High throughput technologies and statistical analyses have transformed the way biological research is performed. These technologies accomplish tasks that were labeled as science fiction only 20 years ago - identifying millions of genetic variations in a genome, a chip that measures expression levels of all genes, quantifying the concentration of dozens of proteins at a single cell resolution. High-throughput genome-wide approaches allowed us, for the first time, to perform unbiased research that doesn't depend on existing knowledge. Thanks to these new technologies, we now have a much better understanding on what goes awry in cancer, what are the genetic predispositions for numerous diseases, and how to select the best available treatment for each patient based on his/her genetic and genomic features.
The emergence of new technologies, however, also introduced many new problems that need to be addressed in order to fully exploit the information within the data. Tasks start with data normalization and artifact identification, continue with how to properly model the data using statistical tools, and end with the suitable ways to translate those statistical results into informative and correct biological insights. A new field - computational biology - was emerged to address those problems and bridge the gap between statistics and biology.
Here I present 3 studies on computational modeling of heterogeneity and context-specificity in biological systems. My work focused on the identification of genomic features that can predict or explain a phenotype. In my studies of both yeast and cancer, I found vast heterogeneity between individuals that hampers the prediction power of many statistical models. I developed novel computational models that account for the heterogeneity and discovered that, in most cases, the relationship between the genomic feature and the phenotype is context-specific - genomic features explain, predict or exert influence on the phenotype in only a subset of cases.
In the first project I studied the landscape of genetic interactions in yeast using gene expression data. I found that roughly 80% of interactions are context-specific, where genetic mutations influence expression levels only in the context of other mutations. In the second project I used gene expression and copy number data to identify drivers of oncogenesis. By using gene expression as a phenotype, and by accounting for context-specificity, I identified two novel copy number drivers that were validated experimentally. In the third project I studied the transcriptional and phenotypic effects of MAPK pathway inhibition in melanoma. I show that most MAPK targets are context-specific - under the control of the pathway only in a subset of cell lines. A computational model I designed to detect context-specific interactions of the MAPK pathway identified the interferon pathway as a major player in the cytotoxic response of MAPK inhibition.
Taken together, my research demonstrates the importance of context-specificity in the analysis of biological systems. Context-specific computational modeling, combined with high-throughput technologies, is a powerful tool for dissecting biological networks.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D84T6GHB |
| Date | January 2014 |
| Creators | Litvin, Oren |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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