Biology Reference
In-Depth Information
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CONCLUDING REMARKS
Building on several decades of targeted classic genetics
approaches, unbiased high-throughput technologies are
beginning to generate a systems-level view of cellular
signaling networks. In this chapter we have reviewed
a number of experimental methods available to generate
a comprehensive 'parts list' of cellular signaling networks.
Further, we have described various approaches that can be
used to construct network models based on the phenotypic
signatures of each component. These techniques give us the
unprecedented opportunity to evaluate globally and
systematically the contribution of all genes to a specific
biological process. However, the implementation of these
methods is technically challenging and in some cases they
are best used in combination, as integration of data sets
increases the quality of the networks. Although the false
positive and false negative rates for networks generated
from high-throughput methods are currently relatively
high, new experimental techniques and new methods for
integrating multiple interacting data types will allow these
networks to become powerful predictive tools.
A global view of cellular networks holds great promise
in advancing our mechanistic understanding of how indi-
vidual genetic alterations, as well as combinations of gene
mutations, lead to a disease phenotype. For example,
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complex diseases such as diabetes, obesity, hypertension
and Crohn's disease. Comprehensive structure/function
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logical functions of many of the affected genes. Impor-
tantly, network analyses will facilitate the selection of
protein targets for therapeutic intervention based on the
underlying mechanisms of action. Furthermore, network
maps will shed light on how certain drug
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treatments. Eventually, generation of comprehensive
dynamic models of protein networks in response to signals
over time will allow scientists to quantitatively predict the
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