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 [1].
In this talk, I will first describe novel generalizations of the matrix and tensor computations that underlie theoretical physics (e.g., [2,3]). In my Genomic Signal Processing Lab we are developing these computations for comparison and integration of multiple high-dimensional datasets recording different aspects of, e.g., the cell division cycle and cancer.
Second, I will describe the prediction of a previously unknown mechanism of regulation by using these computations to uncover a genome-wide pattern of correlation between DNA replication initiation and mRNA expression during the cell cycle [4,5]. This computational prediction was recently experimentally verified by analyzing global mRNA expression levels in synchronized cultures under conditions that prevent DNA replication initiation without delaying cell cycle progression [6].
Last, I will describe the computational prognosis of brain cancers by using these computations to compare global DNA copy numbers in patient-matched normal and tumor samples from the Cancer Genome Atlas [7].
1. Alter, PNAS 103, 16063 (2006); http://dx.doi.org/10.1073/pnas.0607650103
2. Alter, Brown & Botstein, PNAS 100, 3351 (2003); http://dx.doi.org/10.1073/pnas.0530258100
3. Ponnapalli, Saunders, Van Loan and Alter, under review.
4. Alter & Golub, PNAS 101, 16577 (2004); http://dx.doi.org/10.1073/pnas.0406767101
5. Omberg, Golub & Alter, PNAS 104, 18371 (2007); http://dx.doi.org/10.1073/pnas.0709146104
6. Omberg, Meyerson, Kobayashi, Drury, Diffley & Alter, MSB 5, 312 (2009); http://dx.doi.org/10.1038/msb.2009.70
7. Lee & Alter, 60th Annual Meeting of the American Society of Human Genetics (ASHG), Washington, DC, November 2-6, 2010.
