Bioinformatics & Computational Biology = same? No.

I spent the first 15 years of my professional life unwilling to recognize a difference between bioinformatics and computational biology.  It was not because I didn’t think that there was or could be a difference, but because I thought the difference was not significant.  I have changed my position on this.  I now believe that they are quite different and worth distinguishing.  For me,

Computational biology = the study of biology using computational techniques.  The goal is to learn new biology, knowledge about living sytems.  It is about science.

Bioinformatics = the creation of tools (algorithms, databases) that solve problems.  The goal is to build useful tools that work on biological data.  It is about engineering.

All this became important to me when I finally joined a bioengineering department, and I was forced to ask myself if I was a scientist or an engineer.  I am both, and now am at peace.

When I build a method (usually as software, and with my staff, students, post-docs–I never unfortunately do it myself anymore), I am engaging in an engineering activity:  I design it to have certain performance characteristics, I build it using best engineering practices, I validate that it performs as I intended, and I create it to solve not just a single problem, but a class of similar problems that all should be solvable with the software.   I then write papers about the method, and these are engineering papers.   This is bioinformatics.

When I use my method (or those of others) to answer a biological question, I am doing science.  I am learning new biology.   The criteria for success has little to do with the computational tools that I use, and is all about whether the new biology is true and has been validated appropriately and to the standards of evidence expected among the biological community.   The papers that result report new biological knowledge and are science papers.  This is computational biology.

As I look at my published work I have always tried to balance the publications in biological/medical journals and those in engineering/informatics journals.  It is an aesthetic really, there is no reason why one should feel compelled to do this.  However, it is useful to know when you are doing biology and when you are doing something else.   I suppose someone can argue with the my use of the term “bioinformatics” as an engineering discipline.  That’s fine–I’m open to a different term.  But I would ask why bioinformatics isn’t good.   I think computational biology is more solid–the ‘biology’ is clearly the noun and the ‘computational’ is clearly the adjective.



  1. Wow, it all makes sense now. Very clear explanation. I agree that some might still debate over the definitions, but these make sense to me.

    Incidentally, I’ve just realized that when someone unfamiliar with bioinformatics asks me what I do, I usually describe it as computational biology – solving biological problems with computational tools. I’m not sure if this is because I think it’s easier for people to understand this vs. the engineering definition, because I wasn’t clear on the distinction before and am now used to saying it, or because I secretly identify as more of a computational biologist? Hmm…

  2. I always felt a little uneasy about this distinction. When I was in BMI I thought it was easy to start out with a bioinformatics problem and then become obsessed with its use in a particular context, at which point it starting looking a lot more like computational biology. I always felt a bit torn between the two, particularly when it showed up on the BMI admission application the year after I joined.

    On a more general level, I think it’s very easy to slip into computational biology mode when talking to lay people because, at the end of the day, a typical person doesn’t care about the reusability of a tool, they care about the payoff, and we normally describe the payoff in terms of biology. Or, as I recall being taught by a certain professor in a certain class, in your grant proposal abstract lots of people should be dead or dying by the second sentence, preferably the first. Of course, bioinformatics has its own payoffs, but it’s harder to get someone from outside the field excited about them (EMRs and saving patients’ lives aside).

    I’ve certainly observed this a lot since leaving bioinformatics for the legal profession. Explaining ontologies in 30 seconds was just never going to happen… move away from clinical informatics and people take away that I wrote computer programs of some sort, which is literally true, but undersells the story… tell someone you used to model proteins, and maybe it could help make drugs, and people nod a bit more vigorously…

  3. I forgot to add — I think it’s a technically good distinction. My unease was over the impression that you really had to be one or the other.

  4. I like the definition that computational biology is the use of computational techniques to answer/address biological questions, much like molecular biology and biochemical techniques are for. They are a mean to an end, not an end itself. However, even within bioinformatics, I think there should be distinction between the development of tools/databases and development of algorithms. To me, the former could include visualization tools for alignments, phylogenetic tree and so on, synchronization of databases, data format standardization etc. However, how to get better alignments, how to construct phylogenetic tree accurately, how to correctly model and identify binding sites, how to distinguish orthologs among homologs etc are research endeavors that involved risk, experimentation, and is not just a matter of implementation (engineering?). Thus, the “research fraction” of bioinformatics should be put nearer to “computational biology”. On the other hand, we can use fairly existing computational techniques to answer important biology questions involving no bioinformatic research.

  5. Totally agree with your distinction.
    I think bioinformatitians forgot to ask biological questions and are more interested in develop “yet another method” that improves an existing one slightly.
    Keep writing.

  6. Computational biology was originally part of theoretical computer science where biologically motivated algorithmic problems were studied. So, this is where problems like ‘pancake flipping’ were defined and studied in all their gory detail. This was work you could do with pencil and paper, and was not bioinformatics either.

  7. There is a pretty fine semantic split in there which maybe you could use to indicate whether you work more on the theoretical or applied side.

    I tend to think, in industry at least, there are three aspects to “What-ever-you-want-to-call-this-endeavour”. There is the infrastructure and software side which is, when done well, engineering. There is the algorithm development which is probably math, and the application to biology which you call computational biology.

    I wouldn’t classify bioinformatics as engineering. I tend to think of bioinformatics as what other industries would call data-mining or buisiness intelligence.

    I guess in the end bioinformatics/computational biology is a pretty fuzzy ‘spectrum disorder’. In industry I would argue you are best to situate yourself near the middle. In academia there may be an argument in favour of specialization.

  8. I do not agree with the boundaries of this distinction. I especially do not think that bioinformatics is limited to engineering new tools. I think that the skillful application of these tools for data analysis is also science, and should also part of the definition of bioinformatics.

    I see a close parallel to biostatistics. A clever and biologically insightful data analysis makes use of appropriate tools, sometimes in new combinations, and sometimes requires the invention of new tools. It is all part of the work, and all falls under the BioStats professional label.

    My definition of computational biology is more theoretical, and is generally not directly involved in solving a specific problem with real data (more of a solution in search of a problem rather than the other way around).

    I think you are overplaying the engineering aspect in another dimension. Sometimes new bioinformatics tools derive from truly original algorithms – the products of deep computational biology analysis. Engineers are usually called upon to implement known scientific principles to solve a specific problem efficiently. It is generally not considered engineering to develop new theoretical approaches to basic problems.

  9. It is all matter of semantics! Having said that, it is a good way of classifying it for the sake of distinction. I agree with a blogger above: it is hard to separate them.

  10. Extremely clear explanation!

    I am in the process of applying to graduate schools and the difference between these two areas of biomedical research was quite fuzzy. Not anymore!.


  11. Within computational biology as defined here we can further separate

    (a) arriving at new scientific knowledge, including new explanatory theories, as a result of using computational techniques (e.g. using bioinformatic technology) and

    (b) developing and testing theories about biological systems CONSTRUED AS information processing systems (= computational systems). E.g. studying control systems in a cell (including homeostatic systems, reproductive mechanisms, waste-disposal mechanisms, …) at a level of abstraction that is different from just analysing all the physical/chemical processes, or studying the perceptual functions or learning functions, or problem-solving capabilties, in animals without going into all the physical and chemical processes, but attempting to be precise and detailed enough to allow working models to be built to test the implications of the theories.

    I have recently tried to explain why this needs a deeper and broader computing education in schools, in these online slides:
    Best wishes.

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