genomic analysis of regulatory network dynamics reveals large topological changes

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genomic analysis of regulatory network dynamics
reveals large topological changes

• Paper Study
• Speaker Cai Chunhui
• Sep 21, 2004

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Introduction

• 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.

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Main Work

• 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

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Main Work

• 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.

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Result

• 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.

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Further Study

• 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).

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SANDY

• SANDY (Statistical Analysis of Network Dynamics)
  is spread into three parts
• 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

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Good Point (Local network motifs)

• 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.

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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.

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