Ilya Shmulevich, PhD


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Ilya Shmulevich received his Ph.D. in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, in 1997. His graduate research was in the area of nonlinear signal processing, with a focus on the theory and design of nonlinear digital filters, Boolean algebra, lattice theory, and applications to music pattern recognition. From 1997-1998, he was a postdoctoral researcher at the Nijmegen Institute for Cognition and Information at the University of Nijmegen and National Research Institute for Mathematics and Computer Science at the University of Amsterdam in The Netherlands, where he studied computational models of music perception and recognition, focusing on tonality induction and rhythm complexity. In 1998-2000, he worked as a senior researcher at the Tampere International Center for Signal Processing at the Signal Processing Laboratory in Tampere University of Technology, Tampere, Finland. While in Tampere, he did research in nonlinear systems, image recognition and classification, image correspondence, computational learning theory, multiscale and spectral methods, and statistical signal processing.

This background proved to be fruitful for undertaking problems in computational biology at a time when genomic technologies were beginning to produce large amounts of data. In 2001, he joined the Department of Pathology at The University of Texas M. D. Anderson Cancer Center as an Assistant Professor and held an adjunct faculty appointment in the Department of Statistics in Rice University. His work in cancer genomics research spans multiple cancers, with published work in glioma, lymphoma, leukemia, breast cancer, ovarian cancer, and sarcoma. He and his colleagues developed statistical approaches for cancer classification, diagnosis, and prognosis, and applied them to the study of of metastasis, cancer progression, and tumor heterogeneity. Together with long-standing collaborators Edward R. Dougherty (Texas A&M University) and Wei Zhang (M.D. Anderson Cancer Center), he co-developed the model class of probabilistic Boolean networks (PBNs), which was applied to the study of gene regulatory networks in cancer.

Dr. Shmulevich joined the ISB faculty in 2005 where he is currently a Professor. He directs a Genome Data Analysis Center as part of The Cancer Genome Atlas (TCGA) project, a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. He also directs the Computational Core of the Systems Approach to Immunity and Inflammation consortium, which consists of a large multidisciplinary team of investigators working in the fields of immunology and systems biology. These projects entail the development of computational and mathematical approaches for modeling biological systems and analyzing large-scale measurement data sets. Dr. Shmulevich’s research interests also include theoretical studies of complex systems, including information theoretic approaches, as well as the application of image processing and analysis to high-throughput cellular imaging.

Dr. Shmulevich is a co-editor or co-author of six books in the areas of computational biology. He holds Affiliate Professor appointments in the Departments of Bioengineering and Electrical Engineering at the University of Washington, Department of Signal Processing in Tampere University of Technology, Finland, and Department of Electronic and Electrical Engineering in Strathclyde University, Glasgow, UK.

Computational biology, signal and image processing

PhD, Electrical and Computer Engineering Purdue University, 1997


Liu, Guoyan, Da Yang, Rajesha Rupaimoole, Chad V. Pecot, Yan Sun, Lingegowda S. Mangala, Xia Li, et al. 2015. “Augmentation of Response to Chemotherapy by microRNA-506 Through Regulation of RAD51 in Serous Ovarian Cancers.” J Natl Cancer Inst 107 (July). doi:10.1093/jnci/djv108. Cite
Turner, K. M., Y. Sun, P. Ji, K. J. Granberg, B. Bernard, L. Hu, D. E. Cogdell, et al. 2015. “Genomically Amplified Akt3 Activates DNA Repair Pathway and Promotes Glioma Progression.” Proceedings of the National Academy of Sciences of the United States of America 112 (11): 3421–26. Cite
Li, X., Y. Liu, K. J. Granberg, Q. Wang, L. M. Moore, P. Ji, J. Gumin, et al. 2015. “Two Mature Products of MIR-491 Coordinate to Suppress Key Cancer Hallmarks in Glioblastoma.” Oncogene 34 (March): 1619–28. doi:10.1038/onc.2014.98. Cite
Glusman, G., A. Severson, V. Dhankani, M. Robinson, T. Farrah, D. E. Mauldin, A. B. Stittrich, et al. 2015. “Identification of Copy Number Variants in Whole-Genome Data Using Reference Coverage Profiles.” Frontiers in Genetics 6: 45. Cite


