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CeMM Adjunct Principal Investigator

Jörg Menche

Network Medicine

Institute of Mathematics of the University of Vienna
Dept. of Structural & Computational Biology at the Max Perutz Labs (MFPL)

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Recent advances in high-throughput technologies have created exciting new opportunities for systematically investigating the molecular basis of human disease. CeMM researchers employ a broad range of powerful post-genomic technologies, from next-generation sequencing of genomes, epigenomes and transcriptomes, to high-throughput proteomics and chemical screening. Our group applies diverse computational approaches to help understand and interpret the large datasets derived from these technologies. 

A particular focus is the application of tools and concepts from network theory to elucidate the complex machinery of interacting molecules that constitutes the basis of (patho-) physiological states. The overarching goal of the emerging field of ‘network medicine’ is to (i) provide conceptual insights into the network signatures that characterize diseases states and to (ii) translate these insights to novel bioinformatics tools for the analysis of molecular data. These tools cover a broad range of important challenges, from disease gene identification to tumor classification or drug discovery.


Jörg Menche studied physics in Leipzig, Recife, and Berlin. During his PhD at the Max Planck Institute of Colloids and Interfaces in Potsdam, he specialized in network theory. He then moved to Boston to work as a postdoctoral fellow at Northeastern University and at the Center for Cancer Systems Biology at Dana Farber Cancer Institute, where he applied network theory to elucidate the complex machinery of interacting molecules that constitutes the basis of (patho-)physiological states. Jörg joined CeMM as Principal Investigator in 2015, where he built his group supported by a Vienna Research Groups for Young Investigators career integration grant by the Vienna Science and Technology Fund (WWTF). In 2020, his group moved to the University of Vienna, where he now holds a professorship with a dual appointment at the faculty of mathematics and the Max Perutz Labs. His group is currently pursuing three major lines of research: (i) Trying to identify basic principles that govern how perturbations of biological systems influence each other, (ii) developing novel network-based methods for the integration and analysis of large and diverse biomedical data, in particular using Virtual Reality tools, and (iii) the application of network-based approaches to rare genetic diseases, with the overall goal of developing a truly personalized framework to help individual patients of severe hereditary diseases.

Selected Papers

Caldera M, et al. Mapping the perturbome network of cellular perturbations. Nat Comm. 2019;10:5140. (abstract)

F Langhauser, AI Casas, VTV Dao, E Guney, J Menche, E Geuss, PWM Kleikers MG Lopez, AL, Barabási, C Kleinschnitz, HHHW Schmidt. A diseasome cluster-based drug repurposing of soluble guanylate cyclase activators from smooth muscle relaxation to direct neuroprotection. npj Systems Biology & Applications 4:8; 2018. (abstract)

Caldera M*, Buphamalai P*, et al. Interactome-based approaches to human disease. Curr Opin Syst Biol. 2017;3:88. (abstract)

J Menche*, E Guney*, A. Sharma, P.J. Branigan, M.J. Loza, F. Baribaud, R. Dobrin, A.-L. Barabási. Integrating personalized gene expression profiles into predictive disease-associated gene pool. npj Systems Biology & Applications 3:10; 2017. doi:10.1038/s41540-017-0009-0. (abstract)

E Guney, J Menche, M Vidal, AL Barabási. Network-based in silico drug efficacy screening. Nature Communications 7:10331, 2016. doi: 10.1038/ncomms10331. (abstract)

J Menche, A Sharma, M Kitsak, SD Ghiassian, M Vidal, J Loscalzo, AL Barabási. Uncovering disease-disease relationships through the incomplete interactome. Science 347 (6224), 1257601; 2015. (abstract)

SD Ghiassian*, J Menche*, AL Barabási. A DiseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human Interactome. PLoS Comput Biol 11(4): e1004120; 2015. (abstract)

A Sharma*, J Menche*, C Huang, T Ort, X Zhou, M Kitsak, N Sahni, D Thibault, L Voung, F Guo, N Gulbahce, F Baribaud, J Tocker, R Dobrin, E Barnathan, H Liu, RA Panettieri, KG Tantisira, W Qiu, BA Raby, EK Silverman, M Vidal, ST Weiss, AL Barabási. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Human Molecular Genetics, 24(11):3005-20; 2015. (abstract)

M Hawrylycz, J Miller, V Menon, D Feng, T Dolbeare, A Guillozet-Bongaarts, A Jegga, BJ Aronow, Chang-Kyu Lee, A Bernard, M Glasser, D Dierker, J Menche, F Collman, P Grange, K Berman, S Mihalas, Z Yao, L Stewart, AL Barabási, J Schulkin, J Phillips, L Ng, C Dang, D Haynor, A Jones, D Van Essen, C Koch, E Lein. Canonical Genetic Signatures of the Adult Human Brain. Nature Neuroscience 18: 1832–1844; 2015. (abstract)

T Rolland, M Taşan, B Charloteaux, SJ Pevzner, Q Zhong, N Sahni, S Yi, I Lemmens, C Fontanillo, R Mosca, A Kamburov, SD Ghiassian, X Yang, L Ghamsari, D Balcha, BE Begg, P Braun, M Brehme, MP Broly, AR Carvunis, D Convery-Zupan, R Corominas, J Coulombe-Huntington, E Dann, M Dreze, A Dricot, C Fan, E Franzosa, F Gebreab, BJ Gutierrez, MF Hardy, M Jin, S Kang, R Kiros, GN Lin, K Luck, A MacWilliams, J Menche, RR Murray, A Palagi, MM Poulin, X Rambout, J Rasla, P Reichert, V Romero, E Ruyssinck, JM Sahalie, A Scholz, AA Shah, A Sharma, Y Shen, K Spirohn, S Tam, AO Tejeda, SA Trigg, JC Twizere, K Vega, J Walsh, ME Cusick, Y Xia, AL Barabási, LM Iakoucheva, P Aloy, J De Las Rivas, J Tavernier, MA Calderwood, DE Hill, T Hao, FP Roth, M Vidal.  A proteome-scale map of the human interactome network. Cell 159 (5), 1212-1226; 2014. (abstract)

XZ Zhou*, J Menche*, AL Barabási, A Sharma. Human symptoms–disease network. Nature Communications 5; 2014. (abstract)

[* equal contribution]