samples from each soldier and establish a DNA profile databank. When a soldier is killed and cannot be identified with usual methods, a sample of tissue, blood, or bone marrow from the remains would be subjected to DNA analysis for comparison with entries in the databank. There are 3.3 million active and reserve members of the armed forces. Given the costs associated with the current technology, a DNA databank of such scope would not be amenable to RFLP analysis. The Armed Forces Institute of Pathology therefore proposes to begin collecting and storing samples while working on the development of a DNA analysis method, which when perfected will be much less expensive and time-consuming than existing RFLP methods.
A databank of military personnel could also offer ancillary forensic applications: criminal investigations conducted by criminal investigation divisions of the armed forces could be aided in the same manner as those of other law-enforcement agencies, identification of subjects for security purposes could be enhanced, and identification of urine samples from disputed sources for drug testing could be verified.
The present committee has not been asked to comment on this program; we simply acknowledge its existence.
Local autonomy as to databank structure and function is recommended, for several reasons: a databank can be tailored to meet local needs, the local databank administrator will not have to rely on outside entities for maintenance and change, and security can best be managed with smaller, discrete, well-understood databanks. That is not to say that standards and guidelines should be avoided. On the contrary, very strict regulations, standards, and guidelines for all aspects of the operation should be enforced and monitored. Databank requirements involve determining what a system must accomplish; there are typically many alternative implementation details that can accomplish the same goals.
The experimental protocols used to derive DNA profiles will probably continue to change as the associated technologies continue to mature. That presents a problem that is common in databank applications when the underlying science is in flux: maintaining data integrity while keeping the system current with the most appropriate technology. It will be challenging, but necessary to ensure competence. In practice, that means designing for change, which requires partitioning the problem into two domains—one that is relatively stable and one that is relatively dynamic. For example, data within the sample context are relatively stable, whereas those associated with experiments and derived data are relatively dynamic.
Figure 5-1 is a high-level data flow diagram that shows one possible model for the flow of information from state or regional laboratories to a