Liu, Yuexin, Lalit Patel, Gordon B. Mills, Karen H. Lu, Anil K. Sood, Li Ding, Raju Kucherlapati, et al. 2014. “Clinical Significance of CTNNB1 Mutation and Wnt Pathway Activation in Endometrioid Endometrial Carcinoma.” J Natl Cancer Inst 106 (September). doi:10.1093/jnci/dju245. Cite
Kang, Seunghwa, Simon Kahan, Jason McDermott, Nicholas Flann, and Ilya Shmulevich. 2014. “Biocellion: Accelerating Computer Simulation of Multicellular Biological System Models.” Bioinformatics, July. doi:10.1093/bioinformatics/btu498. Cite
Liu, G., Y. Sun, P. Ji, X. Li, D. Cogdell, D. Yang, B. C. Parker Kerrigan, et al. 2014. “MiR-506 Suppresses Proliferation and Induces Senescence by Directly Targeting the CDK4/6-FOXM1 Axis in Ovarian Cancer.” The Journal of Pathology 233 (3): 308–18. Cite
Knijnenburg, Theo A., Stephen A. Ramsey, Benjamin P. Berman, Kathleen A. Kennedy, Arian F. A. Smit, Lodewyk F. A. Wessels, Peter W. Laird, Alan Aderem, and Ilya Shmulevich. 2014. “Multiscale Representation of Genomic Signals.” Nature Methods 11 (June): 689–94. doi:10.1038/nmeth.2924. Cite
Kemp, C. J., J. M. Moore, R. Moser, B. Bernard, M. Teater, L. E. Smith, N. A. Rabaia, et al. 2014. “CTCF Haploinsufficiency Destabilizes DNA Methylation and Predisposes to Cancer.” Cell Reports, April. Cite
Li, X., Y. Liu, K. J. Granberg, Q. Wang, L. M. Moore, P. Ji, J. Gumin, et al. 2014. “Two Mature Products of MIR-491 Coordinate to Suppress Key Cancer Hallmarks in Glioblastoma.” Oncogene, April. Cite
Ruusuvuori, P., J. Lin, A. C. Scott, Z. Tan, S. Sorsa, A. Kallio, M. Nykter, O. Yli-Harja, I. Shmulevich, and A. M. Dudley. 2014. “Quantitative Analysis of Colony Morphology in Yeast.” BioTechniques 56 (1): 18–27. Cite
Guo, Fei, Brittany C. Parker Kerrigan, Da Yang, Limei Hu, Ilya Shmulevich, Anil K. Sood, Fengxia Xue, and Wei Zhang. 2014. “Post-Transcriptional Regulatory Network of Epithelial-to-Mesenchymal and Mesenchymal-to-Epithelial Transitions.” J Hematol Oncol 7: 19. doi:10.1186/1756-8722-7-19. Cite


