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MACROSCOPE

An Engineering Approach to Translational Medicine

Physician-scientists may benefit from an approach that emphasizes solving problems over generating hypotheses

Michael Liebman

Aging as a Background to Disease

The breast changes between a woman's time in utero and her post-menopausal years. This maturation process is different for women who have had children than for those who have not, and it also varies under the influence of several variables: age of menarche, use of hormonal birth control, number and timing of children, the practice of breastfeeding, age of menopause and use of hormone-replacement therapy. Thus, our definition of "normal" varies with age and experience, and an optimal diagnosis must use a systems-based approach to compare an individual cancer patient's baseline (which we must guess at) to her disease state (which we can measure during diagnosis and treatment). The immediate aim of our project is to determine background levels of gene and protein expression in breast tissue and to find out how these numbers vary in a healthy population. This information will be a significant step in the development of molecular diagnostics.

Note that a woman's life stages are not separated by fixed boundaries. Rather, each represents a unique intersection of a woman's age and an event. Given this complexity, it was crucial to sieve the scientific literature for data that we could integrate into a systems approach. This was more difficult than one might think. Scientists have studied these stages for decades, producing a tremendous body of work in physiology and pathology—more than any one scientist can master. Furthermore, we recognized that even the most encyclopedic and fair-minded review article cannot escape the inherent bias of its author. Thus, we have harnessed some computing power, employing text data-mining to cull the literature. This effort has two aims: to refine the definitions of these stages and to extract information about the underlying physiological and developmental changes. This information becomes the foundation for our molecular analyses and helps integrate clinical and molecular data. We plan to augment this computational approach with a community-based longitudinal study that includes molecular and behavioral components.








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