31 mars 2011: Prof. Manuel Peitsch
Thursday, March 31st 2011, 12h30
Prof. Manuel C. Peitsch
Swiss Institute of Bioinformatics, Chairman of the Executive Board
"How Systems Biology Can Impact Our Society?"
Manuel Peitsch has worked in the pharmaceutical industry for over fifteen years, following seven years in academia. His work has mainly been in the areas of Computational Life Sciences (incl. bioinformatics) and Experimental Biology (incl. genomics and proteomics) in Drug Discovery. Manuel holds several patents related to proteomics, genomics and computer science and has published over 115 articles in top ranking scientific journals (cited over 11500 times). Manuel has done pioneering work in the area of molecular modeling, cell biology and computational text analytics. Manuel is a founder of several initiatives, including two start-up companies and the Swiss Institute of Bioinformatics. He is the Chairman of the Executive Board of the Swiss Institute of Bioinformatics and an active scientific advisor to several academic and commercial entities. Manuel was a member of the Swiss National Research Council from 2004 to 2010. Manuel is a ComputerWorld Honors Laureate and a recipient of several awards including the New England Business and Technology Award and the UnitedDevices Grid Visionary Award. Manuel holds a BASc in Life Sciences, a MASc in Physical Chemistry and a PhD in Biochemistry; he is also a Professor for Bioinformatics at the University of Basel.
1988: Postdoctoral fellow at the National Cancer Institute, USA
1991: Junior faculty member at the University of Lausanne, CH
1994: Glaxo Institute for Molecular Biology, head of Bioinformatics, CH
1998: GlaxoWellcome, Global Director of Scientific Computing and President GlaxoWellcome Experimental Research, WW
2001: Novartis Institute of Biomedical Research, Global Head of Informatics and Knowledge Management, WW
2005: Novartis Institute of Biomedical Research, Global Head of Systems Biology, WW 2008: Philip Morris International, R&D, Director Computational Sciences and Bioinformatics.
2010: Philip Morris International, R&D, VP Biological Systems Research.
"Assessing Relative Impact of Environmental Perturbations on Biological Networks"
Julia Hoeng1, Florian Martin1, Alain Sewer1, Dexter Pratt2, Ty Thomson2, David Drubin2, Carole Mathis1, Manuel C Peitsch1
1Philip Morris International R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
2 Selventa, Cambridge, MA, USA
Today’s society demands increased scrutiny of the potential health risks of long-term, and sometimes lifelong, exposure to potentially toxic substances. Our goal is to assess the impact of substances on biological processes relevant to multi-dimensional adverse effects. To that end, we are developing computational methods to compare the impact of different perturbations on relevant biological networks using high throughput data.
Two distinct algorithmic approaches were developed: NPA (network perturbation amplitude) metrics, which assesses the amplitude of signaling in a network, and BIF metrics (biological impact factor), which defines a framework for the aggregation of amplitude scores for multiple networks. The application of these methods relies on the development of detailed causal network models of mechanistic biological networks relevant to risk assessment that include downstream measurable quantities affected by perturbations of the pathway. Here, we present application of the methods and demonstrate how impact assessment from relatively short-term experiments can be informative about long-term risk.
Firstly, we applied NPA metrics to a simple experimental system and correlated the scores with an immediate phenotypic outcome. Changes in global mRNA expression were measured in a highly controlled in vitro system where Normal Human Bronchial Epithelial (NHBE) cell cultures were stimulated via exposure to a dose series of TNF ligand. NPA scoring based on a causal network model of TNF/NFKB inflammatory signaling correlated with TNF dose and NFKB translocation into the nucleus, an obligate step in this signaling pathway. Second, we sought to determine whether NPA could be applied to a more complex in vivo experimental system. Whole genome transcriptomic data was collected from the nasal epithelium of rats exposed to a dose-response formaldehyde inhalation (Montecello et al.)*. NPA scoring based on a causal network model of cell proliferation signaling correlated with formaldehyde dose and cell proliferation assessed by unit length labeling index (ULLI).
Finally, we demonstrated that a simple BIF aggregation of the NPA scores for both the TNF/NFKB and the cell proliferation signaling networks applied to transcriptomic data collected from a short-term exposure (13 weeks) in the formaldehyde study correlated with tumor occurrence assessed at 24 months. This suggests that the application of BIF metrics using data collected before the onset of disease could provide insights into eventual disease outcome. These methods possess the virtue of transparency: values can be explained in terms of explicit assumptions in the biological models and in deterministic scoring algorithms. In addition, they facilitate the application of systems biology to applications outside of toxicological assessment, such as drug development, pharmacology, and personalized medicine. * Monticello TM, Swenberg JA, Gross EA, Leininger JR, Kinbell JS, Seilkop S, Starr TB, Gibson JE, Morgan KT: Correlation of revional and nonlinear formaldehyde-induced nasal cancer with proliferating populations of cells. Cancer Res 1996, Mar 1;56(5):1012-22