The byteMAL Maastricht conference will host two keynote presentations by:

 

Dr. Benjamin Balluff from Maastricht University

Talk title: “Computational and statistical issues in imaging mass spectrometry data.”

Mass spectrometry imaging (MSI) is a novel and powerful ex vivo molecular imaging technique. It allows the unlabelled mapping of hundreds of molecules simultaneously in tissues through the acquisition of thousands of mass spectra in a spatially-resolved way across a tissue specimen. The ability to obtain cell-type specific profiles, has made MSI a promising technique for the discovery of more specific biomarkers.
In this presentation, the concept of MSI is presented along with descriptions of the nature of the resulting data. Based on that, the possibilities and considerations in computational and statistical analysis of MSI data will be explained. This includes the important steps of data processing and strategies of data analysis. The latter embraces unsupervised and supervised multivariate methods, as well as statistical testing in MSI. Examples will be shown how some of the challenges in MSI can be overcome and successfully applied in biomedical research.

 

Benjamin Balluff is currently Assistant Professor for Imaging Bioinformatics at the Maastricht MultiModal Molecular Imaging institute. He obtained training as bioinformatician at the Weihenstephan University of Applied Sciences. In his PhD he used MSI for biomarker discovery in gastric cancer. As PostDoc he developed novel unsupervised approaches to detect clinically-relevant intra-tumor heterogeneity in MSI datasets.

 

Prof. Dr. Julio Saez-Rodriguez from RWTH University

Talk title: “ Computational models to understand and combat cancer: from clinical genomics to biochemical modelling.”

Large-scale genomic studies are providing unprecedented insights into the molecular basis of cancer, but it remains challenging to leverage this information for the development and application of therapies. We have performed an integrated analysis of the molecular profiles of 11,215 primary tumours and 1,001 cancer cell lines, along with the response of the cell lines to 265 anti-cancer compounds. This analysis finds alterations in tumours that can confer drug sensitivity or resistance, and sheds light on which data types (genomic, transcriptomic, or methylation) are most informative to prioritize treatment.
Integration of this data with various sources of prior knowledge, such as signaling pathways, points at molecular processes involved in resistance mechanisms. These mechanisms are often poorly understood, and mathematical mechanistic models can be built to dissect biochemically the mechanism of action of targeted therapeutics, and understand the molecular basis of drug resistance, thereby providing new treatment opportunities.

 

Julio Saez-Rodriguez is Professor of Computational Biomedicine at the Joint Research Center for Computational Biomedicine at the RWTH University Medical Hospital in Aachen, Germany (www.combine.rwth-aachen.de) and a visiting group leader at the European Bioinformatics Institute (EMBL-EBI). He is an affiliated member of Sage-Bionetworks and a director of the DREAM initiative to catalyze the development of methods in systems biology (www.dreamchallenges.org).
He studied Chemical Engineering at the Universities of Oviedo and Stuttgart, and obtained his PhD at the University of Magdeburg and the Max-Planck-Institute with E. D. Gilles in 2007. After this, he was a postdoctoral fellow at Harvard Medical School with Peter Sorger and Doug Lauffenburger at M.I.T., and a Scientific Coordinator of the NIH-NIGMS Cell Decision Process Center. From 2010 until 2015 he was a group leader at EMBL-EBI and a senior fellow at Wolfson College (Cambridge).
His group develops and applies computational methods to acquire a functional understanding of signaling networks and their deregulation in disease, and to apply this knowledge to develop novel therapeutics. To this end, his group collaborates closely with experimental groups and pharmaceutical companies.