Brennan, Cameron W., Roel G. W. Verhaak, Aaron McKenna, Benito Campos, Houtan Noushmehr, Sofie R. Salama, Siyuan Zheng, et al. 2013. “The Somatic Genomic Landscape of Glioblastoma.” Cell 155 (October): 462–77. doi:10.1016/j.cell.2013.09.034. Cite
Heinäniemi, Merja, Matti Nykter, Roger Kramer, Anke Wienecke-Baldacchino, Lasse Sinkkonen, Joseph Xu Zhou, Richard Kreisberg, Stuart A. Kauffman, S. Huang, and Ilya Shmulevich. 2013. “Gene-Pair Expression Signatures Reveal Lineage Control.” Nat Methods 10 (June): 577–83. doi:10.1038/nmeth.2445. Cite
Mirzaei, H., T. A. Knijnenburg, B. Kim, M. Robinson, P. Picotti, G. W. Carter, S. Li, et al. 2013. “Systematic Measurement of Transcription Factor-DNA Interactions by Targeted Mass Spectrometry Identifies Candidate Gene Regulatory Proteins.” Proceedings of the National Academy of Sciences of the United States of America 110 (9): 3645–50. Cite
Yang, D., Y. Sun, L. Hu, H. Zheng, P. Ji, C. V. Pecot, Y. Zhao, et al. 2013. “Integrated Analyses Identify a Master MicroRNA Regulatory Network for the Mesenchymal Subtype in Serous Ovarian Cancer.” Cancer Cell 23 (2): 186–99. Cite
Larjo, A., I. Shmulevich, and H. Lahdesmaki. 2013. “Structure Learning for Bayesian Networks as Models of Biological Networks.” Methods in Molecular Biology 939: 35–45. Cite
Lin, Jake, Richard Kreisberg, Aleksi Kallio, Aimée M. Dudley, Matti Nykter, Ilya Shmulevich, Patrick May, and Reija Autio. 2013. “POMO–Plotting Omics Analysis Results for Multiple Organisms.” BMC Genomics 14: 918. doi:10.1186/1471-2164-14-918. Cite
Sadot, A., S. Sarbu, J. Kesseli, H. Amir-Kroll, W. Zhang, M. Nykter, and I. Shmulevich. 2013. “Information-Theoretic Analysis of the Dynamics of an Executable Biological Model.” PLoS One 8 (3): e59303. Cite


Bressler, R., J. Lin, A. Eakin, T. Robinson, R. Kreisberg, H. Rovira, T. Knijnenburg, J. Boyle, and I. Shmulevich. 2012. “Fastbreak: A Tool for Analysis and Visualization of Structural Variations in Genomic Data.” EURASIP Journal on Bioinformatics & Systems Biology 2012 (1): 15. Cite
Liu, G., D. Yang, Y. Sun, I. Shmulevich, F. Xue, A. K. Sood, and W. Zhang. 2012. “Differing Clinical Impact of BRCA1 and BRCA2 Mutations in Serous Ovarian Cancer.” Pharmacogenomics 13 (13): 1523–35. Cite
Koboldt, D. C., R. S. Fulton, M. D. McLellan, H. Schmidt, J. Kalicki-Veizer, J. F. McMichael, L. L. Fulton, et al. 2012. “Comprehensive Molecular Portraits of Human Breast Tumours.” Nature, September. Cite
Moore, L. M., V. Kivinen, Y. Liu, M. Annala, D. Cogdell, X. Liu, C. G. Liu, et al. 2012. “Transcriptome and Small RNA Deep Sequencing Reveals Deregulation of miRNA Biogenesis in Human Glioma.” The Journal of Pathology, September. Cite
Bernard, B., V. Thorsson, H. Rovira, and I. Shmulevich. 2012. “Increasing Coverage of Transcription Factor Position Weight Matrices through Domain-Level Homology.” PloS One 7 (8): e42779. Cite
Dougherty, E. R., and I. Shmulevich. 2012. “On the Limitations of Biological Knowledge.” Current Genomics 13 (7): 574–87. Cite
Liu, Y., Y. Sun, R. Broaddus, J. Liu, A. K. Sood, I. Shmulevich, and W. Zhang. 2012. “Integrated Analysis of Gene Expression and Tumor Nuclear Image Profiles Associated with Chemotherapy Response in Serous Ovarian Carcinoma.” PloS One 7 (5): e36383. Cite


