Genomic Signal Processing and Systems Biology

In her Genomic Signal Processing Lab, Alter is breaking new ground in three areas. First, she develops generalizations of the matrix and tensor computations that underlie theoretical physics. Second, she uses the computations to create models that compare and integrate different types of large-scale molecular biological data. Third, she uses the models to computationally predict physical,¹  cellular and evolutionary²  mechanisms that govern the activity of DNA and RNA. She believes that future discovery and control in biology and medicine will come from the mathematical modeling of large-scale molecular biological data, just as Kepler discovered the laws of planetary motion by using mathematics to describe trends in astronomical data.³ 

Alter pioneered the use of the matrix singular value decomposition (SVD),⁴  the tensor higher-order SVD (HOSVD)⁵  and their generalizations in modeling different types of genomic data from different studies of cell division and cancer and from different organisms. She showed that the mathematical variables and operations of these models represent biological and experimental reality. Experimental results from her lab verify a computational prediction of a global causal coordination between DNA replication origin activity and mRNA expression,^6,7  demonstrating that matrix and tensor modeling of DNA microarray data can be used to correctly predict previously unknown biological modes of regulation.⁸ 

Alter's recent generalized SVD (GSVD) modeling of just two patient-matched genomic datasets uncovered a previously unknown global pattern of DNA aberrations that is correlated with, and possibly causally related to, brain cancer survival.⁹  This new link between a glioblastoma multiforme (GBM) brain tumor's genome and a patient's prognosis offers insights into the cancer's formation and growth, and suggests promising targets for drug therapy. The best prognostic predictor of GBM prior to this discovery was the patient's age at diagnosis. Alter's recently formulated higher-order GSVD (HO GSVD)¹⁰  is the only mathematical framework to date that enables comparison of more than two patient-matched genomic datasets, and, in general, more than two large-scale datasets arranged in tables of different row dimensions but the same column dimensions. The number of such datasets, recording different aspects of a single phenomenon, is fast growing in basic sciences and medicine. Gaining access to the full information that these datasets store requires mathematical frameworks that can compare and contrast them in order to find the similarities and dissimilarities among them. Ultimately Alter hopes to bring physicians a step closer to one day being able to predict and control the progression of cancers as readily as NASA engineers plot the trajectories of spacecraft today.

Alter's research is cited in hundreds of publications and patents,¹¹  is featured in textbooks, and is part of the curriculum in engineering, medicine and basic sciences. This work enabled other labs to detect and remove experimental variations,¹²  compare and integrate different types of data,^13,14  and discover previously unknown cellular phenomena.¹⁵  This work also motivated mathematical¹⁶  and statistical¹⁷  theorems and genomic experiments ¹⁸ by other labs.

Lee CH, Alpert BO, Sankaranarayanan P, Alter O, GSVD comparison of patient-matched normal and tumor aCGH profiles reveals global copy-number alterations predicting glioblastoma multiforme survival. PLoS One 2012;7(1):e30098

Ponnapalli SP, Saunders MA, Van Loan CF, Alter O, A higher-order generalized singular value decomposition for comparison of global mRNA expression from multiple organisms. PLoS One 2011;6(12):e28072

Muralidhara C, Gross AM, Gutell RR, Alter O, Tensor decomposition reveals concurrent evolutionary convergences and divergences and correlations with structural motifs in ribosomal RNA. PLoS One 2011;6(4):e18768

Omberg L, Meyerson JR, Kobayashi K, Drury LS, Diffley JF, Alter O, Global effects of DNA replication and DNA replication origin activity on eukaryotic gene expression. Mol Syst Biol 2009;5():312

Omberg L, Golub GH, Alter O, A tensor higher-order singular value decomposition for integrative analysis of DNA microarray data from different studies. Proc Natl Acad Sci U S A 2007 Nov 20;104(47):18371-6

Alter O, Genomic signal processing: from matrix algebra to genetic networks. Methods Mol Biol 2007;377():17-60

Alter O, Discovery of principles of nature from mathematical modeling of DNA microarray data. Proc Natl Acad Sci U S A 2006 Oct 31;103(44):16063-4

Alter O, Golub GH, Singular value decomposition of genome-scale mRNA lengths distribution reveals asymmetry in RNA gel electrophoresis band broadening. Proc Natl Acad Sci U S A 2006 Aug 8;103(32):11828-33

Alter O, Golub GH, Reconstructing the pathways of a cellular system from genome-scale signals by using matrix and tensor computations. Proc Natl Acad Sci U S A 2005 Dec 6;102(49):17559-64

Alter O, Golub GH, Integrative analysis of genome-scale data by using pseudoinverse projection predicts novel correlation between DNA replication and RNA transcription. Proc Natl Acad Sci U S A 2004 Nov 23;101(47):16577-82

Alter O, Brown PO, Botstein D, Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms. Proc Natl Acad Sci U S A 2003 Mar 18;100(6):3351-6

Nielsen TO, West RB, Linn SC, Alter O, Knowling MA, O'Connell JX, Zhu S, Fero M, Sherlock G, Pollack JR, Brown PO, Botstein D, van de Rijn M, Molecular characterisation of soft tissue tumours: a gene expression study. Lancet 2002 Apr 13;359(9314):1301-7

Alter O, Brown PO, Botstein D, Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A 2000 Aug 29;97(18):10101-6