'Systems biology' integrates all that's known about disease and humans

Ken Stuart, president and founder of the nonprofit Seattle Biomedical Research Institute, gives us a decent rundown of what is meant by "systems biology" in an article appearing in Drug Discovery & Development. He describes it as a "holistic approach" that has been made possible only relatively recently with the convergence of two things: an improvement in our ability to measure biological molecules and the increased computational power necessary to make sense out of that new data.

Many of the biotech breakthroughs you read about involve studies of single characteristics of biomolecules or cells and their traits, and then conjecture from researchers as to how this single characteristic can impact the progression of a disease, for example. Systems biology allows this extrapolation to become more than an educated guess. "This approach combines extensive data collection from biological studies with mathematical and computational analyses that elucidate emergent biological properties rather than single characteristics," Stuart writes.

And this approach is not only useful in discovering the causes of disease, but also in measuring the effectiveness of treatments. "A key value of the approach is the ability to predict various biological responses to a drug including toxicity and efficacy," Stuart writes. "Overall, systems biology provides for a more realistic comprehension of biology and is predictive and efficient."

Seattle BioMed's role in systems biology, he writes, is in the study of infectious diseases. Viruses have only a few genes, but they have a kind of domino effect when they subvert and co-opt the complex biological processes of the infected host. The systems biology approach can handle the huge amounts of data, plus take into account the complex biological systems effected, to come up with treatments and vaccines that prevent disease without having an adverse effect on the human host.

Simply put, systems biology involves putting to use much of the data collected about disease over the course of the previous decades and integrating them into cures that take into account the complexity and interconnectedness of all systems in the human body.

- read the piece in Drug Discovery & Development

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