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In practice, to date, most bioinformatics sources and tools have leaned heavily on the use of flat file structures, with all the associated problems with system evolution and limited access facilities that such a baseline implies Although many of the largest and most heavily used sources now make use of relational storage systems, it is still often the case that the external representation made available to programmers is based on flat file structures It can be anticipated that flat files will continue to be important in the foreseeable future, but that representation standards such as XML will help in the provision of less ad-hoc approaches to the organization and sharing of biological information in a distributed setting However, although flat file structures have persisted in bioinformatics, it is clear that there is considerable interest in the use of object-orientation.

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Blade servers within CIM are no different from traditional systems, when viewed as a logical system or element. However, when modeling the physical aspects of a bladed system, the differences become apparent. The relationships (or associations, as they are called within CIM) show the connections between components in a chassis, including associations between blades and the shared components, such as power supplies, cooling fans, and physical network connections. There are also relationships between a blade and the chassis and slot where the device is physically located.

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If we integrate these missing elements with the sequencing of the system solution domains, we can create a methodology that enables us to apply it to any entity, regardless of level of abstraction. The steps of the methodology are: Step 1: Step 2: Step 3: Step 4: Step 5: Step 6: Understand the entity s opportunity/problem and solution spaces. Develop the entity s Requirements Domain Solution. Develop the entity s Operations Domain Solution. Develop the entity s Behavioral Domain Solution. Develop the entity s Physical Domain Solution. Evaluate and optimize the entity s total design solution.

In particular, the OMG has a Life Sciences Research (LSR) group [LSR] that is developing a collection of standard definitions for describing biological objects in CORBA The scope of the LSR activity is quite broad, including sequence analysis, genome maps, gene expression, macromolecular structure, and clinical trials This activity, however, leaves open the question as to how the underlying data repositories should be organized and in particular whether there is a role for object databases for managing and analyzing biological data This chapter contains a brief overview of bioinformatics projects that have made use of object databases This is followed by some experience reports, including some examples of biological information models, a description of how these models can be implemented in an ODMG database [Cattell 1997], and a discussion of how these databases have been populated and used.

When we depict these steps, their initial sequencing, and interrelationships graphically, Figure 26.1 emerges. We will refer to this as the SE Process Model. Before we proceed with describing the SE Process Model, let s preface our discussion with several key points: 1. The description uses the term, entity, to denote a logical/functional capability or physical item such as PRODUCT, SUBSYSTEM, ASSEMBLY, SUBASSEMBLY, and PART. You could apply the term, component. However, there may be some unprecedented systems in which physical components may not emerge until later in a design process. Therefore, we use the term entity. 2. The act of partitioning a problem space into lower level solution spaces is traditionally referred to in SE as decomposition. Since decomposition connotes various meanings, some people prefer to use the term expansion. 3. Since the model applies to any level of abstraction, role-based terms such as Acquirer, User, and System Developer are contextual. For example, the Acquirer (role) of a system con-

Understand Opportunity / / Problem & Solution Spaces Understand Opportunity Problem & Solution Spaces

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The experience presented is essentially of a genomic data warehouse, where information that is available elsewhere is replicated in an object database in order to allow efficient analyses over the data..

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