Download regulatory circuits

Reconstruction of
Transcriptional Regulatory Networks
Adapted from Chapter 4 of
“Systems Biology: Properties of Reconstructed Networks”
by
Bernhard O.Palsson
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What is Transcriptional regulation ?
• Transcriptional regulatory networks (TRNs) are the on-off switches
at the gene level
Input
Signals
Regulating
component
Changed
RNA and
protein output
Changed cell
behavior and
structures
Adapted from http://genomicsgtl.energy.gov/science/generegulatorynetwork.shtml
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Why do we care about Regulation?
Regulation has a significant effect on cell behavior
Example: E. coli
– Estimated 400 regulatory genes
– 178 regulatory and putative regulatory genes found in genome
– 690 transcription units (contiguous genes with a common expression
condition, promoter and terminator) identified in RegulonDB
– Will have a major effect on model predictions of cellular behavior
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Hierarchy in Transcriptional Regulation
genes
operon
Regulon
Stimulon
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The lac Operon
Carbon Source
Operation status
lac repressor
Glucose only
OFF
CAP
RNA polymerase
Lactose only
ON
mRNA
Neither
OFF
Glucose and
Lactose
OFF
CAP site
Promoter
Operator
Structural
genes for
lactose metobolizing
enzymes
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The GAL Regulon
Carbon Source
Operation status
Gal80
Gal4
Neither Glucose
nor Galactose
OFF (basal)
RNA polymerase
ON
Galactose only
mRNA
Tup
Mig 1
Glucose and
Galactose
OFF
UASG
Mig1 site
GAL1 gene
required for
galactose
metabolism
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Fundamental data types for TRNs
Component data
– Binding sites, transcription factor (TF) molecules etc.
Interaction data
– Links are formed by chemical interactions
– DNA-protein,protein-protein,metabolite-RNA
– Positive and negative controls
Network state data
– Reconstructed networks have functional states
– Controls for network states assessed by perturbation experiments
• Genetic/environmental/systemic
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Regulatory vs Metabolic Circuits
Regulatory circuits are poorly characterized
• Less-well understood
• Qualitative statements vs. “hard” stoichiometry
• Not mechanistically conserved across different organisms
Regulatory circuits are more complex
• Multiple effects/transcription factor (TF)
• Multiple regulators/gene
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Key Considerations for TRN
Reconstruction
• How to represent regulatory information?
– Is transcription regulation Boolean (switch-like) or continuous?
– Should transcription be thought of as a stochastic or
deterministic process?
• What constitutes significant regulation?
– Many extracellular signals can affect expression level of a gene.
– Which signals are actually physiologically significant?
• Problems with experimental data in the literature:
– Experiments done under different conditions (e.g. strain background)
– Typically experimentalists concentrate on studying well-known
TF/target pairs in great detail
– In vivo vs in vitro
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Bottom-up Reconstruction
• Pool genomic, biochemical and physiological data, inferring functions
where necessary.
– include regulatory rules
• Represent rules using Boolean logic, kinetic theory and the like.
• Analyze separately or together with metabolic network as a
metabolic/regulatory model.
• Use model to make predictions about the behavior and emergent
properties of the system
– predictions should be seen as hypotheses which must be tested
experimentally.
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Top-down Reconstruction
• Problems with bottom-up reconstruction:
– Many (most?) TF targets are not characterized
– Tedious process, because informative databases are rare
• Alternative approach: Utilize data from well-designed highthroughput experiments to reverse-engineer (or “back-calculate”)
regulatory circuits
– Gene expression profiles for wild type and deletion strains
under appropriate conditions (genetic perturbation)
– Promoter sequence data and possibly consensus binding sites
for TFs
– Location analysis (ChIP-Chip) data on transcription factor
binding sites
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Issues with Top-down Reconstruction
• Very complex models and algorithms are required to reverse engineer
regulatory circuits
– Computational issues: Explosion in the number of structures
– Model complexity issues: Explosion in the number of parameters
– Optimality issues: Only locally optimal circuits can be found
• Data is not usually available in sufficient quantities or with appropriate
quality – computational and experimental people usually don’t work
together 
• Currently these methods are primarily used to create hypotheses about
potential targets of TFs
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Combining knowledge-based and data-based
regulatory network reconstruction strategies.a
a
Herrgård M.J. et al , Current Opinion in Biotechnology,2004,15:70-77
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Graphical Representation of Boolean TRNs in
E. coli.
Stimuli (102)
Stimuli affecting TF activity
Transcription factors (104)
TFs regulating gene expression
Metabolic genes (479)
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Summary
• Transcriptional regulatory networks determine the expression state
of a genome
• These networks are presently incompletely defined
• Approaches to regulatory reconstruction are still being developed
(especially top-down)
• Models of TRNs will help unravel the “logic” of gene circuits
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Thank you !
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