MACROSCOPE
An Engineering Approach to Translational Medicine
Physician-scientists may benefit from an approach that emphasizes solving problems over generating hypotheses
Michael Liebman
In the years since the completion of the Human Genome Project,
physician-scientists have applied new energy to translating findings
from the laboratory into better treatments for patients. Yet this
accelerated, unidirectional transfer of knowledge from the bench to
the bedside, a practice that goes by the name of translational
medicine, is hitting an obstacle: The generation of data is far
outstripping scientists' ability to convert it into usable
knowledge. I believe that, paradoxically, this problem stems from
the tightly focused approach that gives science much of its power.
Genomics, proteomics and other high-throughput technologies are
seductively powerful, but that seduction may limit our view of the
complex problems of physiology and disease.
For example, scientists can now correlate a disease with a specific
pattern of gene expression. Such experiments are straightforward and
fairly quick when the tools are available, and they provide a
massive quantity of data. However, by diverting limited resources of
time, money and personnel, mining this wealth of data may actually
lead investigators away from grasping the governing laws from which
they could build predictive models of the disease.
I am not suggesting that investigators should give up
high-throughput, brute-force methods. Today's technology is a boon
to science and a powerful component of my own research. However, as
clinical investigators, we stand to reap significant benefits on
behalf of society by expanding our focus and viewing translational
medicine not through the eyes of a scientist, but as an engineer might.
Why an engineer? Because an engineer uses the fruits of science to
feed the appetite of technology. Unlike scientists, who tend to
approach problems from a "bottom-up" perspective by
collecting data and seeking patterns, engineers take a
"top-down" approach, probing a specific system for clues,
taking it apart and considering how each component can be handled in
a tailored solution. An engineer is a problem solver rather than a
hypothesis generator.
The two perspectives are neatly symbiotic in physics and chemistry,
for which fundamental laws yield predictive models. But in the life
sciences, biologists, including physicians, must be more aware of
the gap between science and technology—we still know too
little about the complexity of living systems to make many
generalizations from first principles.
I propose that an engineering approach, what might be called
"real systems analysis," may be a better way for
scientists to identify and develop solutions for biomedical
problems. This kind of problem solving requires that
translational-medicine research place more emphasis on going from
the bedside to the bench, rather than the other way around. The
Clinical Breast Care Program (CBCP) is a collaboration between
Windber Research Institute and Walter Reed Army Medical Center, and
it is the prototype for an integrated approach to the study of
breast cancer. Here, I present some examples of how top-down problem
solving in the CBCP has provided unique insights.
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