Transcript genomic analysis of regulatory network dynamics reveals

genomic analysis of regulatory
network dynamics reveals large
topological changes
Paper Study
Speaker: Cai Chunhui
Sep 21, 2004
Introduction
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The dynamics of a biological network on a
genomic scale is presented by way of integration
of transcriptional regulatory information and
gene-expression data for multiple conditions in
Saccharomyces cerevisiae.
SANDY is developed as a new approach for the
statistical analysis of network dynamics,
combining well-known global topological
measures, local motifs and newly derived
statistics.
Main Work
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Integrate gene-expression data for the
following five conditions: cell cycle,
sporulation, diauxic shift, DNA damage and
stress response.
Fig. 1 represents the first dynamic view of a
genome-scale network: the sub-networks
active under different cellular conditions and
standard statistics under different conditions
Main Work
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The follow-on statistics in SANDY (Fig. 2)
indicates several characteristics of the
regulatory system: scale-free, hubs would be
invariant features of the network across
conditions and the combinatorial transcription
factor usage while not the individual one
seems to be the key of the regulation of a
condition.
Result
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SANDY presents an approach to examine
biological network dynamics.
In refocusing to a dynamic perspective, the
author uncover substantial topological
changes in network structure, and capture
the essence of the transcriptional regulatory
data in a new way.
Further Study
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future experiments can be used to determine
condition-specific interactions directly.
Many of the concepts introduced could be
readily transferred to other types of biological
networks and complex sub-systems in
multicellular organisms (such as those
directing the circadian cycle and cellular
development).
SANDY
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SANDY (Statistical Analysis of Network
Dynamics) is spread into three parts:
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Well-known statistics (global
topological measures, local network motifs).
Newly-derived follow-on statistics
(hub usage, interchange index, TF usage).
Statistical validation with randomly
simulated networks
Good Point
(Local network motifs)
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There are three motifs which show the
precise inter-connections between a
small number of TFs and target genes
We are very interested in the local
network motifs, and thus find out the
way in which they are identified.
Good Point
(Local network motifs)
In order to identify the motifs, the author
constructed a pair of affinity matrices A and
B. Matrix A contained binary entries Aij where
a 1 indicated a regulatory interaction from TF
j to target gene i. Matrix B was a sub-matrix
of A, containing only the rows corresponding
to target genes that are TFs themselves.
Nodes and edges can be part of more than
one motif.