Genetic Networks genetic regulatory networks a group of

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Genetic Networks (genetic regulatory networks) - a group of genes connected through transcription regulators

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 operator X gene Y

Genetic Networks (genetic regulatory networks) - a group of genes connected through transcription regulators

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

Genetic Networks (genetic regulatory networks) - a group of genes connected through transcription regulators

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

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

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

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

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

Regulation of flagella gene expression: A three tiered transcriptional hierarchy Positive transcriptional regulators Alternative

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 CRP, H-NS, Omp. R other? Flh.

The Flagella Transcription Hierarchy 1. The Master Regulon CRP, H-NS, Omp. R other? Flh. CD

The Flagella Transcription Hierarchy 1. The Master Regulon 2. The Flh. CD Regulon CRP,

The Flagella Transcription Hierarchy 1. The Master Regulon 2. The Flh. CD Regulon CRP, H-NS, Omp. R other? Flh. CD inside outside Flg. M Fli. A other? Basal Body and Hook

The Flagella Transcription Hierarchy 1. The Master Regulon 2. The Flh. CD Regulon CRP,

The Flagella Transcription Hierarchy 1. The Master Regulon 2. The Flh. CD Regulon CRP, H-NS, Omp. R other? Chemotaxis proteins Motor proteins Flh. CD inside outside Flg. M Fli. A Basal Body and Hook other? 3. The Fli. A Regulon Filament

The flh. DC promoter integrates inputs from multiple environmental signals flh. DC ? CRP

The flh. DC promoter integrates inputs from multiple environmental signals flh. DC ? CRP - catabolite repression, carbohydrate metabolism Omp. R - osmolarity IHF - growth state of cell? Hdf. R - ?

Fli. A Regulation by Flg. M Flh. DC expression leads to activation of Level

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

Fli. A Regulation by Flg. M Other level 2 genes required for Basal body

Fli. A Regulation by Flg. M 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

Fli. A Regulation by Flg. M The Basal body and hook assembly are completed.

Fli. A Regulation by Flg. M The Basal body and hook assembly are completed. Level 3 Genes inside outside Completed Basal Body and Hook

Fli. A Regulation by Flg. M The Basal body and hook assembly are completed.

Fli. A Regulation by Flg. M The Basal body and hook assembly are completed. Flg. M is exported through the Basal Body and Hook Assembly Level 3 Genes inside outside Completed Basal Body and Hook

Fli. A Regulation by Flg. M Level 3 gene expression is initiated. Flg. M

Fli. A Regulation by Flg. M Level 3 gene expression is initiated. Flg. M is exported through the Basal Body and Hook Assembly. Fli. A can interact with RNA polymerase and activate Level 3 gene expression. Level 3 Genes inside outside Completed Basal Body and Hook

Fli. A Regulation by Flg. M Level 3 gene products are added to the

Fli. A Regulation by Flg. M Level 3 gene products are added to the motility machinery including the flagella filament, motor proteins and chemotaxis signal transduction system. inside outside Filament

The “genetic network diagram” for the fla system A B C D E

The “genetic network diagram” for the fla system A B C D E

The “genetic network diagram” for the fla system Level 1 flh. CD fli. L

The “genetic network diagram” for the fla system Level 1 flh. CD fli. L fli. E fli. F flg. A flg. B flh. B n=6 flg. M fli. D flg. K fli. C meche mocha flg. M fli. A Level 2 n=6 Level 3

Using reporter genes to measure gene expression RNA polymerase Regulator Organization of operon on

Using reporter genes to measure gene expression RNA polymerase Regulator Organization of operon on chromosome. flh. DC promoter flh. C

Using reporter genes to measure gene expression RNA polymerase Regulator Organization of operon on

Using reporter genes to measure gene expression RNA polymerase Regulator Organization of operon on chromosome. flh. D flh. C flh. DC promoter Clone a copy of the promoter into a reporter plasmid. Reporter gene

Using reporter genes to measure gene expression RNA polymerase Regulator flh. D flh. C

Using reporter genes to measure gene expression RNA polymerase Regulator flh. D flh. C Both the flh. DC 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.

Gene Expression in Populations Gene Expression in Single Cells Multi-well plate reader Video microscopy

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

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

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

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

Fluorescence of flagella reporter strains as a function of time n ero Op s

Fluorescence of flagella reporter strains as a function of time n ero Op s s Cla Fluorescence relative to max 0. 6 0. 1 0. 01 0 Time [min] 600

The order of flagellar gene expression is the order of assembly Early Cluster 1

The order of flagellar gene expression is the order of assembly Early Cluster 1 Class 1 flh. DC Cluster 2 Late Class 2 fli. L Class 2 fli. E Class 2 fli. F Class 2 flg. A Class 2 flg. B Class 2 flh. B Class 2 fli. A Class 3 fli. D Class 3 flg. K Class 3 fli. C Class 3 meche Cluster 3 Class 3 mocha Class 3 flg. M Master regulator Activator of class 3

Simple Mechanism for Temporal Expression Within an Regulon [protein] Induction of positive regulator Time

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

Simple Mechanism for Temporal Expression Within an Regulon [protein]

Simple Mechanism for Temporal Expression Within an Regulon [protein]

Using Expression Data to Define and Describe Regulatory Networks With the flagella regulon, current

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

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 is. That 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

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? C A B M D E F N X Y W V U Z O

Engineered Gene Circuits: The Repressilator A 3 -element negative feedback transcriptional loop that should

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 m. RNA decay rates Michael Elowitz & Stanislas Leibler Nature, 2000

Engineered Gene Circuits: The Repressilator

Engineered Gene Circuits: The Repressilator

Engineered Gene Circuits: The Repressilator • Periodic synthesis of GFP (150 minutes, 3 x

Engineered Gene Circuits: The Repressilator • Periodic synthesis of GFP (150 minutes, 3 x cell cycle time) • The state of the network is transmitted to the siblings • Average decorrelation time = 95 +/- 10 minutes

Some lessons learned: § Even well studied systems still have elements of surprise! §

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.