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 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
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
1. Alter, PNAS 103, 16063 (2006);
2. Alter, Brown & Botstein, PNAS 100, 3351 (2003);
3. Ponnapalli, Saunders and Alter, under review.
4. Alter & Golub, PNAS 101, 16577 (2004);
5. Omberg, Golub & Alter, PNAS 104, 18371 (2007);
6. Omberg, Meyerson, Kobayashi, Drury, Diffley & Alter, MSB 5, 312
7. Lee & Alter, 60th Annual Meeting of the American Society of Human
Genetics (ASHG), Washington, DC, November 2-6, 2010.