1. Building and Analyzing Genome-Wide Gene Disruption Networks
J. Rung, T. Schlitt, et al. (2002)
Presented by Sean Whalen, 2/26/03

2. [ chocobospore.org ][ 11/18/2018 ]
Outline
What is a gene network
What is a disruption network
Building the network
Observations
Degree distribution
Connectivity
Review
Conclusions
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3. [ chocobospore.org ][ 11/18/2018 ]
What is a gene network?
Directed Acyclic Graph (DAG)
Nodes/Vertices=Objects, Edges/Arcs=Relationships
Arbitrary meaning is assigned, in order to visualize relationships in a system (and acquire knowledge)
Gene networks simply model genetic relationships
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More on Gene Networks
How to represent the network? Arbitrary.
Example: Edge between nodes means parent codes for transcription factor
Example: Edge between nodes means change in expression level of parent affects level of child
Different modeling methods
Bayesian, Dynamic Bayesian
Problem: only deals with small data sets
This paper’s method: simple, genome-wide analysis, demonstrated biologically meaningful (yeast)
[ chocobospore.org ][ 11/18/2018 ]

5. What is a disruption network?
Gene network built from expression data (mutant strain vs. control)
Nodes are genes, edges indicated a causal change in expression level
Represented as a matrix
A discretized matrix is built from this matrix, to infer connectivity properties
Disruption network=graph representation of discretized matrix
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Building the Network
Expression data matrix
rij = log( lij / cij )
rij = jth element of ith row
l = exp. level in mutant
c = exp. level in control
Discretized matrix
Expression level up, down, or unchanged
Normalize rij, adjust for gene-specific standard deviation
Select cutoff level γ [2..4]
Expression matrix → Normalize → Select Cutoff → Discrete Matrix
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7. Building the Network (cont.)
Disruption network γ' is representation of discretized network as a graph
Edge between gi and gj if dij ≠ 0
Label edge as down regulating if dij=-1, up regulating if dij=1. Nodes labeled w/gene names
Expression data from all genes in a yeast mutant (single gene deletion) taken over 300 experiments w/63 control experiments
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Matrix → Graph Example A B C up
down
Gene A B C 1 -1
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Observations
High out degree = influence many other genes
High in degree = complex regulation
Distribution of total degree follows power law (scale-free topology)
50% of genes show change in expression with single deletion
Few genes with high in AND out degree
Strongy connected subnets (hubs) are evolutionally more conserved
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Degree Distribution
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Out Degree vs. In Degree
The point? Rare for node to have high ranked in degree AND out degree.
Only 1 node’s in degree is in the top 50% of in degrees,
AND out degree is in top 50% of out degrees.
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Connectivity
How connected is the graph with different γ values?
γ<3, one big component
Remove top 1%, 5%, and 10% of highest degree genes
For 3<γ<3.6, biggest component still order of magnitude higher
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13. Sample hub (γ=4, r=down, g=up)
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Review
A disruption networks is a graph representation of a discretized expression matrix, with a degree cutoff γ
Allows genome-wide analysis
Power-law distribution of edges
High out degree=gene encodes regulatory proteins
High in degree=gene involved in metabolism
[ chocobospore.org ][ 11/18/2018 ]

15. [ chocobospore.org ][ 11/18/2018 ]
Conclusions
Disruption networks suggest scale free topology in gene regulatory networks
Dominated by single large component (hub)
Looking for subnets containing genes involved in a process allowed prediction of genes with similar functions
DNs offer a different perspective of expression data than tradition methods such as heirarchical clustering
[ chocobospore.org ][ 11/18/2018 ]