Admittedly, the term “Life Sciences Discovery Informatics” which figures so prominently in the tag line of this blog is a little unwieldy. Here, I will make a brief attempt at explaining why we chose it anyways.

Let us start with a definition of the term “Life Sciences”. Wikipedia makes the following helpful suggestion:

“The life sciences comprise the fields of science that involve the scientific study of living organisms, such as plants, animals, and human beings, as well as related considerations like bioethics. While biology remains the centerpiece of the life sciences, technological advances in molecular biology and biotechnology have led to a burgeoning of specializations and new, often interdisciplinary, fields.”

Reading only the first sentence, one would think that the Life Sciences are actually nothing more than good old biology. Even after finishing the second sentence one cannot help but think that it was probably the refusal on the part of scientists coming from such illustrious disciplines as physics and chemistry to call themselves “biologists” that drove the invention of this now so ubiquitous term. In any event, Life Sciences are hip these days and the research results from its various subdisciplines drive the development of countless commercial applications in the biotechnology, pharmaceutical, and healthcare industries.

Conducting research in the Life Sciences, however, is notoriously difficult: Experiments tend to have many factors and need many replicates to account for the intrinsic complexity and variability of living systems. Also, experimental methods and designs are refined iteratively as insights into the system under study accumulate. With respect to building an IT infrastructure to support Life Sciences research operations, this translates to massive, complex data sets and frequently changing requirements. Naturally, standardization of data structures and processes tends to be difficult in such an environment and agility is key, both with respect to the software tools to use and the development methods to adopt.

Note that it is only the research – or “discovery” – domain within the large field of Life Sciences IT which poses this very special set of challenges. Large parts of the Healthcare industry, for instance, are tightly regulated, resulting in very different constraints on their supporting IT infrastructure.

There is a nascent field called “Discovery Informatics” which is devoted to applying computer science to advance discovery across all scientific disciplines. The field is so nascent, in fact, that Wikipedia has nothing to say about it. The best definition I could find is this one from William W. Agresti [1]:

“Discovery Informatics is the study and practice of employing the full spectrum of computing and analytical science and technology to the singular pursuit of discovering new information by identifying and validating patterns in data.”

It is at the intersection of Life Sciences and Discovery Informatics where this blog is trying to make a contribution – and, despite its length, the term “Life Sciences Discovery Informatics” seems the best way to describe this very special field.

Footnotes    (↵ returns to text)
  1. Communications of The ACM – CACM , vol. 46, no. 8, pp. 25-28, 2003

One thought on ““Life Sciences Discovery Informatics” ?!

  1. Pingback: KNIME and REST: A dream team for Life Sciences Discovery Informatics « Bits and Bases

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