Moreover, INF will provide integrated analysis of different data by mathematical modeling for further discussion, interpretation and to draw conclusions for follow-up experiments. Pattern recognition and machine learning methods will be applied to predict chemical structure of yet unknown secondary metabolites. Network model inference will support the understanding of the roles of chemical mediators in structuring communities. The context-dependent and diverse interaction of chemical mediators in complex biosystems of bacterial, fungal, microalgal, plant, animal and even human cells will be analyzed and modeled to understand its roles in structuring communities. That includes the analysis of transcriptome, proteome and metabolome data as well as the modeling of gene regulation. Pattern recognition and computational modeling will help to unveil new chemical entities, in particular secondary metabolites, and offer new strategies to modulate and control consortia. Pattern recognition and network modeling together with model-based experimental design will direct to experimentally investigate the structure, regulation, activity and function of natural products within complex biotic and abiotic communities. Finally, pattern recognition and network modeling will provide a comprehensive overview on the mode of action of natural products that shape complex communities.

login INTRANET