Methodology (Measurement and Modelling)
- Jan Dusek, IMFS
- Bertrand Vileno, Institut de Chimie de Strasbourg, Laboratoire POMAM
- Philippe Turek, Institut de Chimie de Strasbourg, Laboratoire POMAM
- Marc André Delsuc, IGBMC
- Roland H. Stote, IGBMC
- Emmanuelle Leize, Institut de Chimie de Strasbourg, LDSM2
- Noelle Potier, Institut de Chimie de Strasbourg, LDSM2
- Armelle Charrié, Institut de Chimie de Strasbourg, LDSM2
- Yannis Francois, Institut de Chimie de Strasbourg, LDSM2
- Christophe Prud'homme, IRMA/EDPTC
- Abdelkader Saidi, IRMA/EDPTC
- Alexandre Varnek, Institut de Chimie de Strasbourg, Laboratoire d'Infochimie
Mass Spectrometry, Nuclear Magnetic Resonance, Electron Paramagnetic Resonance, Molecular Dynamics Simulations, Computational Biology, Non-linear Stochastic Dynamical Systems, Irreversible Dynamics, Multiscale.
In physical and life sciences, systems are inherently complex. Their study is undergoing an evolution towards multi-disciplinarity in the quest for answers to specific questions, whether they be fundamentally oriented or applicative. In this context, there is a clear and increasing need for characterization methods, which include advanced experimental techniques, such as Mass Spectrometry and Electron Paramagnetic Resonance, techniques in theoretical modeling, such as Molecular Dynamics simulations and modeling by partial differential equations, as well as structural techniques, including X-ray crystallography and NMR spectroscopy. The problems are sufficiently complex that they often require a multipronged approach. From the complexity of the systems and diversity of the experimental methods arise fundamental problems in data analysis that are common to the above experimental methods. One of the principal needs in this multidisciplinary environment is an approach that lets one integrate this diverse data towards a coherent interpretation and the use of coherent datasets in subsequent studies. Emerging techniques in both the experimental and theoretical sciences generate massive datasets that often require an interpretation at an increasingly high resolution. Noise, then, takes on a predominant role making data interpretation more difficult. New techniques are required to address this phenomenon. Concurrent with this evolution toward multidisciplinary is the increasing role of modeling in a wide rage of domains ranging from behavioral, educational, organizational, physical, life and social sciences. Modeling aids both in data interpretation, as well as for the generation of new knowledge. This brings with it issues that are addressed in the questions below.
- Experimental Characterization of Complex Systems
- Noisy and/or Massive Datasets
- Data Integration
- Modelling and Software
Strasbourg is a node of the National network for advanced EPR spectroscopies (TGE Renard CNRS). This network gives an access to great variety of EPR facilities.
There is an obvious need for interdisciplinary education to encompass the diversity of issues to be address when investigating complex system (e.g. multiscale approach from meso- to nano-scale is required for such study). Specific needs for the study of complex systems will be satisfied by putting appropriate educational tools online through the digital campus. At the University of Strasbourg, inter-disciplinary and international educational programs are presently open at the physics department:
- French-German master of soft matter sciences (physics and chemistry)
- Nano-science and materials of biological interest (oriented towards medical applications).
- Master in Chemoinformatics (modeling of complex systems in chemistry and biology)
- Master In Silico Drug Design (oriented towards medical applications)
- Trinational research training group (GRK 1478) and doctoral college "Membrane Proteins and Biological Membranes" / offers doctoral positions (involved Universities: Basel, Freiburg, Strasbourg)