Knijnenburg, T. A., J. Lin, H. Rovira, J. Boyle, and I. Shmulevich. 2011. “EPEPT: A Web Service for Enhanced P-Value Estimation in Permutation Tests.” BMC Bioinformatics 12 (1): 411. Cite
Yang, D., S. Khan, Y. Sun, K. Hess, I. Shmulevich, A. K. Sood, and W. Zhang. 2011. “Association of BRCA1 and BRCA2 Mutations with Survival, Chemotherapy Sensitivity, and Gene Mutator Phenotype in Patients with Ovarian Cancer.” JAMA : The Journal of the American Medical Association 306 (14): 1557–65. Cite
Ratushny, A. V., I. Shmulevich, and J. D. Aitchison. 2011. “Trade-off between Responsiveness and Noise Suppression in Biomolecular System Responses to Environmental Cues.” PLoS Computational Biology 7 (6): e1002091. Cite
Falconnet, D., A. Niemisto, R. J. Taylor, M. Ricicova, T. Galitski, I. Shmulevich, and C. L. Hansen. 2011. “High-Throughput Tracking of Single Yeast Cells in a Microfluidic Imaging Matrix.” Lab Chip 11 (3): 466–73. Cite
Knijnenburg, T. A., O. Roda, Y. Wan, G. P. Nolan, J. D. Aitchison, and I. Shmulevich. 2011. “A Regression Model Approach to Enable Cell Morphology Correction in High-Throughput Flow Cytometry.” Molecular Systems Biology 7: 531. Cite


Erkkila, T., S. Lehmusvaara, P. Ruusuvuori, T. Visakorpi, I. Shmulevich, and H. Lahdesmaki. 2010. “Probabilistic Analysis of Gene Expression Measurements from Heterogeneous Tissues.” Bioinformatics 26 (20): 2571–77. Cite
Ramsey, S. A., T. A. Knijnenburg, K. A. Kennedy, D. E. Zak, M. Gilchrist, E. S. Gold, C. D. Johnson, et al. 2010. “Genome-Wide Histone Acetylation Data Improve Prediction of Mammalian Transcription Factor Binding Sites.” Bioinformatics 26 (17): 2071–75. Cite
Yang, D., A. Ylipaa, J. Yang, K. Hunt, R. Pollock, J. Trent, O. Yli-Harja, I. Shmulevich, M. Nykter, and W. Zhang. 2010. “An Integrated Study of Aberrant Gene Copy Number and Gene Expression in GIST and LMS.” Technol Cancer Res Treat 9 (2): 171–78. Cite
Galas, D. J., M. Nykter, G. W. Carter, N. D. Price, and I. Shmulevich. 2010. “Biological Information as Set-Based Complexity.” IEEE Transactions on Information Theory 56 (2): 667–77. Cite
Burdick, D. B., C. C. Cavnor, J. Handcock, S. Killcoyne, J. Lin, B. Marzolf, S. A. Ramsey, et al. 2010. “SEQADAPT: An Adaptable System for the Tracking, Storage and Analysis of High Throughput Sequencing Experiments.” BMC Bioinformatics 11: 377. Cite
Saleem, R. A., R. Long-O’Donnell, D. J. Dilworth, A. M. Armstrong, A. P. Jamakhandi, Y. Wan, T. A. Knijnenburg, et al. 2010. “Genome-Wide Analysis of Effectors of Peroxisome Biogenesis.” PLoS OnePLoS ONE 5 (8). Cite


