Wow, things are moving!

I've been following the advice of a wise man lately, "Seek first to understand before you seek to be understood." As such, I've met a bunch of smart people and dove into a bunch of research. I'm terribly behind in all my reading, but eager to keep plowing through it. Also, I'm behind in creating my first discussion tools. I guess I better start burning some midnight oil!

First, any serious student of the changes going on in Life Sciences should read "Information Theory and Evolution" by John Avery. This man has great perspective. Second, I found some great work from the annals of the history of IT management. My thesis is that we can understand and predict the disruption to traditional Life Sciences organizations by understanding the history of the disruption caused by Object Oriented programming.

Alan Kay, the godfather of OO technology (and also its greatest critic) was a mathematician and molecular biologist:

• "I thought of objects being like biological cells and/or individual computers on a network, only able to communicate with messages..."


• "In computer terms, Smalltalk is a recursion on the notion of computer itself. Instead of dividing "computer stuff" into things each less strong than the whole--like data structures, procedures, and functions which are the usual paraphernalia of programming languages--each Smalltalk object is a recursion on the entire possibilities of the computer. Thus its semantics are a bit like having thousands and thousands of computer all hooked together by a very fast network."


• "Object-oriented design is a successful attempt to qualitatively improve the efficiency of modeling the ever more complex dynamic systems and user relationships made possible by the silicon explosion."


• "I invented the term Object-Oriented, and I can tell you I did not have C++ in mind."

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Check out this article on the Lessons learned from an early, large OO development effort at IBM. The team size was about 150, a number thrown around as a common size for teams working on commercial Life Sciences research projects. On the one hand, some of the lessons learned provide insight into why mother nature manages her IT projects the way that she does to eliminate complexity. On the other hand, other lessons apply to the management issues that a Life Sciences CEO might face in managing an early, large scale Systems-based project.

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