Prof. Yanay Ofran is a senior lecturer in the Mina and Everard Goodman Faculty of Life Sciences, and a returning scientist from Columbia University.
Ofran’s interests include functional genomics and systems biology, and their interaction with cancer research and cancer patients.
Ofran’s team studies interactions between biological molecules from the level of the molecular interface to the organization of molecular networks.
They are looking for the molecular mechanisms that account for the control and dynamics of these networks. In order to better understand these networks, the research team is developing tools to predict, analyze and design protein function and interactions, including a special focus on understanding the principles that govern antibody-antigen binding.
Ofran’s research team uses a systems-based approach to cancer and cancer patients. Patients with identical diagnoses often respond differently to treatment and have very different prognosis.
Ofran’s team is examining the role of tiny genetic variations, known as Single Nucleotide Polymorphisms (SNPs), in these differences. SNPs are suggested to predispose individuals to disease and also to influence their response to drugs.
In the realm of cancer research, Ofran’s team is examining how specific SNP combinations are linked to tumors and treatment outcomes. Furthermore, analysis of SNP combinations and genomic data is being used to try and predict how patients will respond to various combinations of chemotherapeutic medications.
The functional genomic approaches used in cancer research can also be applied to other diseases. Much of the research in Ofran’s lab is based on the attempt to predict how certain molecular changes (such as mutations) will affect the molecular function of proteins.
Using clinical data collected from patients, the research team compares molecular interactions in disease to those observed in normal cells, and map data from patients to disease via the genome.
Ofran’s research team makes use of computational and experimental tools to study molecular interactions and their relationship to biological function and disease. The team is also developing tools to predict molecular interactions, and design molecules that can bind to specific biological macromolecules.
By integrating computational tools, experimental analysis and clinical data from patients, the research in the lab attempts to develop a computerized model that will predict how interferences in the activity of individual molecules (often referred to as perturbations) can affect the patients.
Such perturbations may be a drug, a mutation, or any combination of drugs and mutations. The long term vision of this attempt is a framework that will be able to predict the molecular outcome of giving a certain combination of drugs to a patient with a certain combination of SNPs.