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.