Genetic Regulatory Network

[Jerry Zhu <***>]

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. GRNs act as
analog biochemical computers to specify the identity
and level of expression of a group of targeted genes.
Its output is the constellation of RNAs and proteins
encoded by target genes.  Time series expression data
obtained from DNA microarrays is one of the most
useful kinds of data used to construct and test GRNs.

There are numerous techniques to model GRNs or the
behavior of a cell: boolean networks, Petri nets,
Bayesian networks, cluster analysis etc. There are
even genetic/metabolic circuit networks where genes,
metabolic enzymes, and proteins are modeled as nodes
with the relationship between activation, inhibition
and mediation as links.  GRN models can be used to
identify genetic diseases and estimate the effects of
medications.

My doubt is that such efforts will fall into the fate
of the hype of artifical intelligence for the
following three reasons:

First, using mathematic model to describe celluar
process is non-holistic in the sense of ignoring the
matter aspect of life. Cellular systems are not
reducible to formal systems.

Second, mathematic based GRN models ignore the
hierarchical dynamics.  Current GRN use one level
mathematic models only.

Third, it is not historical.  Every form of life has a
history.  It is the history that answers why
questions.

My guesture is that today's development activities of
biological sciences such as molecular biology and all
social sciences are questionable.  The net effect of
such activities is the waste of resources and
intellectual work.  The focus of science today is
mostly deductively representing theories rather than
the understanding of inductive development efforts or
scientific activities.  

Jerry 




                
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