Back
to top

 

NAViGaTOR feature
Using Appearence filters it is possible to map Features to widths, heights, colours, transparency and many other visual attributes.

Learn more...

NAViGaTOR feature
In NAViGaTOR it is possible to load and manipulate networks with millions of connections.

Learn more...

Help Conquer Cancer
is our own project on the World Community Grid, which will help improve the results of protein X-ray crystallography and thereby improve understanding of cancer initiation, progression and treatment.

Learn more...

NAViGaTOR
is our software package for visualizing and analyzing protein-protein interaction networks. NAViGaTOR can query OPHID / I2D online databases of interaction data and display networks in 2D or 3D.

Learn more...

NAViGaTOR example
Savas, S., Geraci, J., Jurisica, I., Liu, G. A comprehensive catalogue of functional genetic variations in the EGFR pathway: Protein-protein interaction analysis reveals novel genes and polymorphisms important for cancer research. Int J Cancer,125(6): 1257-65, 2009.

Learn more...

NAViGaTOR example
Savas, S., Geraci, J., Jurisica, I., Liu, G. A comprehensive catalogue of functional genetic variations in the EGFR pathway: Protein-protein interaction analysis reveals novel genes and polymorphisms important for cancer research. Int J Cancer,125(6): 1257-65, 2009.

Learn more...

NAViGaTOR example
Showing data from Cell paper, Fig 5 Global Sequencing of Proteolytic Cleavage Sites in Apoptosis by Specific Labeling of Protein N Termini

Learn more...

NAViGaTOR example
Cox, B., Kotlyar, M., Evangelou, A., Ignatchenko, V., Ignatchenko, A., Whiteley, K., Jurisica, I., Adamson, L., Rossant, J., Kislinger, T., Comparative systems biology of human and mouse as a tool for modeling human placental pathology, Mol Sys Bio, 5, 279, 2009.
Learn more...

NAViGaTOR example
Agarwal R., Jurisica, I., Cheng K.W., Mills G.B. The emerging role of the Rab25 small GTPase in cancer, Traffic, 2009. E-Pub July 23, 2009. In Press.

Learn more...

News and Events

CGEP project update

WCG HCC project update

SCRIPDB database published: Heifets, A. and Jurisica, I.. SCRIPDB: A portal for easy access to syntheses, chemicals, and reactions in patents. Nucl Acid Res, In press.

NetwoRx database application published: Fortney, K., Morgen, E. K., Kotlyar, M., Jurisica, I. In silico drug screen in mouse liver identifies candidate calorie restriction mimetics. Rejuvenation Res, In press.

Read more news...

 

 

Lab Affiliations

 

Welcome to Jurisica Lab!

Igor Jurisica

Our research focuses on integrative computational biology in the context of Cancer Informatics.

It involves representation, analysis and visualization of high dimensional data generated by high-throughput biology experiments.

The laboratory is located at Ontario Cancer Institute, Princess Margaret Hospital.

Learn more about us...

Research Areas

CGEP: Cancer Gene Encyclopedia: Computationally optimized characterization of cancer genes, proteins, their structure, function and interactions
In collaboration with UofT (Bader, Morris) OICR (Stein), and many leading cancer researchers we work on integrating and expanding the best tools, resources and approaches available for gene function annotation, protein-protein interaction prediction, curation and biological validation, pathway curation and functional validation to achieve comprehensive, more economical and effective characterization of cancer targets, their related interactions and pathways.
Learn more...

 

 

Visualization and Analysis of protein-protein interaction networks

Visualization and Analysis of protein-protein interaction networks
We develop novel graph theory based algorithms for visualization and systematic analysis of protein-protein interaction networks.
Learn more...

 

High-throughput Protein Crystallography

High-throughput Protein Crystallography
We focus on image analysis methods for automatically detecting the presence of crystals in crystallization screens, data mining of resulting information and optimization.
Learn more...

 

Genomic and Proteomic Cancer Profiles Analysis

Genomic and Proteomic Cancer Profiles Analysis
We are involved in developing and applying tools for analysis of genomic/proteomic cancer profiles including gene expression, CGH, protein expression, siRNA, miRNA.
Learn more...

 

About Us

The primary research focus is on integrative computational biology, and representation, analysis and visualization of high-dimensional data generated by high-throughput biology experiments. Of particular interest is the use of comparative analysis in the mining of different dataset types such as protein-protein interaction, gene/protein expression profiling of cancer, and high-throughput screens for protein crystallization.

Intelligent molecular medicine
Technologies to measure gene and protein expression offer the opportunity to evaluate large sets of genes and proteins in parallel, and improve our understanding of tumorigenesis and patient treatment. However, molecular screening alone is not sufficient to achieve intelligent molecular medicine. Computational advances and computing power to analyze, manage and use genomic/proteomic information is required to turn data into knowledge for hypotheses generation for further research or to render them readily comprehensible for patient outcome prediction and treatment selection. To be used effectively, molecular profiling must be applied at the individual patient level to allow personalized information-based medicine. Our focus is on algorithm and tools development, their application and evaluation.

Many techniques for the analysis of genomic/proteomic data are available, yet none offers an integrated and comprehensive approach, by combining results from gene/protein expression data in the context of protein protein interactions (PPIs). We address this bottleneck in multiple cancers by systematic, unbiased analysis and visualization of data integrated from multiple high-throughput platforms under the hypothesis that such information will create insight not appreciable from the component parts.

The results of this research will help to fathom biological mechanisms of cancer, and will be applicable to improve disease classification, diagnostic measures, therapy planning, and treatment prognosis. Improving the treatment could in turn improve quality of life for cancer patients. Using the proposed tools and methodology, physicians will have more relevant information available at the time of diagnosis and treatment planning, and the patient will have a better explanation of the disease, its origin, progression path and treatment alternatives.

Structure-function relationship in protein interaction networks
It has been established that despite inherent noise present in protein-protein interaction (PPI) data sets, systematic analysis of resulting networks uncovers biologically relevant information, such as lethality, functional organization, hierarchical structure and network-building motifs. These results suggest that PPI networks have strong structure-function relationship. We are developing novel graph theory based algorithms for systematic analysis of PPI networks (both predicted and experimentally determined). We use this information to build predictive models and to integrate this information with gene/protein expression profiles.

High-throughput protein crystallization
One of the fundamental challenges in modern molecular biology is the elucidation and understanding of the rules by which proteins adopt their three-dimensional structure. Currently, the most powerful method for protein structure determination is single crystal X-ray diffraction, although new breakthroughs in NMR and in silico approaches are growing in their importance.

Conceptually, protein crystallization can be divided into two phases: search and optimization. Approximate crystallization conditions are identified during the search phase, while the optimization phase varies these conditions to ultimately yield high quality crystals. Robotic protein crystallization screening can speedup the search phase, and has a potential to increase process quality. However, this requires an automated process for evaluating experiment results. We focus on automated image classification, data mining of resulting information when integrated with protein properties, using the information for crystallization optimization planning and screen optimization.