Source Bioconductor packages, and addresses the identification of unknowns in mass spectrometry data. The IPB is member of the MassBank consortium and operated the first MassBank server in Europe. The MetFrag and MetFusion tools allow the identification of compounds where no reference spectra are available.
During the MOVISS meeting, I am looking forward to discussions on large-scale metabolite profiling, and large-scale characterisation of metabolite features from MS data. Last-but-not-least, the value of the data drastically increases if it is shared in standardised formats, so that others can grab it, re-process and make the most of the data.
Pre-processing of metabolomic data as a challenging enterprise
Prof. Beata Walczak graduated in chemistry from the Faculty of Mathematics, Physics and Chemistry, Silesian University, Katowice, Poland, in 1979. Since then she has been working in the Institute of Chemistry, Silesian University, where now she is the head of the Department of Analytical Chemistry. Meanwhile, she stayed as a post-doc at the University of Orleans (France) and at the Graz University of Technology (Austria). She also held a post of a visiting professor at Vrije Universitiet Brussel (Belgium), at Rome Univeristy ‘La Sapienza’ (Italy), at AgroParisTech University (France), at University of Modena and Reggio Emilia (Italy) and at Radboud University (The Netherlands). From the early 90’s she has been involved in chemometrics and her main scientific interests are in all aspects of data exploration and modelling (dealing with missing and censored data, dealing with outliers, data representativity, enhancement of instrumental signals, signal warping, data compression, linear and non-linear projections, development of modelling approaches, feature selection techniques etc.). She authored and co-authored ca. 165 scientific papers, 400 conference papers, and delivered many invited lectures at the numerous international chemistry meetings. She acted as an editor and co-author of the book Wavelets in Chemistry, Vol. 22 in the series ‚Data Handling in Science and Technology‘, Elsevier, Amsterdam, 2000, and as an co-editor of four-volume Comprehensive Chemometrics, Elsevier, Amsterdam, 2009. Currently she acts as Editor of the journal Chemometrics and Intelligent Laboratory Systems and of ‘Data Handling in Chemistry and Technology’ (the Elsevier book series), and also as a member of the editorial boards of Talanta, Analytical Letters, J. Chemometrics, and Acta Chromatographica.
She will speak about data preprocessing, and namely about challenges associated with preprocessing of flow cytometry data.
20:00 – 24:00 – Dinner and Social Program
Friday, 22. 9.
09:00 – 13:00 – III. From numbers to pictures
methods of univariate and multivariate statistical analysis
Age K. Smilde
Numerical Representations of Metabolic Systems
Age K. Smilde is full professor of Biosystems Data Analysis at the Swammerdam Institute for Life Sciences at the University of Amsterdam and is also affiliated with the Academic Medical Centre of that same university. As of June 1, 2013 he holds a part-time position as professor of Computational Systems Biology at the University of Copenhagen. His research interest focuses on two topics: data fusion and network inference. Data fusion concerns integrating functional genomics data and fusing data with prior biological knowledge. The network topic encompasses network reconstruction, deriving network properties from time-resolved data and computational network analysis. He has published more than 200 peer-reviewed papers and has been the Editor-Europe of the Journal of Chemometrics during the period 1994-2002. He chaired the 1996 Gordon Research Conference on Statistics in Chemistry & Chemical Engineering. In 2006, he received the Achievements in Chemometrics Award of the Eastern Analytical Symposium.
Data is not the same as numbers. Instruments generate numbers but before these become meaningful they have to be transformed to data. That transformation involves theory (both instrumental as well as biological). Hence, data is loaded with theory and this has repercussions for the subsequent data analysis and interpretation of the results. These notions will be illustrated with examples from metabolomics. There are many open questions which will also be discussed.
13:00 – 14:00 – Lunch
14:00 – 18:00 – IV. From pictures to understanding
understanding in terms of biology, flux and/vs. steady state data
Nicola Zamboni earned his PhD in Biotechnology in the group of Jay Bailey at ETH Zurich with a thesis on metabolic engineering and 13C metabolic flux analysis. In 2004 he moved as a postdoctoral fellow to the Stanford Genome Technology Center, where he developed and applied metabolomics-based approaches for unraveling metabolic changes in eukaryotic cells. Since late 2005, he’s a group leader and independent PI at the Institute of Molecular Systems Biology of ETH Zurich.
The lab focuses on the development of mass spectrometry and computational methods to investigate cellular metabolism from bacteria to human cells in a variety of questions related to systems biology, preclinical research, biotech, and healthcare. It pursues a primarily data-driven approach largely based on mass spectrometry, i.e. high-throughput and non-targeted metabolomics and 13C-metabolic flux analysis. These data are integrated with a variety of computational approaches to support data mining, integration, interpretation, and prediction.
At the MOVISS meeting, the discussion will focus on the interpretation of metabolome data, i.e. on how to test testable hypotheses from typically complex, ill-dimensioned, non-quantitative, sparse and noisy data.
18:00 – 18:10 – Closing the Workshop
20:00 – 24:00 – Dinner and Social Program