1. (genetic regulatory networks)
Genetic Networks
(genetic regulatory networks)
- a group of genes connected through transcription regulators encoded within the set of genes
Promoter X
gene X
Promoter Y
gene Y
operator X

2. (genetic regulatory networks)
Genetic Networks
(genetic regulatory networks)
- a group of genes connected through transcription regulators encoded within the set of genes
gene X X
gene Y

3. (genetic regulatory networks)
Genetic Networks
(genetic regulatory networks)
- a group of genes connected through transcription regulators encoded within the set of genes
gene X X
gene Y X Y

4. (genetic regulatory networks)
Genetic Networks
(genetic regulatory networks)
By convention we simplify these diagrams as follows:
gene X X X
gene Y Y X Y

5. (genetic regulatory networks)
Genetic Networks
(genetic regulatory networks) X Y
Denotes positive
regulation
Denotes negative
regulation Y Z

6. Regulation of flagella gene expression: A three tiered transcriptional hierarchy
Positive transcriptional regulators
Alternative sigma factors
Anti-sigma factors
Temporal regulation

7. The Flagella Transcription Hierarchy
CRP,H-NS,OmpR
other?
1. The Master Regulon
FlhCD

8. The Flagella Transcription Hierarchy
CRP,H-NS,OmpR
other?
1. The Master Regulon
2. The FlhCD Regulon
FlhCD
inside
outside
Basal Body
and Hook
FlgM
FliA
other?

9. The Flagella Transcription Hierarchy
CRP,H-NS,OmpR
other?
Chemotaxis
proteins
1. The Master Regulon
2. The FlhCD Regulon
3. The FliA Regulon
Motor
proteins
FlhCD
inside
outside
Basal Body
and Hook
FlgM
FliA
other?
Filament

10. The flhDC promoter integrates inputs from multiple environmental signals?
CRP - catabolite repression, carbohydrate metabolism
OmpR - osmolarity
IHF - growth state of cell?
HdfR - ?

11. FliA Regulation by FlgM
FlhDC expression leads to activation of Level 2 genes including the alternative sigma factor FliA and an anti sigma factor FlgM
FlgM accumulates in the cell and binds to FliA blocking its activity (i.e. interaction with RNA polymerase) preventing Level 3 gene expression.
Level 3 Genes
inside
outside

12. FliA Regulation by FlgM
Other level 2 genes required for Basal body and hook assembly are made and begin to assemble in the membrane.
Level 3 Genes
inside
outside
Basal Body
and Hook
Assembly

13. FliA Regulation by FlgM
The Basal body and hook assembly are completed.
Level 3 Genes
inside
outside
Completed Basal Body
and Hook

14. FliA Regulation by FlgM
The Basal body and hook assembly are completed.
FlgM is exported through the Basal Body and Hook Assembly
Level 3 Genes
inside
outside
Completed Basal Body
and Hook

15. FliA Regulation by FlgM
Level 3 gene expression is initiated.
FliA can interact with RNA polymerase and activate Level 3 gene expression.
FlgM is exported through the Basal Body and Hook Assembly.
Level 3 Genes
inside
outside
Completed Basal Body
and Hook

16. FliA Regulation by FlgM
Level 3 gene products are added to the motility machinery including the flagella filament, motor proteins and chemotaxis signal transduction system.
inside
outside
Filament

17. The “genetic network diagram” for the fla systemB C D E

18. The “genetic network diagram” for the fla system
flhCD
Level 1
fliL
fliE
fliF
flgA
flgB
flhB
n = 6
flgM
fliA
Level 2
fliD
flgK
fliC
meche
mocha
flgM
n = 6
Level 3

19. Using reporter genes to measure gene expression
RNA polymerase
Regulator
Organization of operon on chromosome.
flhD flhC
flhDC promoter

20. Using reporter genes to measure gene expression
RNA polymerase
Regulator
Organization of operon on chromosome.
flhD flhC
flhDC promoter
Clone a copy of the promoter into a reporter plasmid.
Reporter gene

21. Using reporter genes to measure gene expression
RNA polymerase
Regulator
flhD flhC
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.
Reporter gene
Note that the strain still has a normal copy of the genes.

22. Gene Expression in Populations Gene Expression in Single Cells
Multi-well plate reader
- sensitive, fast reading
- high-throughput screening
- liquid cultures
- colonies
- mixed cultures
Video microscopy
- “individuality”
- cell cycle regulation
- epigenetic phenomenon
Automation: Both approaches are amenable to high throughput robotics

23. Gene Expression in Single Cells: Cell to Cell Variability
Michael Elowitz, Rockefeller University

24. 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

25. Fluorescence of flagella reporter strains as a function of time
Class
Operon
Fluorescence
relative to max
0.6
0.1
This is the visualization of the cluster output
Clustering is a tool based on statistical algorithms that arrange genes or groups of genes according to the similarity of their expression profiles---Not the magnitude of their expression
Effective visual representation of the cluster output--such as this graphical representation---is necessary to assimilate patterns of expression
Normalized fluorescence data: fluorescence for each time point, divided by the maximal fluorescence for that gene over the time course--plotted as a log scale
scales from blue (low) to red (high)
operons are arranged according to the temporal clustering results
clustering the operons according to similarity in their expression profiles shows that the operons fall into clusters that correspond to the genetically defined classes 1,2,3
A temporal labelling procedure extends the algorithm to hierarchically place the clusters in an order according to the relative timing of their average expression profiles--shown here illustrates a
more elaborate temporal program than when examining the clusters of genes that corresond to the 3 classes of flagella genes--temporal labelling of this kind data places clusters in order of the timing of their expression
0.01
600
Time [min]

26. The order of flagellar gene expression is the order of assembly
Early
Cluster 1
Class 1 flhDC
Master regulator
Class 2 fliL
Class 2 fliE
Class 2 fliF
Class 2 flgA
Class 2 flgB
Class 2 flhB
Class 2 fliA
Cluster 2
Activator of class 3
Class 3 fliD
Class 3 flgK
Class 3 fliC
Class 3 meche
Class 3 mocha
Class 3 flgM
Cluster 3
Late

27. Simple Mechanism for Temporal Expression Within an Regulon
Induction of positive regulator
[protein]
Time
Promoters with decreasing affinity for regulator

28. Simple Mechanism for Temporal Expression Within an Regulon
[protein]

29. “You can not infer mechanism from pattern.”
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.”

30. 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

31. 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 M N X Y Z W O V U

32. 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 Leibler
Nature, 2000

33. Engineered Gene Circuits: The Repressilator

34. 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

35. 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.