William S, Barnes, Ph.D.
Clarion University of Pennsylvania
 
 
 

Three short definitions:   “Bioinformatics is conceptualizing biology in terms of molecules (in the sense of Physical Chemistry) and then applying informatics techniques (derived from disciplnes such as Applied Math, Computer Science and Staitstics) to understand and organize the information associated with these molecules on a large scale.”   - Mark Gerstein, Yale University, 1999. "Bioinformatics is the application of computer technology to the management and analysis of biological data. The result is that computers are being used to gather, store, analyse and merge biological data. Bioinformatics is an interdisciplinary research area that is the interface between the biological and computational sciences. The ultimate goal of bioinformatics is to uncover the wealth of biological information hidden in the mass of data and obtain a clearer insight into the fundamental biology of organisms." - European Bioinformatics Institute (EBI) "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information."  - Fredj Tekaia, Institut Pasteur

 

A longer discussion.

Information in this section is largely exerpted and abridged from: 
"Bioinformatics Frequently Asked Questions."  by Damian Counsell.
 

Roughly, bioinformatics describes any use of computers to handle biological information. In practice, the definition used by most people is narrower; bioinformatics to them is a synonym for "computational molecular biology" - the use of computers to characterize the molecular components of living things.

"Classical" bioinformatics

Most biologists talk about "doing bioinformatics" when they use computers to store, compare, 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. 

It is a mathematically interesting property of most large biological molecules (macromolecules such as DNA, RNA or protein) that they are polymers composed of monomers (the five nitrogenous bases or the twenty amino acids). Because of their length and complexity, macromolecules can have exquisitely specific informational content and/or chemical properties. The concern of "classical" bioinformatics, is to understand this information, most often with the computational techniques of sequence analysis.
 

"New" bioinformatics

The greatest achievement of bioinformatics methods, the Human Genome Project, is currently being completed. Because of this the nature and priorities of bioinformatics research and applications are changing. People often talk portentously of our living in the " post-genomic" era. My personal view is that this will affect bioinformatics in several ways:

• Now we possess multiple whole genomes we can look for differences and similarities between all the genes of multiple species. From such studies we can draw particular conclusions about species and general ones about evolution. This kind of science is often referred to as comparative genomics.

• With the complete sequencing of many genomes, functional genomics ­ elucidation of gene functions and associations has become a major project of biology. New technologies to measure gene expression at different stages in development or disease or in different tissues require heavy computational and statistical analysis.

• There will be a general shift in emphasis toward proteomics ­ with attempts to catalogue the activities and characterize interactions between all gene .

• An mportant sub-discipline of proteomics is structural biology which elucidates the structure and function of proteins and their ligands. Mathematical, statistical and computational approaches are integral and essential tools.

There are other "-omics" and  "-informatics"  fields which are related to Bioinformatics or even overlap with it:
 

Medical Informatics.

Biomedical Informatics is an emerging discipline that has been defined as the study, invention, and implementation of structures and algorithms to improve communication, understanding and management of medical information. Unlike Bioinformatics, Medical informatics seems to be more concerned with structures and algorithms for the manipulation of medical data, rather than with the data itself. Medical informatics, for practical reasons, is more likely to deal with data obtained at "grosser" biological levels---that is information from super-cellular systems, right up to the population level---while most bioinformatics is concerned with information about cellular and biomolecular structures and systems.
 

Computational Biology.

Computational biology is not so much a "field", as an "approach" involving the use of computers to study biological processes; hence it is an area as diverse as biology itself. Whereas Bioinformatics has more to do with management and the subsequent use of biological information, particularly genetic information, Computational biology implies modelling and simulation of dynamic biological systems using mathematical and computational approaches. Evolutionary, population and theoretical biology has traditionally been a strong emphasis among Computational Biologists. However “Molecular Systems Biology”, in which dynamic processes at the level of the cell or organelle are modelled, is a new field attracting great excitement.

 
Mathematical Biology.

Mathematical biology is easier to distinguish from bioinformatics than computational biology. Mathematical biology also tackles biological problems, but the methods it uses to tackle them need not be numerical and need not be implemented in software or hardware. Indeed, such methods need not "solve" anything; in mathematical biology it would be considered reasonable to publish a result which merely explains how a biological problem can be quantified and understood. Mathematical Biology "...includes things of theoretical interest which are not necessarily algorithmic, not necessarily molecular in nature, and are not necessarily useful in analyzing collected data."
 

Pharmacogenomics.

Pharmacogenomics is the application of genomic approaches and technologies to the identification of drug targets. Examples include trawling entire genomes for potential receptors by bioinformatics means, or by investigating patterns of gene expression in both pathogens and hosts during infection, or by examining the characteristic expression patterns found in tumours or patients samples for diagnostic purposes (possibly in the pursuit of potential cancer therapy targets).

Pharmacogenetics is a subset of pharmacogenomics. All individuals respond differently to drug treatments; some positively, others with little obvious change in their conditions and yet others with side effects or allergic reactions. Much of this variation is known to have a genetic basis. Pharmacogenetics uses genomic/bioinformatic methods to identify genomic correlates, for example SNPs (Single Nucleotide Polymorphisms), characteristic of particular patient response profiles and use those markers to inform the administration and development of therapies. Strikingly, such approaches have been used to "resurrect" drugs thought previously to be ineffective, but subsequently found to work with in subset of patients. They can also be used for optimizing the doses of chemotherapy for particular patients.
 

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