Experimental Characterization of Complex Systems

Towards the characterization of increasingly labile, large and complex systems in biology and chemistry, the development of adapted analytical approaches is required. The main approaches proposed in this network are among the classical approaches used in structural chemistry physics and biology, including spectroscopic methods (Nuclear Magnetic Resonance (NMR) and Electron Paramagnetic Resonance, (EPR)), Mass Spectrometry (MS) and Computational Biology (CB). Beyond their apparent diversity, these techniques all share common challenges : increase in sensitivity, increase in measurement accuracy, the development of adapted sample preparation , improvements in data interpretation and data integration. These techniques are all employed to study system complexity at different size and time scales, ranging from the molecular level to biological macro molecules. In order to exploit the data, it might be relevant to establish connections between experimentations, models and simulations. In order to characterize the systems with respect to their function, it seems crucial to us, to combine a lot of different experimental data sets. The difficulty encountered in the integration of these data comes from the fact that they are very different from the point of view of the physical measurement (molecular distances, molecular weight, activation energy) and from the point of view of the conditions for sample preparation (physical state, concentration).