Download Transcription factor binding location analysis (chIP

Schedule for the Afternoon
13:00
13:30
14:30
14:45
15:15
15:45
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13:30
14:30
14:45
15:15
15:45
16:00
CBS, Department of Systems Biology
ChIP-chip lecture
Exercise
Break
Regulatory pathways lecture
Exercise (complete previous exercises)
Wrap up
27803::Systems Biology
Microarrays for transcription factor
binding location analysis (chIP-chip)
and the “Active Modules” approach
Protein-DNA interactions: ChIP-chip
Simon et al., Cell 2001
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CBS, Department of Systems Biology
Lee et al., Science 2002
27803::Systems Biology
ChIP-chip Microarray Data
Differentially
represented
intergenic regions
provides evidence
for protein-DNA
interaction
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CBS, Department of Systems Biology
27803::Systems Biology
Network representation of
TF-DNA interactions
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CBS, Department of Systems Biology
27803::Systems Biology
Dynamic role of transcription factors
Harbison C, Gordon B, et al. Nature 2004
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CBS, Department of Systems Biology
27803::Systems Biology
Mapping transcription factor binding sites
Harbison C, Gordon B, et al. Nature 2004
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CBS, Department of Systems Biology
27803::Systems Biology
Affymetrix tiling arrays
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CBS, Department of Systems Biology
27803::Systems Biology
9
CBS, Department of Systems Biology
27803::Systems Biology
10
CBS, Department of Systems Biology
27803::Systems Biology
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CBS, Department of Systems Biology
27803::Systems Biology
ChIP-Seq with Illumina (Solexa) Genome Analyzer
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CBS, Department of Systems Biology
27803::Systems Biology
Integrating gene expression data
with interaction networks
Data Integration
Need computational
tools able to distill
pathways of interest
from large molecular
interaction databases
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CBS, Department of Systems Biology
27803::Systems Biology
Types of information to integrate
• Data that determine the network (nodes and edges)
– protein-protein
– protein-DNA, etc…
• Data that determine the state of the system
– mRNA expression data
– Protein modifications
– Protein levels
– Growth phenotype
– Dynamics over time
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CBS, Department of Systems Biology
27803::Systems Biology
Network perturbations
• Environmental:
– Growth conditions
– Drugs
– Toxins
• Genetic:
– Gene knockouts
– Mutations
– Disease states
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CBS, Department of Systems Biology
27803::Systems Biology
Finding activated modules/pathways in a
large network is hard
• Finding the highest scoring sub-network is NP hard, so we use heuristic
search algorithms to identify a collection of high-scoring sub-networks
(local optima)
• Simulated annealing and/or greedy search starting from an initial subnetwork “seed”
• Considerations: Local topology, sub-network score significance (is score
higher than would be expected at random?), multiple states (conditions)
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CBS, Department of Systems Biology
27803::Systems Biology
Activated Subgraphs
Ideker T, Ozier O, Schwikowski B, Siegel AF.
Discovering regulatory and signaling circuits in
molecular interaction networks.
Bioinformatics. 2002;18 Suppl 1:S233-40.
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CBS, Department of Systems Biology
27803::Systems Biology
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CBS, Department of Systems Biology
27803::Systems Biology
Network based classifier of cancer
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CBS, Department of Systems Biology
27803::Systems Biology
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CBS, Department of Systems Biology
27803::Systems Biology