Ten Simple Rules for Effective Computational Research
I have written at some length about what I feel is necessary to make computational modeling really practical and useful in the life sciences. In the article Biologists Flirt With Models for example, that appeared in Drug Discovery Worldin 2009, and in the light-hearted video that I made for the Google Sci Foo Conference, I have argued for computational models that can be encoded in the kind of language that biologists themselves use to describe their systems of interest, and which deliver their results in a similarly intuitive fashion. It is clear that the great majority of biologists are interested in asking biological questions rather than solving theoretical problems in the field of computer science. Similarly, it is important that these models can translate data (of which we typically have an abundance) into real knowledge (for which we are almost invariably starving). If Big Data is to live up to its big hype, it will need to deliver "Big Knowledge", preferably in the form of actionable insights that can be tested in the laboratory. Beyond their ability to translate data into knowledge, models are also excellent vehicles for the collaborative exchange and communication of scientific ideas.
With this in mind, it is really gratifying to see researchers in the field of computational biology, reaching out to the mainstream life science research community in an effort to address these kinds of issues, as in this article "Ten Simple Rules for Effective Computational Research" that appeared in a recent issue of PLOS Computational Biology. The 10 simple rules presented in the article touch upon many of the issues that we have discussed here, although they are for the most part, much easier said than done. Rule 3. in the article for example "Make Your Code Understandable to Others (and Yourself)" is something of a doozy that may ultimately require biologists to abandon the traditional mathematical approaches borrowed from other fields and create their own computational languages for describing living systems.
To be fair to the authors of the article however, recognizing that there is a problem is an invaluable first step in dealing with it, even if you don't yet have a ready solution - and for that I salute them.
Postscript: Very much on topic, this article about the challenges facing the "Big Data" approach subsequently appeared in Wired on April 11th.
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