Prof. Dr. Pascal Falter-Braun

Career

Prof. Dr. Pascal Falter-Braun studied biochemistry in Leipzig and Berlin, and conducted his PhD research and post-doctoral training at Harvard University Harvard Medical School and affiliated hospitals in Boston, MA. 2012 he started his own research group at TU Munich and was awarded an ERC consolidator grant in 2015. Since 2017, Falter-Braun is Professor and chair for Microbe-Host Interactions at LMU and Head of the Institute of Network Biology at the Helmholtz Center Munich. Falter-Braun is a pioneer in protein-interaction network mapping and network analysis with a focus on molecular microbe-host networks. The institute develops experimental high-throughput and analytical deep-learning approaches to understand how network structure influences and determines the biological consequences of perturbations by human genetic variants and infectious agents.

Molecular interactions form the basis of almost all biological processes in any living organism. Perturbations in these molecular networks  result in dysfunctions which manifest themselves in disease up to fatal outcomes. 

Our aim at INET is to understand how these molecular interactions network changes caused by genetic variants or also by environmental influences like viruses or bacteria, lead to pathological processes.  A deep understanding of the interplay of all the different layers  of molecular networks may lead to new strategies for disease prevention and  pharmacological interventions.

To address these fundamental questions, molecular interactions are systematically identified by us at the modul- and proteome level using a robotic experimental platform. The networks mapped in this way are integrated with population genetic, molecular and functional data and analyzed using graph-theoretical and statistical methods. Increasingly important are artificial intelligence (AI) and machine learning (ML) methods for identifying genetic sensitivity and pharmacological intervention points. In addition to our experimental high-throughput (HT) pipeline, bioinformatics, statistical analysis and latest deep learning approaches are used to understand network changes and their system-wide effects. Hypotheses and predictions from this interdisciplinary approach are iteratively validated by biochemical, cell biological and genetic studies at INET.

The combination of systematic interaction mapping, deep learning and mechanistic follow-up allows us to identify disease modules and intervention points.

Network BiologyMolecular InteractionsMicrobe-Host InteractionsSARS-CoV-2 contactomeMicrobiomeAIORFeomesHT Technologies