| Comparative analysis of gene expression Barkai's lab We are developing computational methods for comparative analysis of expression networks from diverse organisms. These methods rely on comparison of the co-expression relationships between genes rather than their expression levels. Several previous studies in our lab have utilized this approach and they form the basis for further analysis. We will extend this approach for simultaneous analysis of multiple organisms, and relate the changes in network structure to known physiological differences and evolutionary events. For example, we will attempt to identify global expression differences which reflect the emergence of multicellularity or are unique to mammals. In addition, the methods that we are developing for analysis of gene expression will be applicable for other comparative analyses of biological data. We will extend these approaches to provide a general comparative tool and examine its utility for integration of heterogeneous datasets, a task which stands at the heart of bioinformatics. Environmental versus growth dependent modulation of gene expression The interpretation of gene expression data is often complicated by the need to distinguish between the direct effects of the environmental perturbation and the general effects of altering cell growth. We wish to characterize gene expression in live cells growing in a well-controlled growth environment. To this end, we have setup a chemostat system, where cellular conditions can be controlled and monitored at great accuracy. Cells growing at steady state conditions were subject to a variety of perturbations, and their genome-wide expression and growth rate were monitored simultaneously. This allows us to identify the gene groups whose expression is linked to growth, vs. those that respond directly to environmental perturbation. In particular, we are interested in the pattern of the general stress response (ESR) and of rRNA processing genes, which show unexpected behavior.In a second project, we are planning to study genetic adaptation to perturbation over evolutionary time scales. Specifically, we would test the hypothesis evolution in a fluctuating environment leads to a robust network design. This would be done by comparing the evolution of cells grown in a constant environment to that of cells subject to fluctuating environments. References acknowledging Kahn's project: 1. Ihmels J, Bergmann S, Berman J, Barkai N (2005) Comparative gene expression analysis by differential clustering approach: application to the Candida albicans transcription program. PLoS Genet 1: e39. 2. Ihmels J, Bergmann S, Gerami-Nejad M, Yanai I, McClellan M, et al. (2005) Rewiring of the yeast transcriptional network through the evolution of motif usage. Science 309: 938-940. 3. Bilu Y, Barkai N (2005) The design of transcription-factor binding sites is affected by combinatorial regulation. Genome Biol 6: R103. 4. Tirosh I, Weinberger A, Carmi M, Barkai N. A genetic signature of interspecies variability in gene expression. Nature Genetics. In press. |
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