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Reconstruction of Transcriptional Regulatory Networks

Adapted from Chapter 4 of“Systems Biology: Properties of Reconstructed Networks”

byBernhard 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 behaviorExample: 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

CAP site Promoter Operator Structural genes for lactose -metobolizing enzymes

Operation statusCarbon Source

OFFNeither

OFFGlucose and Lactose

CAP RNA polymerase

ONmRNA

Lactose only

lac repressor

OFFGlucose only

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The GAL Regulon

Operation statusCarbon Source

RNA polymerase

ONmRNA

Galactose only

UASG Mig1 site GAL1 gene required for galactose metabolism

OFFGlucose and Galactose

Mig 1

Tup

OFF (basal)Neither Glucose nor Galactose

Gal4

Gal80

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Fundamental data types for TRNsComponent 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 high-throughput experiments to reverse-engineer (or “back-calculate”) regulatory circuits– Gene expression profiles for wild type and deletion strainsunder appropriate conditions (genetic perturbation)– Promoter sequence data and possibly consensus binding sitesfor TFs– Location analysis (ChIP-Chip) data on transcription factorbinding 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.

Transcription factors (104)

Metabolic genes (479)

Stimuli (102)

TFs regulating gene expression

Stimuli affecting TF activity

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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 !