Abstract: Recent studies have demonstrated that network approaches are highly appropriate tools to understand the extreme complexity of the aging process. The generality of the network concept helps to define and study the aging of technological, social networks and ecosystems, which may give novel concepts to cure age-related diseases. The current review focuses on the role of protein-protein interaction networks (interactomes) in aging. Hubs and inter-modular elements of both interactomes and signaling networks are key regulators of the aging process. Aging induces an increase in the permeability of several cellular compartments, such as the cell nucleus, introducing gross changes in the representation of network structures. The large overlap between aging genes and genes of age-related major diseases makes drugs which aid healthy aging promising candidates for the prevention and treatment of age-related diseases, such as cancer, atherosclerosis, diabetes and neurodegenerative disorders. We also discuss a number of possible research options to further explore the potential of the network concept in this important field, and show that multi-target drugs (representing “magic-buckshots” instead of the traditional “magic bullets”) may become an especially useful class of age-related future drugs.

An Inner ‘Fingerprint’ For Personalizing Medical Care

http://www.sciencedaily.com/releases/2009/07/090722110859.htm

“Doctors have dreamed of using such tests for the early diagnosis of disease and personalized medical care. They could pick drugs and treatments that are best for each individual, rather than today’s one-size-fits all medicine. To do so, however, doctors need evidence that the metabolic fingerprint remains stable over a period of years, with changes due to disease or medications, for instance, but not advancing age or other factors. The new study provides that evidence, based on the analysis of over 1,800 urine samples from people monitored for 2-3 years. Researchers could identify individual patients from their metabolic profiles with an accuracy of over 99 percent. The study could pave the way for using metabolic profiling to apply personalized medical care, the researchers suggest.”

I wasn’t going to follow-up the prior comment, but this summary of Bernini et al. Individual Human Phenotypes in Metabolic Space and Time. Journal of Proteome Research, 2009, was completely on-topic.

However, to keep this short, I’ll omit my anecdote about myself, Lee Hood, and the representative from the Smithsonian. After all, this is not a short short story that needs more characters and plot. My personal treatment now begins with the morning’s first cup of coffee.