Igor Jurisica

Picture of Dr. Igor Jurisica


PhD, University of Toronto

Krembil Research Tower,
60 Leonard Avenue, Room KDT 5KD-407
Toronto ON  M5T 0S8

Phone: (416) 581-7437
Email Dr. Igor Jurisica




Integrative Computational Biology

Merely coping with the deluge of data is no longer an option; their systematic analysis is a necessity in biomedical research.

Computational biology is concerned with developing and using techniques from computer science, informatics, mathematics, and statistics to solve biological problems. Analyzing biomedical data requires robust approaches that deal with high dimensionality, multi-modal and rapidly evolving representations, missing information, ambiguity and uncertainty, noise, and incompleteness of domain theories.

Our research is focussed on integrative computational biology, and representation, analysis and visualization of high dimensional data generated by high-throughput biology experiments, in the context of cancer informatics. Of particular interest is the use of comparative analysis for the mining of integrated different datasets such as protein-protein interaction, gene expression profiling, and high-throughput screens for protein crystallization. We integrate multiple data sources and database, develop and apply combination of diverse algorithms in a systematic manner. Besides performance, our focus is on scalable algorithms with easy use by non-experts.

Truly understanding biological systems requires the integration of data across multiple high-throughput platforms. It has been established that despite inherent noise present in proteinprotein interaction data sets, systematic analysis of resulting networks uncovers biologically relevant information, such as lethality, functional organization, hierarchical structure, dynamic modularity and network-building motifs. These results suggest that protein interaction networks have a strong structure-function relationship, which we use to help interpret integrated cancer profile data. Focusing on network analysis and modeling, integrated with cancer profiles will enable us to identify diagnostic and prognostic biomarkers, understand disease initiation and progression, which will lead to improving cancer treatment. Tools, such as BTSVQ, I2D ( http://ophid.utoronto.ca/i2d ), and NAViGaTOR ( http://ophid.utoronto.ca/navigator ) enable users to interpret integrated cancer profiles, and create relevant models dynamically. Many targets discovered using approaches described above will lead to uncharacterized proteins. To further our understanding of their function, we may use X-ray crystallography or NMR to determine their 3D structure.

Protein crystallization is a major bottleneck in highthroughput structure determination, partially due to many parameters affecting the crystallization outcome (e.g., purity of proteins, intrinsic physico-chemical, biochemical, biophysical and biological parameters), and the unknown correlations between the parameter and the propensity for a given protein to crystallize. Protein crystallization has two phases: search and optimization. High-throughput screening (HTS) can speed up the search phase, and has the potential to increase process quality. To achieve these benefits, we work on developing a sophisticated automated image classification algorithms, data mining and reasoning approaches to optimize protein crystallization plans, improve our understanding of the crystallization process, and increase a number of determined disease-related protein structures ( http://www.cs.toronto.edu/~juris/WCG /).

For further information, please go to http://www.cs.toronto.edu/~juris/


List of Key Publications:

Link to Pubmed Publications
  • Gortzak-Uzan, L., Ignatchenko, A., Evangelou, A., Agochiya, M., Brown, K., St.Onge, P., Kireeva, I., Schmitt-Ulms, G., Brown, T., Murphy, J., Rosen, B., Shaw, P., Jurisica, I., Kislinger, T. A Proteome Resource of Ovarian Cancer Ascites: Integrated Proteomic and Bioinformatic Analyses To Identify Putative Biomarkers. J Proteome Res (2007).

  • Brown, K. and I. Jurisica. Unequal evolutionary conservation of human protein interactions in interologous networks. Genome Biology,8(5), 2007.

  • Przulj, N, D. G. Corneil, I. Jurisica. Efficient estimation of graphlet frequency distributions in protein-protein interaction networks. Bioinformatics, 22(8):974-980, 2006.

  • Cumbaa, C. A. and I. Jurisica. Automatic classification and pattern discovery in high-throughput protein crystallization trials, Journal of Structural and Functional Genomics,6(2-3):195-202, 2005.


Graduate Students:

  • Maryam Salehi
  • Sara Rahmati