Information Processing in Genetic Regulatory Networks

Ofer Biham

Mor NitzanYishai ShimoniBaruch BarzelAdiel LoingerAzi LipshtatOded RosolioAssaf Pe’erYael Altuvia

Hanah MargalitPascale RombyPierre Fechter

Network and motifs

Transcriptional network of E. coli

Other modules

A

B

A

AB

A

b c d e

A

Motifs

b

Regulation Mechanisms

Different levels of regulation Transcriptional regulation Post-transcriptional regulation (by sRNA-mRNA int.) Post-translational regulation (by protein-protein int.)

gene a gene b

m B

m A

BA

gene a gene b

B

m B

S A

Transcriptional regulation Post-transcriptional regulation

Information processing

Transcriptional regulation

c

m C

C

A

B

Input Functions

Diverse two-dimensional input functions control bacterial sugar Genes, Kaplan, Bren, Zaslaver, Dekel and Alon, Molecular Cell 29, 783 (2008).

Transcription factor

Transcription regulation

Post-transcriptional regulation

ncRNA

Post-transcriptional regulation by ncRNA

Transcription regulation Integrated networkMulti-layer feed-forward loop

Multi-layer regulatory circuits

Asaf Peer, Mor Nitzan, Zohar Itzhaki, Hanah Margalit

Combination of regulations at different levels

gene b

SB

gene agene c

mCm A

CA

Staphylococcus aureus

Pathogenic bacteriaCause a wide range of human diseasesDisease manifestations depend on the

expression of numerous virulence factorsWithin S. aureus virulence

pathways lies a regulator switch that is

induced by a quorum

sensing signal

Quorum sensing for a growing population

At low numbers, violent bacteria will be quickly targeted for degradation

Only at higher numbers, the bacteria become virulent.

Quorum sensing for a dense population

Outer bacteria act as a shieldInner, protected bacteria excrete violent

proteins

S. aureus virulence path

hla

Quorum SensingQuorum Sensing

spa

RNAIII

Rot

Adhesins, camouflage proteins

(defensive state)

Adhesins, camouflage proteins

(defensive state)

Exotoxins, ­hemolysinα

(offensive state)

Exotoxins, ­hemolysinα

(offensive state)

The Switch

Target 2Target 1

Regulator

A Simpler Switch

Selector Switch

Target 2Target 1

activator

Selector Switch without activator/repressor

repressor

Target 2Target 1

Top Regulator

Bottom Regulator

Double Selector Switch

The model- rate equations

.

(sRNA regulator)

(mRNA transcripts of TF )

(TF protein)

(TF - promoter complexes)

(mRNA transcripts of target 1)(mRNA transcripts of target 2)(Target 1 proteins)

(Target 2 proteins )

(sRNA - target mRNA complexes)

Switching on and off

Target 2Target 1

sRNA

TF

Time (min)

Response to a spike

Leakage of Target 1

1

(1 ) (1 )

T

T

T s

T m

bTPu

b bTP su d

NLeakage

N N

Target 2Target 1

sRNA

TF

Mixed Feedback Loop

Bifurcation Diagrams

Stochastic Trajectories

Life-times of bistable states

Deterministic vs. Stochastic Models

SS

S A

P

Probability Distribution

sRNA-target interaction

E. Levine, Z. Zhang, T. Kuhlman and T. Hwa, Plos. Biol. (2007)

Fine-tuning of target expression

E. Levine, Z. Zhang, T. Kuhlman and T. Hwa, Plos. Biol. (2007)

miRstargets

Post transcriptional network in HEK293 Cells

Crosstalk between Competing endogeneous RNAs (ceRNAs)

miR-Y

mRNA target 1mRNA target 2

Salmena et al., Cell 146, 353 (2011); Tay et al., Cell 147, 344 (2011);Bosia et al., Plos One 8, e66609 (2013); Figliuzzi et al., Biophys J. 104, 1203 (2013)

Crosstalk between ncRNAs

Crosstalk between mRNAs through their common regulators

Fast Transmission of Signals

(a) Wild-type (c)

(a) Wild-type (b)

T0T0 T1

T1 T2T2 T0

T0 T1T1 T2

T2 T0T0 T1

T1 T2T2

T0T0 T1

T1 T0T0 T1

T1

R0R0 R0

R0

T0T0 T1

T1 T2T2

R0R0 R1

R1 R0R0 R1

R1 R0R0 R1

R1 R0R0 R1

R1

(c) (d)

(b)

T0T0 T1

T1

R0R0

Knock-down of T0

Knock-down of T0

Knock-down of T10

Knock-down of T10

A

C

(a) Wild-type (c)(b)

B

Over-expression

of R0

Over-expression

of R0

T1T1

R0R0 R1

R1

T1T1

R0R0 R1

R1

T1T1

R0R0 R1

R1

Signal Propagation – Experimental Data

T0T0 T1

T1 T2T2

R0R0 R1

R1 R4R4

T3T3 T4

T4

R2R2 R3

R3

T5T5

R5R5

Subnetwork of sRNA Regulators and theirTargets

T0T0 T1

T1 T2T2

R0R0 R1

R1 R4R4

T3T3 T4

T4

R2R2 R3

R3

T5T5

R5R5

Decay Rate of the Signal

Correlations in the Network

Summary

We have studied information processing in genetic regulatory networks that involve different levels of regulation

These networks combine sharp on/off type regulation with fine tuning processes, fast and slow processes, synchronization and subtle coordination

Further progress will require experiments both at the single cell level and at the cell population level

Transcriptional vs. Post-transcriptional regulation

Transcriptional Post-transcriptional

Response time Slow Fast

Regulation type Sharp On/Off Enables fine-tuning

Regulator-target interaction

Non-stoichiometric Stoichiometric

Regulation strength determined by

TF copy number and affinity to promoter

Relative copy numbers of sRNAs and mRNAs and their affinity

Directionality Directional – from regulator to target

Bi-directional

Energetic cost Protein synthesis RNA synthesis

Combination of regulations at different levels

gene b

SB

gene a gene c

mCm A

CA

Target 2Target 1

TF

sRNA

Target 2Target 1

sRNA

TF

Target 2Target 1

TF

TF

Three variants of the DSS

Dynamics of DSS variants

Leakage in target genes