CeMM Adjunct Principal Investigator
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. Menche joined CeMM as principal investigator in 2015, where he built his group supported by a Vienna Research Groups for Young Investigators career integration grant from 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 Mathematics Department 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 with severe hereditary diseases.
Pirch S et al. The VRNetzer platform enables interactive network analysis in Virtual Reality. Nat Commun. 2021 Apr 23;12(1):2432. (abstract)
Buphamalai P et al. Network analysis reveals rare disease signatures across multiple levels of biological organization. Nat Commun. 2021 Nov 9;12(1):6306. (abstract)
Caldera M, et al. Mapping the perturbome network of cellular perturbations. Nat Comm. 2019;10:5140. (abstract)
Langhauser F, Casas AI, Dao VT, Guney E, Menche J, Geuss E, Kleikers PWM, López MG, Barabási AL, Kleinschnitz C, Schmidt HHHW. A diseasome cluster-based drug repurposing of soluble guanylate cyclase activators from smooth muscle relaxation to direct neuroprotection. NPJ Syst Biol Appl. 2018 Feb 5;4:8. (abstract)
Caldera M, Buphamalai P, et al. Interactome-based approaches to human disease. Curr Opin Syst Biol. 2017;3:88. (abstract)
Menche J, Guney E, Sharma A, Branigan PJ, Loza MJ, Baribaud F, Dobrin R, Barabási AL. Integrating personalized gene expression profiles into predictive disease-associated gene pools. NPJ Syst Biol Appl. 2017 Mar 13;3:10. (abstract)
Guney E, Menche J, Vidal M, Barábasi AL. Network-based in silico drug efficacy screening. Nat Commun. 2016 Feb 1;7:10331. (abstract)
Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science. 2015 Feb 20;347(6224):1257601. (abstract)
Ghiassian SD*, Menche J*, Barabási AL. A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. PLoS Comput Biol. 2015 Apr 8;11(4):e1004120. (abstract)
Sharma A*, Menche J*, Huang CC, Ort T, Zhou X, Kitsak M, Sahni N, Thibault D, Voung L, Guo F, Ghiassian SD, Gulbahce N, Baribaud F, Tocker J, Dobrin R, Barnathan E, Liu H, Panettieri RA Jr, Tantisira KG, Qiu W, Raby BA, Silverman EK, Vidal M, Weiss ST, Barabási AL. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum Mol Genet. 2015 Jun 1;24(11):3005-20. (abstract)
Hawrylycz M, Miller JA, Menon V, Feng D, Dolbeare T, Guillozet-Bongaarts AL, Jegga AG, Aronow BJ, Lee CK, Bernard A, Glasser MF, Dierker DL, Menche J, Szafer A, Collman F, Grange P, Berman KA, Mihalas S, Yao Z, Stewart L, Barabási AL, Schulkin J, Phillips J, Ng L, Dang C, Haynor DR, Jones A, Van Essen DC, Koch C, Lein E. Canonical genetic signatures of the adult human brain. Nat Neurosci. 2015 Dec;18(12):1832-44. (abstract)
Rolland T, Taşan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N, Yi S, Lemmens I, Fontanillo C, Mosca R, Kamburov A, Ghiassian SD, Yang X, Ghamsari L, Balcha D, Begg BE, Braun P, Brehme M, Broly MP, Carvunis AR, Convery-Zupan D, Corominas R, Coulombe-Huntington J, Dann E, Dreze M, Dricot A, Fan C, Franzosa E, Gebreab F, Gutierrez BJ, Hardy MF, Jin M, Kang S, Kiros R, Lin GN, Luck K, MacWilliams A, Menche J, Murray RR, Palagi A, Poulin MM, Rambout X, Rasla J, Reichert P, Romero V, Ruyssinck E, Sahalie JM, Scholz A, Shah AA, Sharma A, Shen Y, Spirohn K, Tam S, Tejeda AO, Wanamaker SA, Twizere JC, Vega K, Walsh J, Cusick ME, Xia Y, Barabási AL, Iakoucheva LM, Aloy P, De Las Rivas J, Tavernier J, Calderwood MA, Hill DE, Hao T, Roth FP, Vidal M. A proteome-scale map of the human interactome network. Cell. 2014 Nov 20;159(5):1212-1226. (abstract)
Zhou X*, Menche J*, Barabási AL, Sharma A. Human symptoms-disease network. Nat Commun. 2014 Jun 26;5:4212. (abstract)
* equal contribution