Ph.D.: Technion, Israel Institute of Technology
Post-doctorate: Whitehead Institute for Biomedical Research & MIT Department of Biological Engineering
Position: Senior Lecturer
Department of Clinical Biochemistry
Faculty of Health Sciences
E-mail: estiyl@bgu.ac.il
Computational systems biology of human disease
Comprehensive understanding of the molecular basis of incurable human diseases is essential for opening new avenues for treatment. In an effort to elucidate their molecular basis, human diseases are increasingly studied using high-throughput approaches offering unprecedented genomic, transcriptomic and proteomic views into their etiology. However, independent analyses of the resulting data typically enable only a limited understanding of disease processes. mRNA profiling assays, for instance, identify transcriptional changes that occur during disease, but they do not reveal the cellular pathways that lead to these changes. Integrative analysis of these valuable data has a great potential to reveal a much broader view of disease processes, which, in turn, could improve diagnostics and accelerate the search for a cure.
We are developing computational approaches to meaningfully integrate diverse large-scale molecular data. By applying these approaches to top-notch disease data we aim to gain a broad insight into disease mechanisms.
Network biology of human disease - Molecular interaction networks have proven to be a leading methodology for elucidating complex cellular processes from diverse, large-scale molecular data. We are developing network optimization techniques to both distill and meaningfully integrate diverse molecular data in order to reveal the underlying disease processes.
Illuminating the molecular basis of Parkinson disease – The cellular pathways leading to neuronal cell death in this common neurodegenerative disorder (1% of the population over the age 50) are not fully understood. To reveal these pathways we are applying network approaches to state-of-the-art molecular data of the disease.
The toxic determinants of protein overexpression – protein overexpression is associated with various human diseases including neurodegenerative disorders and cancer. We are characterizing the set of proteins whose overexpression is toxic using a wide range of bioinformatics techniques.
Yeger-Lotem E. and Margalit H. (2003). Detection of regulatory circuits by integrating the cellular networks of protein-protein interactions and transcription regulation. Nucleic Acids Research 31:6053-6061.
Yeger-Lotem E., Sattath S., Itzkovitz S., Kashtan N., Milo R., Pinter R.Y., Alon U. and Margalit H. (2004). Network motifs in the integrated cellular network of transcription regulation and protein-protein interaction. Proceedings of the National Academy of Sciences USA (PNAS) 101:5934-5939.
Yeger-Lotem E., Riva L., Su L.J., Gitler A., Cashikar A., King O.D., Auluck P.K., Geddie M.L., Valastyan J.S., Karger D.R., Lindquist S. and Fraenkel E. (2009). Bridging the gap between high-throughput genetic and transcriptional data reveals cellular pathways responding to alpha-synuclein toxicity. Nature Genetics 41:316-323.
Barshir R., Basha O., Eluk A., Smoly I.Y., Lan A. and Yeger-Lotem E. (2013). The TissueNet database of human tissue protein-protein interactions. Nucleic Acids Research 41:D841-844.
Basha O., Tirman S., Eluk A. and Yeger-Lotem E. (2013). ResponseNet2.0: Revealing signaling and regulatory pathways connecting your proteins and genes-now with human data. Nucleic Acids Research 41:W198-203.
Lan A., Ziv-Ukelson M. and Yeger-Lotem E. (2013). A context-sensitive framework for the analysis of human signalling pathways in molecular interaction networks. ISMB 2013 and Bioinformatics 29:i210-216.
