Genetic Networks (genetic regulatory networks)
description
Transcript of Genetic Networks (genetic regulatory networks)
Genetic Networks(genetic regulatory networks)
- a group of genes connected through transcription regulators encoded within the set of genes
gene X
gene Y
operator X
Promoter X
Promoter Y
Genetic Networks(genetic regulatory networks)
- a group of genes connected through transcription regulators encoded within the set of genes
gene X
gene Y
X
Genetic Networks(genetic regulatory networks)
- a group of genes connected through transcription regulators encoded within the set of genes
gene X
gene Y
X
X
Y
Genetic Networks(genetic regulatory networks)
gene X
gene Y
X
X
Y
X
Y
By convention we simplify these diagrams as follows:
Genetic Networks(genetic regulatory networks)
X
Y
Y
Z
Denotes positive regulation
Denotes negative regulation
Regulation of flagella gene expression: A three tiered transcriptional hierarchy
Positive transcriptional regulators
Alternative sigma factors
Anti-sigma factors
Temporal regulation
The Flagella Transcription Hierarchy
1. The Master Regulon
FlhCD
CRP,H-NS,OmpRother?
The Flagella Transcription Hierarchy
1. The Master Regulon
2. The FlhCD Regulon FlhCD
FliAFlgM
Basal Bodyand Hook
CRP,H-NS,OmpRother?
other?
outside
inside
The Flagella Transcription Hierarchy
1. The Master Regulon
2. The FlhCD Regulon
3. The FliA Regulon
FlhCD
FliAFlgM
Basal Bodyand Hook
Filament
Chemotaxisproteins
Motorproteins
CRP,H-NS,OmpRother?
other?
outside
inside
flhDC
The flhDC promoter integrates inputs from multiple environmental signals
?
CRP - catabolite repression, carbohydrate metabolismOmpR - osmolarityIHF - growth state of cell?HdfR - ?
FliA Regulation by FlgM
outside
inside
FlhDC expression leads to activation of Level 2 genes including the alternative sigma factor FliA and an anti sigma factor FlgM
Level 3 Genes
FlgM accumulates in the cell and binds to FliA blocking its activity (i.e. interaction with RNA polymerase) preventing Level 3 gene expression.
FliA Regulation by FlgM
outside
inside
Other level 2 genes required for Basal body and hook assembly are made and begin to assemble in the membrane.
Level 3 Genes
Basal Bodyand HookAssembly
FliA Regulation by FlgM
outside
inside
The Basal body and hook assembly are completed.
Level 3 Genes
Completed Basal Bodyand Hook
FliA Regulation by FlgM
outside
inside
The Basal body and hook assembly are completed.
Level 3 Genes
Completed Basal Bodyand Hook
FlgM is exported through the Basal Body and Hook Assembly
FliA Regulation by FlgM
outside
inside
Level 3 gene expression is initiated.
Level 3 Genes
Completed Basal Bodyand Hook
FlgM is exported through the Basal Body and Hook Assembly.
FliA can interact with RNA polymerase and activate Level 3 gene expression.
FliA Regulation by FlgM
outside
inside
Filament
Level 3 gene products are added to the motility machinery including the flagella filament, motor proteins and chemotaxis signal transduction system.
A
DCB
E
The “genetic network diagram” for the fla system
flhCD
fliAflgM
fliLfliEfliFflgAflgBflhB
n = 6
n = 6
fliDflgKfliCmechemochaflgM
Level 1
Level 3
Level 2
The “genetic network diagram” for the fla system
flhD flhC
flhDC promoter
Regulator
RNA polymerase
Using reporter genes to measure gene expression
Organization of operon on chromosome.
flhD flhC
flhDC promoter
Regulator
RNA polymerase
Using reporter genes to measure gene expression
Organization of operon on chromosome.
Reporter gene
Clone a copy of the promoter into a reporter plasmid.
flhD flhC
Regulator
RNA polymerase
Using reporter genes to measure gene expression
Reporter gene
Both the flhDC genes and the reporter plasmid are regulated in the same way and thus the level of the reporter indicates the activity of the promoter.
Note that the strain still has a normal copy of the genes.
Gene Expression in Populations
Gene Expressionin Single Cells
Video microscopy
- “individuality”- cell cycle regulation- epigenetic phenomenon
Multi-well plate reader
- sensitive, fast reading- high-throughput screening- liquid cultures- colonies- mixed cultures
Automation: Both approaches are amenable to high throughput robotics
Gene Expression in Single Cells: Cell to Cell Variability
Michael Elowitz, Rockefeller University
1- Gene Expression Profiling With Real Promoters
Modeling Genetic Networks- from small defined systems to genome wide -
Small Defined Networks High Throughput / High Quality Expression Profiling
Modeling, Simulation
Time [min]
Fluorescencerelative to max
0.01
0.1
0.6
Class
Operon
0 600
Fluorescence of flagella reporter strains as a function of time
Cluster 1
Cluster 2
Cluster 3
Class 1 flhDC
Class 2 fliLClass 2 fliEClass 2 fliFClass 2 flgAClass 2 flgBClass 2 flhBClass 2 fliAClass 3 fliDClass 3 flgKClass 3 fliC
Class 3 mecheClass 3 mochaClass 3 flgM
Early
Late
Activator of class 3
Master regulator
The order of flagellar gene expression is the order of assembly
Time
[protein]
Simple Mechanism for Temporal Expression Within an Regulon
Induction of positive regulator
Promoters with decreasing affinity for regulator
[protein]
Simple Mechanism for Temporal Expression Within an Regulon
Using Expression Data to Define and Describe Regulatory Networks
With the flagella regulon, current algorithms can distinguish Level 2 and Level 3 genes based on subtleties in expression patterns not readily distinguished by visual inspection.
Using our methods for expression profiling (sensitive, good time resolution) we have been able to demonstrate more subtle regulation than previously described.
Different mechanisms can give rise to different patterns- in this case temporal patterns arise by transcription hierarchies (I.e. Level 1 Level 2 Level 3) and by differences in binding site affinities within a level.
“You can not infer mechanism from pattern.”
The problem of binding sites:
Aoccdrnig to a rscheearch at an Elingsh uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng isThat frist and lsat ltteer is at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit porbelm. Tihs is bcuseae we do not raed ervey lteter by it slef but the wrod as a wlohe.
Ceehiro
That'll srecw the splelchekcer
Methods such as the one described here or DNA microarrays can be used to measure expression of all the genes in a cell simultaneously.
Reverse Engineering challenge – can we use expression data to infer genetic networks?
E
A
D
B
C
F
U
Z
W
Y
X
V
M
N
O
Engineered Gene Circuits: The Repressilator
A 3-element negative feedback transcriptional loop that should have sustained oscillatory behavior under the appropriate conditions:• strong promoters.• tight transcription repression with low leakiness.• comparable protein and mRNA decay rates
Michael Elowitz & Stanislas LeiblerNature, 2000
Engineered Gene Circuits: The Repressilator
• Periodic synthesis of GFP (150 minutes, 3x cell cycle time)• The state of the network is transmitted to the siblings• Average decorrelation time = 95 +/- 10 minutes
Engineered Gene Circuits: The Repressilator
Some lessons learned:
Even well studied systems still have elements of surprise!
The best engineered systems do not always live up to there predicted behavior (we often do not know as much as we think!).
Predictive ability is limited because of difficult in predicting quantitative properties.
The interfacing of modeling with experiments reveals much more information about biological systems that neither will do alone.