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Hi Jerry,
The first sentence in your post gave me great hope that perhaps the
flaws in the reductionistic mode of analysis were finally being spotlighted,
especially as applied to living systems...
Jerry Zhu wrote: One of the hottest research topic in Genome Science
is
the interaction between genes. Genetic Regulatory Network is one of the recent focuses to understand metabolic pathways and bioprocesses. But then I went on the net and did some research. I found a lot of
this kind of thing:
The traditional approach to research in
Molecular Biology has been an inherently local one, examining and collecting
data on a single gene, a single protein or a single reaction at a time. This is,
of course, the classical reductionist stance: to understand the whole, one must
first understand the parts. Over the years, this approach has led to remarkable
achievements, allowing us to make highly accurate biochemical models of such
favorites as bacteriophage Lambda.
However, with the advent of the "Age of Genomics" an entirely new class
of data is emerging. Can we really expect to construct a detailed biochemical
model of, say, an entire yeast cell with some 6000 genes (only about 1000 of
which were defined before sequencing started, and about 50% of which are clearly
related to other known genes), by analyzing each gene and determining all the
binding and reaction constants one by one? Likewise, from the perspective of
drug target identification for human disease, we cannot realistically hope to
characterize all the relevant molecular interactions one-by-one as a requirement
for building a predictive disease model.
There is a need for methods that can handle this data in a global
fashion, and that can analyze such large systems at some intermediate level,
without going all the way down to the exact biochemical reactions. At the very
least, such an analysis could help guide the traditional pharmacological and
biochemical approaches towards those genes most worthy of attention among the
thousands of newly discovered genes. Ideally, a sufficiently predictive and
explanatory model at an intermediate level could obviate the need for an exact
understanding of the system at the biochemical level.
They don't want to understand the interaction between the genes as
a causal force in gene _expression_. They simply want a way to make their number
crunching easier and less "complicated". They still don't get it.
Frustration!
Judith
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