Knijnenburg, T. A., L. F. Wessels, M. J. Reinders, and I. Shmulevich. 2009. “Fewer Permutations, More Accurate P-Values.” Bioinformatics 25 (12): i161–68. Cite
Hu, L., W. Hittelman, T. Lu, P. Ji, R. Arlinghaus, I. Shmulevich, S. R. Hamilton, and W. Zhang. 2009. “NGAL Decreases E-Cadherin-Mediated Cell-Cell Adhesion and Increases Cell Motility and Invasion through Rac1 in Colon Carcinoma Cells.” Lab Invest 89 (5): 531–48. Cite
Litvak, V., S. A. Ramsey, A. G. Rust, D. E. Zak, K. A. Kennedy, A. E. Lampano, M. Nykter, I. Shmulevich, and A. Aderem. 2009. “Function of C/EBPdelta in a Regulatory Circuit That Discriminates between Transient and Persistent TLR4-Induced Signals.” Nat Immunol 10 (4): 437–43. Cite
Nykter, M., H. Lahdesmaki, A. Rust, V. Thorsson, and I. Shmulevich. 2009. “A Data Integration Framework for Prediction of Transcription Factor Targets.” Ann N Y Acad Sci 1158 (March): 205–14. Cite
Taylor, R. J., D. Falconnet, A. Niemisto, S. A. Ramsey, S. Prinz, I. Shmulevich, T. Galitski, and C. L. Hansen. 2009. “Dynamic Analysis of MAPK Signaling Using a High-Throughput Microfluidic Single-Cell Imaging Platform.” Proc Natl Acad Sci U S A 106 (10): 3758–63. Cite
Boyle, J., H. Rovira, C. Cavnor, D. Burdick, S. Killcoyne, and I. Shmulevich. 2009. “Adaptable Data Management for Systems Biology Investigations.” BMC Bioinformatics 10: 79. Cite
Huang, A. C., L. Hu, S. A. Kauffman, W. Zhang, and I. Shmulevich. 2009. “Using Cell Fate Attractors to Uncover Transcriptional Regulation of HL60 Neutrophil Differentiation.” BMC Syst Biol 3: 20. Cite
Selinummi, J., P. Ruusuvuori, I. Podolsky, A. Ozinsky, E. Gold, O. Yli-Harja, A. Aderem, and I. Shmulevich. 2009. “Bright Field Microscopy as an Alternative to Whole Cell Fluorescence in Automated Analysis of Macrophage Images.” PLoS OnePLoS ONE 4 (10): e7497. Cite
Shmulevich, I., and J. D. Aitchison. 2009. “Deterministic and Stochastic Models of Genetic Regulatory Networks.” Methods Enzymol 467: 335–56. Cite


Lee, W. W., D. Cui, M. Czesnikiewicz-Guzik, R. Z. Vencio, I. Shmulevich, A. Aderem, C. M. Weyand, and J. J. Goronzy. 2008. “Age-Dependent Signature of Metallothionein Expression in Primary CD4 T Cell Responses Is due to Sustained Zinc Signaling.” Rejuvenation Res 11 (6): 1001–11. Cite
Aderem, A., and I. Shmulevich. 2008. “Taming Data.” Cell Host Microbe 4 (4): 312–13. Cite
Ramsey, S. A., S. L. Klemm, D. E. Zak, K. A. Kennedy, V. Thorsson, B. Li, M. Gilchrist, et al. 2008. “Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics.” PLoS Comput Biol 4 (3): e1000021. Cite
Nykter, M., N. D. Price, M. Aldana, S. A. Ramsey, S. A. Kauffman, L. E. Hood, O. Yli-Harja, and I. Shmulevich. 2008. “Gene Expression Dynamics in the Macrophage Exhibit Criticality.” Proc Natl Acad Sci U S A 105 (6): 1897–1900. Cite
Nykter, M. et al. Critical networks exhibit maximal information diversity in structure-dynamics relationships. Phys Rev Lett 100, 058702 (2008). Cite
Balleza, E. et al. Critical dynamics in genetic regulatory networks: examples from four kingdoms. PLoS OnePLoS ONE 3, e2456 (2008). Cite
Boyle, J., Cavnor, C., Killcoyne, S. & Shmulevich, I. Systems biology driven software design for the research enterprise. BMC Bioinformatics 9, 295 (2008). Cite
Kauffman, S. et al. Propagating organization: an enquiry. Biology and Philosophy 23, 27–45 (2008). Cite
Korb, M. et al. The Innate Immune Database (IIDB). BMC Immunol 9, 7 (2008). Cite
Lahdesmaki, H., Rust, A. G. & Shmulevich, I. Probabilistic inference of transcription factor binding from multiple data sources. PLoS OnePLoS ONE 3, e1820 (2008). Cite
Liu, W., Lahdesmaki, H., Dougherty, E. R. & Shmulevich, I. Inference of Boolean networks using sensitivity regularization. EURASIP J Bioinform Syst Biol 780541 (2008). Cite
Lähdesmäki, H. & Shmulevich, I. Learning the structure of dynamic Bayesian networks from time series and steady state measurements. Machine Learning 71, 185–217 (2008). Cite


