Introduction
Background
The Enterprise Biology
Software Project looks for ways of using mathematics and technology to
accelerate learning and discovery in the life sciences. In the first release of the software
(Bolender, 2001a, 2001b), a stereology literature database was introduced as a
research tool for exploring complex problems in basic and clinical
research. Three general observations
came from that exercise.
1. Patterns of connectivity
appeared routinely in research data when the effects of disruptive variables
influencing stereological data were minimized.
These troublesome variables included bias and time.
2. A stereology literature
database can redefine the role of published data. Data from one research paper can influence - and be influenced by
- all other data entries in the database.
Moreover, data stored in a database can serve any purpose the user
wishes thereby ensuring both the short and long-term benefits of a research
publication.
3. The challenging task of
unraveling gene function requires data analysis at the level of complex systems. The database technology included in the
Enterprise Biology Software - effectively upgrades stereological data to this
level by adding a
Connection Model, as shown below.
Living
Animal
·
Many Preparative Steps
·
Many Methods
Stereological
Estimate
Change
Model Designed for Simple
Systems
Minimize
Effects of Disruptive Variables
Connection
Model Designed for Complex Systems
Patterns,
Principles, Gene Function, Etc.
The current release of the
software (EBS 2002) upgrades the stereology literature database through 2001
and explores additional ways of using the database as a research tool. We begin our continuing story with a timely
reality check.
Reality Check
In the current release, we
address a controversial question. Can
stereological data be trusted? The new
design-based methods of stereology have been widely heralded as ushering in an
era of unbiased stereology - a modern stereology. These new methods have enjoyed great success and they are enthusiastically
supported by the stereology community.
However, a reality check forces us to ask what can only be regarded as
an uncomfortable and unpleasant question. Do these new unbiased methods actually produce unbiased data? Consider cases A and B.
|
Case A
Living
Animal
· Many Preparative Steps
· Many Methods
Stereological
Estimate
Stereological
Data (Unbiased) |
Case B
Living
Animal
· Many Preparative Steps
· Many Methods
Stereological
Estimate
Stereological
Data (Biased) |
If we select Case A, then we
can safely assume that all the conditions of the unbiased methods are met
throughout the process of generating data without exception. Here the stereological data estimates
accurately reflect the structures in the living animal and there is no bias. If we select Case B, then we accept the view
that the procedures used to capture data from living animals introduce
bias. In this case, stereological data
do not accurately reflect the structures in the living animal because the data
carry a bias. Case A allows us to
overlook the presence of the troublesome variables bias and time, Case B does
not. Of the two, Case B seems closer to
the truth.
Amazing Secret
Is
the bias in biological data hiding an amazing secret? Probably, yes. A striking
clue comes from recent discoveries in molecular biology. Genome sequences taken from a variety of
animals reveal an astounding similarity.
In several cases, the genetic blueprints are nearly identical (Waterston
et al., 2002). Should this pattern of
similarity persist, then structures in different animals might differ largely
by size and phenotypic expression. In
other words, all structures based on the same genetic code can be expected to
be equivalent and scalable. A recent
article would seem to support this view.
When kidney precursor cells from a human were transplanted into a mouse
a small human kidney developed and functioned normally (Dekel et al.,
2003).