Many areas of biological research have benefited from the technological advances of the past several years. The human genome is sequenced, along with genomes from dozens of other organisms; genome-wide protein-protein interaction maps have been generated for most of the major model systems; simultaneous gene expression profiling for tens of thousands of genes has become routine; and an almost inconceivable amount of primary data are only a mouse-click away via the Internet.
One of the most important questions facing the field of biogerontology is how to integrate and optimize the use of new technologies for the study of aging and age-associated disease. Application of new technologies to aging-related biology has often lagged relative to other fields, such as cancer biology. In the past, aging-related research has been driven largely by gene-specific or model-driven studies based on prior assumptions or knowledge. More recently, however, that trend seems to be changing as researchers recognize the opportunities associated with genome-scale approaches. In many cases, new technologies are being developed with the specific goal of tackling a particular aspect of aging-related research.
Along with the advantages of high-throughput methodologies, however, there are also risks, such as higher false positive and false negative rates. This is particularly likely to be the case when a high-throughput method departs substantially from the standard method, and, in extreme cases, can result in artifacts that threaten the validity of entire lines of research (see the section on targeted screens for small molecules that slow aging). It is also often the case that statistical methods for dealing with new types of data generated from high-throughput methods are underdeveloped. This issue, which has plagued microarray studies for several years, is now better appreciated. Potential problems with large-scale methods can generally be avoided by independent replication of key data points using a more standard methodology, such as real-time quantitative polymerase chain reaction (PCR), to verify gene expression changes from microarray studies (see Applications of Microarrays to Aging-Related Research).
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