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Prof. Dr. Pascal Falter-Braun


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.

Skills and Expertise

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

Selected Publications

2022 Nat Biotechnol

Kim DK, Weller B, Lin CW, Sheykhkarimli D, Knapp JJ, Dugied G, Zanzoni A, Pons C, Tofaute MJ, Maseko SB, Spirohn K, Laval F, Lambourne L, Kishore N, Rayhan A, Sauer M, Young V, Halder H, la Rosa NM, Pogoutse O, Strobel A, Schwehn P, Li R, Rothballer ST, Altmann M, Cassonnet P, Coté AG, Vergara LE, Hazelwood I, Liu BB, Nguyen M, Pandiarajan R, Dohai B, Coloma PAR, Poirson J, Giuliana P, Willems L, Taipale M, Jacob Y, Hao T, Hill DE, Brun C, Twizere JC, Krappmann D, Heinig M, Falter C, Aloy P, Demeret C, Vidal M, Calderwood MA, Roth FP, Falter-Braun P.

A proteome-scale map of the SARS-CoV-2-human contactome

2020 Nature

Altmann M, Altmann S, Rodriguez PA, Weller B, Elorduy Vergara L, Palme J, Marín-de la Rosa N, Sauer M, Wenig M, Villaécija-Aguilar JA, Sales J, Lin CW, Pandiarajan R, Young V, Strobel A, Gross L, Carbonnel S, Kugler KG, Garcia-Molina A, Bassel GW, Falter C, Mayer KFX, Gutjahr C, Vlot AC, Grill E, Falter-Braun P. Extensive signal integration by the phytohormone protein network. .. .

Extensive signal integration by the phytohormone protein network

2014 Cell Host Microbe

Weßling R, Epple P, Altmann S, He Y, Yang L, Henz SR, McDonald N, Wiley K, Bader KC, Gläßer C, Mukhtar MS, Haigis S, Ghamsari L, Stephens AE, Ecker JR, Vidal M, Jones JD, Mayer KF, Ver Loren van Themaat E, Weigel D, Schulze-Lefert P, Dangl JL, Panstruga R, Braun P. Convergent targeting of a common host protein-network by pathogen effectors from three kingdoms of life. Cell Host Microbe.;16(3):364-75. .

Convergent targeting of a common host protein-network by pathogen effectors from three kingdoms of life.

2011 Science

Braun P (Chair)*, Carvunis AR, Charloteaux B, Dreze M, Ecker JR*, Hill DE*, Roth FR, Galli M, Balumuri P, Bautista V, Chesnut JD, Kim RC, de los Reyes C, Gilles P, Kim CJ, Matrubutham U, Mirchandani J, Olivares E, Patnaik S, Quan R, Ramaswamy G, Shinn P, Swamilingiah GM, Wu S, Byrdsong D, Dricot A, Duarte M, Gebreab F, Gutierrez BJ, MacWilliams A, Monachello D, Mukhtar MS, Poulin MM, Reichert P, Romero V, Tam S, Waaijers S, Weiner EM, Cusick ME, Roth FP, Tasan M, Yazaki J, Ahn YY, Barabási AL, Chen H, Dangl JL, Fan C, Gai L, Ghoshal G, Galli M, Hao T, Lurin C, Milenkovic T, Moore J, Pevzner SJ, Przulj N, Rabello S, Rietman EA, Rolland T, Santhanam B, Schmitz RJ, Spooner W, Stein J, Tasan M, Vandenhaute J, Ware D, Vidal M;

Evidence for network evolution in an Arabidopsis interactome map. Arabidopsis Interactome Mapping Consortium.

2008 Science

Yu H†, Braun P†, Yildirim MA†, Lemmons I, Venkatesan K, Sahalie J, Hirozane-Kishikawa T, Gabreab F, Li N, Simonis N, Hao T, Rual JF, Dricot A, Vazquez A, Murray RR, Simon C, Tardivo L, Tam S, Svrzikapa N, Fan C, de Smet AS, Motyl A, Hudson ME, Park J, Xin X, Cusick ME, Moore T, Boone C, Snyder M, Roth FP, Barabási AL, Tavernier J, Hill DE, Vidal M*

High-quality binary protein interaction map of the yeast interactome network.


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