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Bioinformatics has been defined many different ways, since practitioners do not always agree upon the scope of its use within the biological and computer sciences, but it is always considered a combination of both sciences, along with other contributing disciplines.


Bioinformatics as a biological science

It is debatable whether bioinformatics and the discipline computational biology, literally "biology that involves computation," are the same or distinct. To some, both bioinformatics and computational biology are defined as any use of computers for processing any biologically-derived information, whether DNA sequences or breast X-rays. Therefore, there are other fields, e.g. medical imaging / image analysis, that might be considered part of bioinformatics. This would be the broadest definition of the term. But, in practice, the definition used by most people is even narrower; bioinformatics to them is a synonym for computational molecular biology: any use of computers to characterize the molecular components of living things.

Bioinformatics as a computer science

To others, bioinformatics is a grammatical contraction of "biological informatics" and is therefore related to the computer science disciplines of information science and/or information technology. This definition would thus emphasize the information contained within the biological data, also implying that large amounts of data would be managed and/or analyzed.

Pre-genomic bioinformatics

Most biologists talk about "doing bioinformatics" when they use computers to store, retrieve, analyze or predict the composition or the structure of biomolecules. As computers become more powerful you could probably add simulate to this list of bioinformatics verbs. "Biomolecules" include your genetic material---nucleic acids---and the products of your genes: proteins. These are the concerns of pre-genomic or "classical" bioinformatics, which deal primarily with sequence analysis.

Fredj Tekaia at the Institut Pasteur offers this definition of bioinformatics:

"The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information."

It is a mathematically interesting property of most large biological molecules that they are polymers; ordered chains of simpler molecular modules called monomers. Think of the monomers as beads or building blocks which, despite having different colors and shapes, all have the same thickness and the same way of connecting to one another.

Monomers that can combine in a chain are of the same general class, but each kind of monomer in that class has its own well-defined set of characteristics. And many monomer molecules can be joined together to form a single, far larger, macromolecule. Macromolecules can have exquisitely specific informational content and/or chemical properties.

According to this scheme, the monomers in a given macromolecule of DNA or protein can be treated computationally as letters of an alphabet, put together in pre-programmed arrangements to carry messages or do work in a cell.

Post-genomic bioinformatics

The greatest achievement of bioinformatics methods, the Human Genome Project, is practically complete. Because of this the nature and priorities of bioinformatics research and applications have changed. People often talk portentously of our living in the "post-genomic" era. This affects bioinformatics in several ways:

It is worth noting that all of the above post-genomic areas of research depend upon established, pre-genomic sequence analysis techniques.

Computer science disciplines inspired by the life sciences

There are also whole other disciplines of biologically-inspired computation, e.g. genetic algorithms, AI, and neural networks. Often these areas interact in strange ways. Neural networks, inspired by crude models of the functioning of nerve cells in the brain, are used in a program called PHD to predict, surprisingly accurately, the secondary structures of proteins from their primary sequences.

See also

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