CISE News & Events
Assistant Professor Tamer Kahveci receives the prestigious NSF CAREER Award
March 26th, 2009
Just like every living organism, countless molecules in our body go through an exciting journey on which they interact with and change each other. When, where, how, and how much they interact affects how well your body functions. All these interactions are governed by the pathways determined by our genes.
Tamer Kahveci, an assistant professor in the CISE Department received the prestigious NSF CAREER Award for his research project titled "New technologies for querying pathway databases." The CAREER program recognizes and supports early career-development activities of teacher-scholars who are most-likely to become the academic leaders of the 21st century, according to the NSF website. The award provides $400,000 over a five year period. This brings the Department's number of CAREER awards to two digits.
Genes interact with each other directly by suppressing or activating each other or indirectly by altering the molecules each other help create. These interactions enable them to collaborate and serve functions they can not perform individually. The coffee you drank and the breakfast you had will stimulate parts of this network of interactions and maybe start, accelerate or slow down the reactions in a sub-network within this network.
Dr. Kahveci's research group is bringing a new computational perspective to understanding how organisms function through a complex network of interactions. The first step in this direction is to understand how these interactions can be modeled to formulate the functions of sub-networks. Modifying or simply knocking out a sub-network can have a butterfly effect on the rest of the network. The possibility of having such an effect depends on how that sub-network interacts with the rest of the network, the current state of the network and the state of the external stimulants. Dr. Kahveci's lab is developing efficient and scalable computational methods to compute or approximate this effect as a function of the steady state of the network.
Assume that somehow your biological network is altered. It may be because of an external stimulant such as the medicine your doctor gave or may be because the activity levels of some of your genes have changed. Clearly, this can change how your body functions greatly as it interferes with the biological network. Now imagine this in an inverse scenario. I want to change how an organism functions for some reason. For example, maybe I want to increase the production of fatty acids that are used in the cosmetics industry in creams and lotions. These molecules can be obtained from micro organisms. How can I alter the genes of these organisms to optimize the production of the molecules that are profitable for me? Dr. Kahveci's research lays the foundations to develop computational methods that can predict the most promising genetic alterations that will create desirable mutant organisms.
It is more practical to study and experiment some organisms more than the others. For instance, it is cheaper to perform experiments on bacteria than rat. Also, it is preferable to study new drugs on rats than humans. Assume that an experiment is performed on rats and its impacts on the rat metabolism are measured. What kind of measurements would we get if we have done the same experiment on human? Clearly, there is great value in computationally predicting this before braving it on human. Comparing the biological networks of the two organisms can reveal their similar regions. It can help in predicting the effect of an external stimulant on an organism when we know its effect on the other one. Alignment of two pathways, a fundamental problem in pathway analysis, seeks a mapping between the entities of the two pathways. Ideally the mapping should bring the similar parts of the two pathways together. Dr. Kahveci’s research group is developing novel algorithms and software that can align large biological networks of any type considering their homological, topological and functional similarities. They are also addressing the problem of finding such similarities in a large database of biological networks with the help of feature and reference based indexing techniques.
Dr. Kahveci received his Ph.D. degree from the University of California, Santa Barbara and joined the CISE department of the University of Florida in 2004.
