Why?    

Why do we want biology to become a mathematical science?  It's the quickest way of finding out how biology plays by the rules.  Consider our options.  We can either derive biology from first principles or we can discover these principles by reverse engineering.  The second choice, which comes from a data-driven biology, is the easier of the two options because we already know how to do it. 

How do we reverse engineer biology?  We need to find the most effective data in the literature, move them into a relational database, and standardize them.  Next, we minimize methodological biases and animal variability by forming data ratios, fit these ratios to equations, and then use the equations - as working rules - to assemble biological blueprints or to engineer (reverse or forward) biology. 

Although this plan may seem overly simple, it none-the-less is proving to be quite effective at delivering the desired outcomes.  We now know how to predict biology - upstream and down - from a single seed value, to reverse and forward engineer biological parts, and to assemble a blueprint that shows the stoichiometry of the parts as ratios of small whole numbers.

By constructing a biological blueprint spanning 14 levels of the structural hierarchy, we can see exactly how biology assembles its parts by rule.  Moreover, by leveraging the clues coming from the first 14 levels, the remaining two levels (molecules and genes) can now be captured with hybrid hierarchy equations.  What does this mean?  It tells us that we now have a workable strategy for forward and reverse engineer biology from one end of the hierarchy to the other - creating a free flow of information from genes to organisms.  In effect, this platform becomes a promising candidate for a mathematical systems biology.