Why?    

Have you ever wondered why biology is so much better at its job than we are at ours?  What does it get right that we don’t?  The answer may surprise you.  It has learned how to gather, use, and interpret information correctly.  This is the lesson we can now learn from an information infrastructure.  Allow me to explain.

We as investigators explore biology by devising models to supply and interpret biological information that comes to us in the form of data.  Biology continues to outperform us because it already knows how to match all of its models (rules) to its information (data).  We arrive at the playing field with two principal shortcomings.  First we fail to understand that our primary role is that of an observer and second we are genuinely inept at matching our models to our data. 

Our goal here is quite straightforward.  We – as players - want to become just as skillful as biology at playing the game.  Exactly, what game is biology playing?  It uses a vast collection of parts and its remarkable engineering skills to assemble complexities with advantageous properties.  As a complex adaptive system, it continually upgrades or creates new complexities as the need arises.  In effect, biology seems to be playing a complexity-emergent properties game.  If true, then we - as researchers – can profit by learning to play this game as well. 

Although biological stereology - supported with advanced technologies - is rapidly learning how to play this game, the big players will ultimately be biochemistry and molecular biology, largely because of their ability to generate vast quantities of research data. 

There is, however, a small problem.  These molecular disciplines have allowed themselves - unwittingly - to become caught in a catch-22.  They are producing huge amounts of semiquantitative data that bear little if any resemblance to the quantitative data of biology.  As accurate descriptors or predictors of biology, semiquantitative and quantitative data tend to be mutually exclusive.  Since our new model model for systems biology (SB2) includes molecular data, their shortcomings become ours as well.  This means that before we can tap into the molecular data of these disciplines, we have to find a way to release the catch-22.    

Consider this.  Can we bring biochemistry and molecular biology into the game by leveraging what we are learning about biological complexity with our stereological data?  Yes, I think so.  The new connection phenotypes offer the promise of turning semiquantitative data into quantitative data – provided the sampling is unbiased.  Recall that genes control both the amounts and the proportions of the parts they produce.  Since connection phenotypes can extract quantitative proportions from semiquantitative data, we can at least bring biochemistry and molecular biology into the game as minor players.  To become major players, however, someone will have to figure out how to estimate unbiased counts of molecules in an in vivo setting with hierarchical connections.

Glacier National Park, Montana