Why?    

If biology exists as a rule-based system, then why not treat biology as a mathematical problem?

We begin by setting up the problem – in a way not unlike what we did in our introductory math courses.  Recall that this consisted of first applying an algorithm that we deduced from our understanding of the problem and then plugged in the data to get the solution.  But, how can we apply this to biology? 

In biology, complexity – in a mathematical setting – defines the problem as a function of a complex data set.  Solving such a complexity seems to require data that capture biological parts, connections, and rules - simultaneously.  Given such data, biology becomes surprisingly accommodating in that it will set up the problem for us; all we have to do is know where to look, make the calculations, and explain the results.  Indeed, solving biology as a complexity turns out to be a surprisingly straightforward exercise.  

In practice, this process of complex problem solving consists of running published data through an information infrastructure designed specifically to set up and solve complexities.  In the current report (2011), we will apply this approach to a complex disease of the brain (schizophrenia) by mapping - with rules - the parts to the connections.  By interpreting the results with graphics and equations, both solutions and the interpretations quickly come into view.  In time, such a strategy will allow us to map vast numbers of parts as our computers become more powerful. 

At some point in time, we will find it impossible to continue to ignore a basic truth.  Namely, it takes a complexity to understand a complexity.    

Canyon X, Arizona