Definition of Bioinformatics

The science of bioinformatics presents the rich complexity of biology in such a way that meaning can be extracted using digital tools. As a discipline having multiple parts, it can be defined overall in a number of ways. One definition of bioinformatics and its components is as follows (D'Trends n.d.):

(1) Bioinformatics - database-like activities involving persistent sets of data that are maintained in a consistent state over essentially indefinite periods of time

(2) Computational biology - the use of algorithmic tools to facilitate biological analyses

(3) Bioinformation infrastructure - the entire collective of information management systems, analysis tools and communication networks supporting biology

This composite definition points out the importance of three activities critical to the success of bioinformatics activities:

• The use of analytic methods to enable the presentation of biological information in digital fashion.

• The leveraging of massive digital storage systems and database technologies to manage the information obtained.

• The application of digital analytic tools to identify patterns in the data that clarify causes and effects in biological systems, augmented by visualization tools that enable the human mind to rapidly grasp these patterns.

Bioinformatics makes the complexity of biological systems tangible. Taken in the stepwise fashion described above, this complexity can often be reduced to terms that are understandable to scientists probing biological problems. Biological complexity is worthwhile to understand. A clear appreciation of cause and effect in biological systems can provide the knowledge needed to develop drugs and other medical therapies and also to provide a greater appreciation for what we are as humans beings. It is interesting to note that biological complexity is so extreme that it challenges the best that high-performance computing presently has to offer. Ironically, the fulfillment of the Socratic adage "Know Thyself" can now only be achieved through man's interaction with and dependence upon computing systems.

The recent accomplishment of sequencing the human genome (and now the genomes of several other species) focused attention on the information processing requirements at the molecular end of the biological spectrum. For a time, it seemed that "bioinformatics" was wholly concerned with the management and deciphering of genetic information. Soon, information descriptive of the patterns of expression of proteins and their interactions was added (proteomics). Since this information required stratification by disease type, cellular and tissue information became important to consider. Inevitably, it became apparent that information descriptive of the whole organism, such as radiological data, other morphometric data, chemistries, and other health record data should be included. Once this was done, aggregated societal data was the next logical addition.

The picture that has come into view is therefore one of a continuum of bioinformatics (Figure C.5). In the COB model, linked data at multiple scales of biological complexity are considered together for both individuals and aggregates of individuals. The key to the value of the COB will be the ability to derive correlations between causes (such as gene expression, protein interactions, and the like) and effects (such as healthcare outcomes for individuals and societies). In this model, it may well be possible one day to determine the cost to society of the mutation of a single gene in a single individual! It will also be possible to predict with clarity which drugs will work for which individuals and why. By taking a reverse course through the COB from effects to causes, it will also be possible to sharply identify proteins that can serve as drug targets for specific disease states.

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