2002 Merck-supported Participants
(Mentor: Dr. David Yaron)
Partitioning Correlation Energy in Molecular Subsystems
(Mentor: Dr. Nathan Urban)
Lateral inhibition in an olfactory bulb model
The goal of the project was to determine the nature of the functional transformation taking place in the olfactory bulb by constructing a computational model which would allow for the virtually unlimited simulation of odorant exposures and facilitate an in-depth analysis of the olfactory bulb as an olfactory information processor. Physiological data from the mouse olfactory bulb was used to construct this computational model, which was composed of leaky integrate-and-fire (i.e. spiking) neurons with spikes time-distributed according to an alpha function. The behavior of the model paralleled data recorded from cells in the olfactory bulb, exhibiting oscillatory activity, spontaneous spiking, and temporally evolving patterns of activation over the mitral cells. The lateral inhibition in the model was also found to cause time-dependent decorrelation of the responses of mitral cells to similar odors over the course of an odorant exposure, a behavior observed in the zebrafish olfactory bulb. Such decorrelation may allow similar odorants to be more easily distinguished.
(Mentors: Drs. Tom Mitchell and Robert F. Murphy)
Caption Interpretation from Online Biological Journal Articles
However, currently the system lacks the feature of understanding the relationship between a caption and the panels of its figure from any biological online journal article. Besides analyzing the pixels of a cell image directly, by interpreting its caption, the image could be understood to some extent as well. Therefore, an automated caption interpreter that can map cell types and protein names in a given caption to panel labels of the figure of that caption was developed.
One challenging task for developing the caption interpreter is the extraction of cell type and protein names from the caption. However, the most challenging task is to be able to understand the caption and identify the mapping from the extracted names to panels in the figure.
The caption interpreter shows a high precision for identifying cell type names and a reasonable precision for identifying protein names. In addition, it maps the cell type and protein names to panel labels by intelligently assigning portions of text in a caption for each label and has achieved a satisfying precision as well. Adding this interpreter into the knowledge base system should improve the precision of finding fluorescence microscope images depicting particular subcellular patterns.
(Mentor: Dr. David Yaron)
Trying Trimers: Extension on Aggregates of Cyanine Dye Molecules in the Minor Groove of DNA