The Galitski lab works toward the mapping and modeling of complex genetic information networks. In particular, they are interested in the conserved network controlling fungal infectious disease. Fungal pathogens of humans (e.g, Candida albicans), animals, and crop plants commonly exhibit a dimorphism in which yeast-form round single cells can give rise to a pathogenic filamentous/invasive multicellular form. This cell differentiation involves changes in cell shape and size, budding pattern, cell cycle progression, adhesiveness, and the ability to invade a solid substrate or penetrate physical barriers. Specific environmental conditions (e.g, host tissues, nitrogen limitation) elicit this response in specific fungal cell types. The understanding and modeling of this complexity is a problem well suited to the systems-biology approach.
Research
Galitski and his team study the development of multicellular invasive filaments in Saccharomyces cerevisiae. A complex signal-transduction network controls yeast filamentation/invasion by integrating multiple cues in the environment and the cell´s genotype. These cues include carbon availability, nitrogen availability, a solid substrate, as well as genetic factors like ploidy and mating type. The goal is to computationally generate network models that suggest hypotheses about the control of the system´s properties. These hypotheses, in turn, drive the experimental work.
The raw materials for these models are masses of data of multiple types. Researchers produce mRNA-expression-profile time courses during the induction of filamentous/invasive development, while systematically varying environmental conditions and strain genotype. In addition, they develop methods for high-throughput genetics and quantitative phenotype assays to identify genetic interactions (epistasis) among hundreds of genes. This requires highly-parallel strain construction and phenotypic characterization.
To generate system-level hypotheses from the genome-scale data sets, Galitski and his team develop algorithms and software to visualize and analyze the global structure and function of the fungal pathogenesis network. This involves the integration of gene-expression data, protein interaction data, genetic interactions, and phenotype data in the form of graphical network models.
For more info on the lab's research, see the Galitski lab home page.
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