9:30 am Thursday, May 3, 2007
Introduction to research at U.T. Austin : Discovery of Principles of Nature from Mathematical Modeling of
DNA Microarray Data by Orly Alter (Department of Biomedical Engineering,Institute for Cellular and Molecular Biology and
Institute for Computational Engineering and Sciences) in RLM 12.166
DNA microarrays make it possible to record the complete genomic signals that guide the progression of cellular processes. Future predictive power, discovery and control in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment and drug development. I will describe the first data-driven models that were created from these large-scale data through generalizations of matrix and tensor computations that have proven successful in describing the physical world. In these models, the mathematical variables and operations might represent biological reality: The variables, patterns uncovered in the data, might correlate with activities of cellular elements, such as regulators or transcription factors, that drive the measured signals. The operations, such as data classification and reconstruction in subspaces of selected patterns, might simulate experimental observation of the correlations and possibly also causal coordination of these activities [1--3]. I will illustrate these models in comparative and integrative analyses of mRNA expression and proteins' DNA-binding data from yeast and human cell cultures. In these analyses, the ability of the models to predict previously unknown biological and physical principles is demonstrated with a prediction of a novel mechanism of regulation that correlates DNA replication initiation with RNA transcription. The predicted mechanism is in agreement with current biological understanding, and is supported by recent experimental results [4]. I will also illustrate these models in the analysis of yeast genome- scale mRNA lengths distribution data measured with DNA microarrays. SVD uncovers in thse data "asymmetric Hermite functions,"a generalization of the eigenfunction of the quantum harmonic oscillator. These patterns of mRNA abundance levels across gel migration lengths might be explained by a previously undiscovered asymmetry in RNA gel electrophoresis thermal band broadening [5]. These models may become the foundation of a future in which biological systems are modeled and controlled as physical systems are today [6]. 1. Alter, Brown & Botstein, PNAS 97, 10101 (2000); http://www.pnas.org/cgi/content/abstract/97/18/10101 2. Alter, Brown & Botstein, PNAS 100, 3351 (2003); http://dx.doi.org/10.1073/pnas.0530258100 3. Alter & Golub, PNAS 102, 17559 (2005); http://dx.doi.org/10.1073/pnas.0509033102 4. Alter & Golub, PNAS 101, 16577 (2004); http://dx.doi.org/10.1073/pnas.0406767101 5. Alter & Golub, PNAS 103, 11828 (2006); http://dx.doi.org/10.1073/pnas.0604756103 6. Alter, PNAS 103, 16063 (2006). http://www.pnas.org/cgi/content/full/103/44/16063
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