Duke University is awarded a grant from the Faculty Early Career Development program (CAREER) to develop an integrated framework for the analysis and interpretation of biological image data. Images have been long used in molecular and developmental biology as a means to document the outcome of experiments, but are increasingly seen as quantitative data and not just qualitative descriptions. With recent advances in microscopy technology, as well as means to visualize biological molecules, the growing amount of available data has turned images into a new data type for computational biology with new and exciting challenges and possibilities. In particular, microscopy allows for measuring gene expression patterns at high resolution and in living organisms. Algorithms to extract, represent, and compare spatial and temporal expression patterns from images are still in early stages, and are often tailored to a particular scenario. The key contribution of this project lies in a principled probabilistic framework which utilizes top-down generative strategies to extract samples from images, model gene expression patterns from microscopy data, and integrate image expression data with other genomic data to understand gene regulation. Close collaborations with biologists working on animal and plant model systems will ensure that the developed methods are widely applicable, and will allow for the targeted validation of specific model predictions.
The interdiscplinary nature of this proposal reaches across both research and education. In concert with the research program, a graduate course in computational biology will be expanded to include case study modules combining methodological background with hands-on examples to analyze primary research data. Topics will include genome annotation, gene regulation, and image analysis. To increase the impact of this effort, the PI will closely collaborate with the ongoing NSF iPlant initiative and teach at workshops for educators at the high school, undergraduate, and graduate level. The PI will also continue to participate in development and teaching of systems biology curricula for undergraduates and graduates spearheaded by the Duke Center for Systems Biology. Ongoing international efforts by the Center include the development of a platform to allow for an open sharing of teaching resources.
