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Orly Alter, Ph.D.

Orly Alter, Ph.D.
Assistant Professor

Orly Alter, Ph.D.

Assistant Professor
Department of Biomedical Engineering
Fellow, Institute for Cellular and Molecular Biology

  • Department of Biomedical Engineering
    Institute for Cellular and Molecular Biology
    The University of Texas at Austin
    1 University Station A4800
    Austin, Texas 78712-0159
    (512) 471-7939 (phone)
    (512) 471-2149 (fax)
  • Lab Website
  • Office Hours: By appointment

Research Interests

In her Genomic Signal Processing Lab at UT Austin, Dr. Orly Alter and her students use generalizations of mathematical frameworks that have proven successful in describing the physical world to model large-scale molecular biological data, such as DNA microarray data. DNA microarrays make it possible to record the complete genomic signals that guide the progression of cellular processes. Future discovery and control in biology and medicine will come from the mathematical modeling of these data. To this end, Dr. Alter built the first predictive models of DNA microarray data using matrix computations. She demonstrated the ability of her models to predict previously unknown biological principles with a prediction of a novel mechanism of regulation that correlates DNA replication initiation with cell cycle-regulated mRNA expression. This research work is cited in hundreds of scientific papers and several textbooks. It has become a part of the academic core curriculum in courses in mathematics and molecular biology.

In 2007 Dr. Alter has been awarded more than $1.5 million in an R01 Grant from the National Human Genome Research Institute (NHGRI). This grant will support a five-year project in her lab, titled "Tensor Computations For Modeling Large-Scale Molecular Biological Data: From Discovery of Patterns to Discovery of Principles of Nature."

Dr. Alter's goal is to enable better understanding and ultimately also control of life processes on the molecular level. Her models may become the foundation of a future in which biological systems are modeled as physical systems are today. The predicted mechanism of regulation may be at the basis of a future where the cell cycle and cancer can be controlled.


Selected Publications

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