ABSTRACT

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

Future discovery and control in biology and medicine will come from the mathematical modeling of large-scale molecular biological data, such as DNA microarray data, just as Kepler discovered the laws of planetary motion by using mathematics to describe trends in astronomical data. In prior work, generalizations of the matrix computations that underlie theoretical physics were developed, and their use was pioneered in creating models from DNA microarray data. These models have been used to predict mechanisms that govern the activity of DNA and RNA, including a previously unknown mechanism of regulation that correlates DNA replication initiation with RNA expression during the cell cycle, the first mechanism to be predicted from mathematical modeling of microarray data. Preliminary experimental results verify this computational prediction, demonstrating for the first time that mathematical modeling of microarray data can be used to correctly predict previously unknown cellular mechanisms. This work will now be extended by creating the first tensor algebra models, integrating and comparing different types of cell cycle data from different studies and organisms and under different conditions. This project will result in innovative mathematical frameworks for large-scale data analysis, as well as new insights into the cellular program of cell division. For example, one preliminary tensor model is the only mathematical framework to date that enables comparison of data from multiple organisms without being limited to similar genes among these organisms. This model promises to reveal universality and specialization of evolutionary, biochemical and genetic pathways that are truly on genomic scales. Another preliminary model enables integration of data from time courses under different perturbations into a coherent picture of the effects of these perturbations on cell cycle progression. This model promises to uncover previously unrecognized causal coordination of cellular activities.

Recently developed high-throughput technologies, such as the DNA microarray, make it possible for the first time to record the complete molecular biological signals that guide the progression of cellular processes, i.e., tell cells what to do and when and where to do it. Biology and medicine today may be at a point similar to where physics was after the advent of the telescope. The rapidly growing number of large-scale data sets holds the key to the discovery of cellular mechanisms, just as the astronomical tables compiled by Galileo and Tycho enabled accurate predictions of planetary motions and, later, the discovery of universal gravitation. Just as Kepler and Newton made these predictions and discoveries by using mathematical frameworks to describe trends in astronomical data, so future discovery and control in biology and medicine will come from the mathematical modeling of large-scale molecular biological data. In this project, mathematical frameworks for large-scale data analysis are developed and used to create models from microarray data, and predict cellular mechanisms. For example, earlier work predicted a previously unknown mechanism of regulation that correlates the beginning of DNA replication with RNA transcription, the process by which the information in DNA is transferred to RNA, during the cell division cycle. This is the first mechanism to be predicted from mathematical modeling of microarray data. Preliminary experimental results verify this computational prediction, demonstrating for the first time that mathematical modeling of microarray data can be used to correctly predict previously unknown cellular mechanisms. This project will now extend this work by creating models that integrate and compare different types of cell cycle data from different studies and organisms and under different conditions. Models such as these may become the foundation of a future where physicians model and control biological systems as readily as engineers model and control physical systems today. In this future, cancer and disease can be stopped or reversed, damaged tissues can be engineered to regenerate, and aging can be slowed or even halted altogether.

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