Rule Book: Guidelines to a Mathematical Biology
When faced with challenging research problems, nothing may be more comforting than
having access to a collection of helpful guidelines.
This explains the appearance of a Rule Book.
Biomedical research is the art of reaching into biology,
pulling something out, putting numbers on it, and then telling a credible story.
A rule book calls attention to what we are actually doing and
frequently explains
things we need to know. Consider, if you will, the following
examples.
- In a mathematical biology, practically everything is done
with equations running on the best available research data. Why?
Only equations can deal successfully with the hierarchical
nature of biology and the remarkable relationships that ensue
there from. Just as parts are embedded parts, so too are
complexities embedded in complexities. Equations allow us
to
dig our way into these complexities and then return safely by the same
route.
- Stereology needs biochemistry and molecular biology just as
much as these disciplines need stereology. Why? Because
they can supply gold standards only when working
together in the same equations.
- A sizable portion of modern day research in biology is built on
a
foundation of semiquantitative data. By learning to
look at these data - and the assumptions behind them - through
the lens of a mathematical biology you will see how quickly this
shaky foundation collapses when put to the test. Why?
Semiquantitative data are not trustworthy because they don't play by the rules.
Table of Contents
-
Conceptual
Framework
-
Sampling
-
Hierarchical
Parts
-
Experiments as
Equations
-
Optimal Data
-
Interpretations
-
Gold Standards
-
Connections
-
Change
-
Bias and
Animal Variability
-
Counting
Molecules
-
Complexity
-
Reverse and Forward Engineering
-
Integrating Data
-
Mathematical Phenotypes
-
Dimensional Consistency
-
Standardization
-
Universal Databases
-
Semiquantitative Data
The Rule Book comes with an interesting piece of
software called the Concentration Trap. With it you
will quickly discover how easy it is to deprive semiquantitative
data of one of its
most treasured perks - the ability to gain the appearance of
respectability by attaching itself to a statistical significance.
By learning to run the program, you will be able to show that the
appearance of an increase is - in reality - often a decrease or a
decrease an increase. Consider a real-life scenario. If semiquantitative data can get it
right only about 50% of the time, then a significant difference
(P<0.05) will also get it right only 50% of the time. In
other words, semiquantitative data invariably carry a hidden probability layer that undermines the validity of a statistical outcome.
Using the concentration trap program, you will be able to estimate
the effects of this
hidden layer on the results coming from a wide range of published studies.
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