Krawitz, P. & Shmulevich, I. Entropy of complex relevant components of Boolean networks. Phys Rev E Stat Nonlin Soft Matter Phys 76, 036115 (2007). Cite
Price, N. D. & Shmulevich, I. Biochemical and statistical network models for systems biology. Curr Opin Biotechnol 18, 365–70 (2007). Cite
Krawitz, P. & Shmulevich, I. Basin entropy in Boolean network ensembles. Phys Rev Lett 98, 158701 (2007). Cite
Price, N. D. et al. Highly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomas. Proc Natl Acad Sci U S A 104, 3414–9 (2007). Cite
Ahdesmaki, M., Lahdesmaki, H., Gracey, A., Shmulevich, I. & Yli-Harja, O. Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data. BMC Bioinformatics 8, 233 (2007). Cite
Niemisto, A. et al. A K-means segmentation method for finding 2-D object areas based on 3-D image stacks obtained by confocal microscopy. Conf Proc IEEE Eng Med Biol Soc 2007, 5559–62 (2007). Cite
Vencio, R. Z. & Shmulevich, I. ProbCD: enrichment analysis accounting for categorization uncertainty. BMC Bioinformatics 8, 383 (2007). Cite
Vencio, R. Z., Varuzza, L., de, B. P. C. A., Brentani, H. & Shmulevich, I. Simcluster: clustering enumeration gene expression data on the simplex space. BMC Bioinformatics 8, 246 (2007). Cite


Jiang, R. et al. Pathway alterations during glioma progression revealed by reverse phase protein lysate arrays. Proteomics 6, 2964–71 (2006). Cite
Lahdesmaki, H., Hautaniemi, S., Shmulevich, I. & Yli-Harja, O. Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks. Signal Processing 86, 814–834 (2006). Cite
Nykter, M. et al. Unsupervised analysis uncovers changes in histopathologic diagnosis in supervised genomic studies. Technol Cancer Res Treat 5, 177–82 (2006). Cite
Niemisto, A. et al. Extraction of the number of peroxisomes in yeast cells by automated image analysis. Conf Proc IEEE Eng Med Biol Soc 1, 2353–6 (2006). Cite


Niemisto, A., Dunmire, V., Yli-Harja, O., Zhang, W. & Shmulevich, I. Analysis of angiogenesis using in vitro experiments and stochastic growth models. Phys Rev E Stat Nonlin Soft Matter Phys 72, 062902 (2005). Cite
Shmulevich, I., Kauffman, S. A. & Aldana, M. Eukaryotic cells are dynamically ordered or critical but not chaotic. Proc Natl Acad Sci U S A 102, 13439–44 (2005). Cite
Brun, M., Dougherty, E. R. & Shmulevich, I. Steady-state probabilities for attractors in probabilistic Boolean networks. Signal Processing 85, 1993–2013 (2005). Cite
Gershenson, C., Kauffman, S. A. & Shmulevich, I. The role of redundancy in the robustness of random Boolean networks. Arxiv preprint nlin/0511018 (2005). Cite
Niemisto, A., Shmulevich, I., Yli-Harja, O., Chirieac, L. R. & Hamilton, S. R. Automated quantification of lymph node size and number in surgical specimens of stage II colorectal cancer. Conf Proc IEEE Eng Med Biol Soc 6, 6313–6 (2005). Cite
Toulouse, T., Ao, P., Shmulevich, I. & Kauffman, S. Noise in a Small Genetic Circuit that Undergoes Bifurcation. Complexity 11, 45–51 (2005). Cite