Download Functional genomics and inferring regulatory pathways

Functional genomics and inferring
regulatory pathways with gene
expression data
Principle of Epistasis Analysis
•Determines order of influence
•Used to reconstruct pathways
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CBS, Department of Systems Biology
27803::Systems Biology
Experimental Design:
Single vs Double-Gene Deletions
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CBS, Department of Systems Biology
27803::Systems Biology
Epistasis Analysis Using Microarrays to
Determine the Molecular Phenotypes
Van Driessche et al. Epistasis
analysis with global
transcriptional phenotypes.
Nature Genetics 37, 471 - 477
(2005)
Time series
expression (0-24hrs)
every 2hrs
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CBS, Department of Systems Biology
27803::Systems Biology
Pathway Reconstruction
Expression data
Known pathway
Inferred pathway
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CBS, Department of Systems Biology
27803::Systems Biology
Expression Profiling in 276
Yeast Single-Gene Deletion Strains
“The Rosetta Compendium”
• Only 19 % of yeast genes are essential in rich media, Giaever
et. al. Nature (2002)
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CBS, Department of Systems biology
Presentation name
17/04/2008
Clustered Rosetta Compendium Data
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CBS, Department of Systems Biology
27803::Systems Biology
Gene Deletion Profiles Identify Gene
Function and Pathways
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CBS, Department of Systems Biology
27803::Systems Biology
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CBS, Department of Systems Biology
27803::Systems Biology
Systematic phenotyping
Barcode
(UPTAG):
Deletion
CTAACTC
TCGCGCA
TCATAAT
yfg1D
yfg2D
yfg3D
…
Strain:
Rich media
Growth 6hrs
in minimal media
(how many doublings?)
Harvest and label genomic DNA
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CBS, Department of Systems Biology
27803::Systems Biology
Microarrays for functional genomics
Hillenmeyer M, et al., Science 2008
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CBS, Department of Systems Biology
27803::Systems Biology
Explaining deletion effects
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CBS, Department of Systems Biology
27803::Systems Biology
Relevant Relationships
(that need to be explained)
• Rosetta compendium used
• 28 deletions were TF (red
circles)
– 355 diff. exp. genes
(white boxes)
– P < 0.005
– 755 TF-deletion effects
(grey squiggles)
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CBS, Department of Systems biology
Presentation name
17/04/2008
Evidence for pathway inferrence
• Step 1: Physical Interaction Network
– Y2H, chIP-chip
• Step 2: Integrate state data
– Measure variables that are a function of the
network (gene expression)
– Monitor these effects after perturbing the network
(TF knockouts).
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CBS, Department of Systems Biology
27803::Systems Biology
Inferring regulatory paths
Direct
Indirect
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CBS, Department of Systems Biology
=
=
27803::Systems Biology
Annotate: inducer or repressor
OR
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CBS, Department of Systems Biology
27803::Systems Biology
Annotate: Inducer or Repressor
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CBS, Department of Systems Biology
27803::Systems Biology
Computational methods
• Problem Statement:
– Find regulatory paths consisting of physical interactions that “explain”
a functional relationship
• Method:
– A probabilistic inference approach
– Yeang, Ideker et. al. J Comp Bio (2004)
• To assign annotations
• Formalize problem using a factor graph
• Solve using max product algorithm
– Kschischang. IEEE Trans. Information Theory (2001)
– Mathematically similar to Bayesian inference, Markov random fields,
belief propagation
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CBS, Department of Systems Biology
27803::Systems Biology
Resolving ambiguous interactions networks
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CBS, Department of Systems Biology
27803::Systems Biology
Inferred Network Annotations
A network with
ambiguous annotation
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CBS, Department of Systems Biology
27803::Systems Biology
Inferring Regulatory Role
50/132 protein-DNA
interactions had been
confirmed in low-throughput
assays (Proteome
BioKnowledge Library)
Inferred regulatory roles
(induction or repression) for
48 out of 50 of these
interactions agreed with
their experimentally
determined roles.
(96%, binomial p-value <
1.22 × 10-7)
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CBS, Department of Systems Biology
27803::Systems Biology
Target experiments to one network region
Expression for: sok2, hap4, msn4, yap6
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CBS, Department of Systems Biology
27803::Systems Biology
Expression of Msn4 targets
Average Z-score
Negative control
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CBS, Department of Systems Biology
27803::Systems Biology
Expression of Hap4 targets
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CBS, Department of Systems Biology
27803::Systems Biology
Yap6 targets are unaffected
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CBS, Department of Systems Biology
27803::Systems Biology
Refined network model
• Caveats
– Assumes target genes are
correct
– Only models linear paths
– Combinatorial effects
missed
– Measurements are for
“normal” growth condition
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CBS, Department of Systems Biology
27803::Systems Biology
Using this method of choosing
the next experiment
• Is it better than other methods?
• How many experiments?
• Run simulations vs:
– Random
– Hubs
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CBS, Department of Systems Biology
27803::Systems Biology
Simulation results
# simulated deletions profiles used to learn a “true” network
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CBS, Department of Systems Biology
27803::Systems Biology