Title: “Bridging the Gap between Data-Driven Knowledge Discovery and Medicine”
Speaker: Dr Gianni PANAGIOTOU, School of Biological Sciences, The University of Hong Kong
Date: 1 March 2013 (Friday)
Time: 12 noon
Venue: Seminar Room 1 and 2, G/F Laboratory Block, Li Ka Shing Faculty of Medicine, 21 Sassoon Road
All are welcome.
Short Biography Gianni Panagiotou is Associate Professor at the School of Biological Sciences at The University of Hong Kong since January 2013. During the period 2004-2012 he worked at the Technical University of Denmark under the mentorship of Prof. Jens Nielsen (Center for Microbial Biotechnology), Prof. Søren Brunak (Center for Biological Sequence Analysis) and Prof. Bernhard Palsson (Center for Biosustainability). During 2008-2009 he was a Visiting Assistant Professor at the Department of Pharmaceutical Sciences of The University of Tokyo and for 6 months in 2010 he was a Visiting Associate Professor at the Industrial Biotechnology laboratory of the Chalmers University of Technology (Sweden). He is a classical trained chemical engineer graduated from the National Technical University of Athens (Greece) and he received his PhD in metabolic engineering from the same university in 2004. His research interests include Systems Biology, Metagenomics, Computational Chemical Biology, Metabolic Engineering and Industrial Biotechnology. He has 2 patents, 40 publications in international journals and he has received more than 5 million HK€ as personal grants for his research activities. From 2012 he serves as Editor-in-Chief for the Computational and Structural Biotechnology Journal (www.csbj.org).
Abstract The main objective of our group is to discover new molecular mechanisms using an iterative cycle that starts with experimental data, followed by data analysis and data integration to determine correlations between concentrations of molecules, and ends with the formulation of hypotheses concerning co- and inter-regulation of groups of those molecules. These hypotheses then predict new correlations, which can be tested in new rounds of experiments or by further biochemical analyses. The major strengths of our approach are that it is potentially complete (i.e. genome-wide) and that it addresses the transcriptome, proteome, metabolome and fluxome. Our group works on addressing questions fundamental to our understanding of life, yet progress here will lead to practical innovations in medicine, drug discovery and bioengineering. Our group has two distinct branches: knowledge discovery, or data-mining, which extracts the hidden patterns from huge quantities of experimental data, forming hypotheses as a result; and simulation-based analysis, which tests hypotheses with in silico experiments, providing predictions to be tested by in vitro and in vivo studies. During my talk I will present three case studies; (i) a global analysis of diet for elucidating the synergistic interactions of the small molecules that yield specific phenotypes, (ii) an evaluation of the variability in the metabolic potential of the gut microbiome in healthy & diseased individuals based on variations in the metabolic gene content of the bacterial population, (iii) a structure-based virtual screening (docking) against targets of infectious diseases.