Section III: In-silico Biology & Machine Learning

Research Section III (Head: N.N.) focuses on algorithmic systems biology using bioinformatics methods and machine learning concepts. Scientists in Section III pursue the following goals:

  • Development and application of new software tools and algorithms for the integrative multi-scale evaluation of large data sets from the analysis of food-relevant effector systems as well as sequence, expression and metabolite analyses.
  • Development of computer-assisted modeling and analytical tools; use of machine learning concepts to analyze and predict effector system dynamics in food and biological systems.
  • Correlation of experimental data on biologically active compounds with in silico data
  • Creation and operation of a data mining platform to integrate, analyze, model and simulate heterogeneous multimodal data from standardized quality-controlled omics and biological studies
  • Combination of text mining, chemoinformatics and network biology to perform comprehensive biological analyses to describe the synergistic interactions between small molecules, which lead to specific phenotypes

This research provides new systems insights into the changes and biological effects of biofunctional food effector signatures. The insights also contribute to the development of personalized nutrition concepts.