Ana Pop will present her research seminar/general exam on Wednesday May 20 at 10AM in room 402. The members of her committee are: Olga Troyanskaya, advisor, David Blei, and Rob Schapire. Everyone is invited to attend her talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Her abstract and reading list follow below. ------ Abstract: ------ The availability of genome-scale data has enabled gene function prediction for different organisms. Computational approaches to this problem have aimed to integrate all this heterogeneous information. One method that has been applied with great success has been Bayesian data integration, which, given the genomic data and known functional relationships, can predict other functional relationships and organize the genomic information into functional relationship networks. In addition to understanding the function of genes we also aim to understand the progression of these genes' functions and interactions as the organism develops. Computational approaches have been applied mostly to simple organisms such as the very well-studied Saccharomyces cerevisiae (baker's yeast). More complex organisms, such as Arabidopsis thaliana (the model organism for plants), which is the focus of this project, are much more challenging to analyze as they are substantially more complex and have different tissues types and development stages. Therefore, this plant is ideal for a developmental gene functional networks study. We have created a global functional network for Arabidopsis where nodes are genes and links between the nodes represent a functional relationship (the link weight represents the probability of interaction). Analysis of this network showed that the top few hundred highest scoring functional relationships are between genes related to photosynthesis. In addition to the global network, we also have biological process context-specific networks for Arabidopsis, which shows gene functional relationships in different biological process contexts (such as cell cycle or DNA replication). We are also extending this model to determine developmental progression of gene function both globally and in various development-stage contexts. We expect that overlaying the biological process context-specific networks with the development stage context-specific networks will shed light on the way gene function progresses over the different developmental stages of the plant. ------ Reading List: ------ [Book] Artificial Intelligence: A Modern Approach (Second Edition) by Stuart Russell and Peter Norvig - section V (Uncertain knowledge and reasoning) - section VI (Learning) [1] C Huttenhower, O Troyanskaya; Bayesian data integration: a functional perspective; Comput. Syst. Bioinformatics, 2006 [2] Guan Y, Myers CL, Hess DC, Barutcuoglu Z, Caudy AA, Troyanskaya OG: Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biol 2008, 9(suppl 1):S3. [3] Curtis Huttenhower, Erin M. Haley, Matthew A. Hibbs, Vanessa Dumeaux, Daniel R. Barrett, Hilary A. Coller, and Olga G. Troyanskaya. Exploring the human genome with functional maps, Genome Research 2009 [4] S Rogers, M Girolami, A Bayesian regression approach to the inference of regulatory networks from gene expression data., Bioinformatics, Vol. 21, No. 14. (15 July 2005), pp. 3131-3137 [5] Paul Pavlidis, Jason Weston, Jinsong Cai and William Noble Grundy, Learning gene functional classifications from multiple data types, Journal of Computational Biology. 9(2):401-411, 2002 [6] Quackenbush J., Computational analysis of microarray data., Nat Rev Genet. 2001 Jun;2(6):418-27 [7] Brendan J. Frey and Delbert Dueck, Clustering by Passing Messages Between Data Points, (11 January 2007) Science [8] Alter O, Brown PO, Botstein D (2000) Singular value decomposition for genomewide expression data processing and modeling. Proc Natl Acad Sci USA 97:10101-10106. [9] Boyes et al., 2001 D.C. Boyes, A.M. Zayed, R. Ascenzi, A.J. McCaskill, N.E. Hoffman and K.R. Davis, Growth stage-based phenotypic analysis of Arabidopsis: a model for high throughput functional genomics in plants, Plant Cell 13 (2001), pp. 1499-1510. [10] S.M. Brady et. al. A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 318 (2007), 801-806 [11] Anjali Iyer-Pascuzzi, June Simpson, Luis Herrera-Estrella, Philip N Benfey, Functional genomics of Arabidopsis root functional genomics, Current Opinion in Plant Biology, In Press, Corrected Proof, Available online 29 December 2008