I do not like the modern proliferation of terms defining specific fields, we can find bombastic declarations saying that a specific application of a technique like -omics high througput screening results is 'a new science !' ( I was used to think that chemistry and physics are for example different sciences because they have quite different basic concepts and focus even if clearly there are large areas of superposition so we can safely speak of physical chemistry..) but calling bioinformatics a new science is frankly exaggerated.The need of new brands is a deprecable attitude infecting science from the marketing world.

After all the term bioinformatics derives from the massive us of computers in analysing data bases coming from biological experimental results, well, given this is the case, a novelist writing his novel using word software instead of the classical type-writer or the still more classical pen, should be doing 'literatureinformatics'...frankly ridiculous.

The use of mathematical models in bioscience (and here we come to computational biology) was very widespread until the sixties where you could easily find an average neurologist able to speak properly about Fourier Analysis with a more than decent knowledge of the mathematical bases of the technique, the same can be said for a morphologist about principal component analysis, while still in the seventies a microbiologist did know many aspects of cluster analysis, and this picture is formed without to say a word of the fact a big portion of classical inferential statistics was born in biological milieu (agronomists, geneticists..) in the first years of the XXth century.

For a curious joke of the Fate (I do not think the Fate was so important but we can think of it without entering in too detailed explanations) this culture practically disappeared when the work of the 90% of biologists consisted in looking at photographic papers covered with spots and then saying 'gene A is involved in process (or disease) Y' no need of quantitative thinking, well then the need of looking at more and more of these associations and the exploding of the number of minimally relevant (but hopefully globally consistent and interesting) experimental data (many of these data being long, very long sequences) and the contemporary development of low cost computers forced biologists to ask for the help of engineers, in the beginning (eighties-nineties) they wanted to keep their science separated from the engineer-servant work , the term 'bioinformatics' is reminescent of this goal, calling someone a bioinformatician had in the beginning the same 'value' that in the fifties had calling someone 'a spectroscopist' a good technician but, come on, not a scientist. When someone discovered that it is not only important to store data on the computer but to make some sensible computation 'computational biology' came into play...very boring indeed this struggle of names, my wish is we can go back to fifty years ago and simply speaking of 'biology' tout-court implying that for doing biology you need to know something about the quantitative aspects of your field.

The way it was explained to me: bio-informatics is primarily concerned with the information infrastructure (databases, modelling tools, etc.) while computational biology aims to create models of the real world.

Of course, this means that computational biology depends on bioinformatics to be effective, and that bioinformatics gains usefulness from models created by computational biologists.

Or, to put it practically: modeldb as a whole is an example of bioinformatics, while a model found in modeldb is an example of computational biology.

Hi Matthijs,

U mean that these both terms have same different as Engineering and Basic Sciences or Genetic Engineering and Molecular Biology....?

I do not like the modern proliferation of terms defining specific fields, we can find bombastic declarations saying that a specific application of a technique like -omics high througput screening results is 'a new science !' ( I was used to think that chemistry and physics are for example different sciences because they have quite different basic concepts and focus even if clearly there are large areas of superposition so we can safely speak of physical chemistry..) but calling bioinformatics a new science is frankly exaggerated.The need of new brands is a deprecable attitude infecting science from the marketing world.

After all the term bioinformatics derives from the massive us of computers in analysing data bases coming from biological experimental results, well, given this is the case, a novelist writing his novel using word software instead of the classical type-writer or the still more classical pen, should be doing 'literatureinformatics'...frankly ridiculous.

The use of mathematical models in bioscience (and here we come to computational biology) was very widespread until the sixties where you could easily find an average neurologist able to speak properly about Fourier Analysis with a more than decent knowledge of the mathematical bases of the technique, the same can be said for a morphologist about principal component analysis, while still in the seventies a microbiologist did know many aspects of cluster analysis, and this picture is formed without to say a word of the fact a big portion of classical inferential statistics was born in biological milieu (agronomists, geneticists..) in the first years of the XXth century.

For a curious joke of the Fate (I do not think the Fate was so important but we can think of it without entering in too detailed explanations) this culture practically disappeared when the work of the 90% of biologists consisted in looking at photographic papers covered with spots and then saying 'gene A is involved in process (or disease) Y' no need of quantitative thinking, well then the need of looking at more and more of these associations and the exploding of the number of minimally relevant (but hopefully globally consistent and interesting) experimental data (many of these data being long, very long sequences) and the contemporary development of low cost computers forced biologists to ask for the help of engineers, in the beginning (eighties-nineties) they wanted to keep their science separated from the engineer-servant work , the term 'bioinformatics' is reminescent of this goal, calling someone a bioinformatician had in the beginning the same 'value' that in the fifties had calling someone 'a spectroscopist' a good technician but, come on, not a scientist. When someone discovered that it is not only important to store data on the computer but to make some sensible computation 'computational biology' came into play...very boring indeed this struggle of names, my wish is we can go back to fifty years ago and simply speaking of 'biology' tout-court implying that for doing biology you need to know something about the quantitative aspects of your field.

I agreed Alessandro, u are right.

These are just combinations. They are not real sciences.

@Dr.Alessandro thanks for the wonderful perspective..!! Especially branding in Science. It completely changed my way of thinking about Bioinformatics.

But isn't the branding needed to bring more people into that field, esp when the field of concern is growing very fast?

In my view computational biology means where we solve problems with the help of softwares...ie using of computers. But bioinformatics is broad which includes designing programmes, developing softwares also. If ur developing a 3d structure to a protein its a computational biology work. Computational biology is one of the part of bioinformatics widely used.

@ Ravi, thank you for your kind words ! But....

Do we really need more people in the field ? And, more important, what is a field ? I think is much more important to unify things with respect to increase fragmentation, we have a very serious problem of proliferation of irrelevant results in science (see the link attached) due to the increase fragmentation of sub-fields and disciplines so that basically the sameconcepts are re-iterated many many times increasing informational noise and reducing the efficacy of science:

http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0020124