network motifs and modules. what is a motif? a motif is a statistically over-represented subgraph in...
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Network Motifs and Modules
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Network Motifs and Modules
What is a motif?
A motif is a statistically over-represented subgraph in a network.
A pattern of connections that generates a characteristic dynamical response. A motif is a connection pattern template which could in principle be implemented.
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Network Motifs and Modules
What is a module?
A module is an exchangeable functional unit. Its chief characteristic is that when placed in a different context, its intrinsic functional properties do not change.
All modules are motifs but not all motifs are modules.
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Network Motifs
Negative Autoregulation
Positive Autoregulation
Double Positive Feedback
Double Negative Feedback
CoherentFeedforward
InCoherentFeedforward
Delay orultrasensitivity unit
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Network Motifs
Multi-Output FFL
Regulated Double Negative Feedback
Regulated Double Positive Feedback
Bi-Fan
Dense Overlapping
Regulons
SIM – Single Input Module
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Network MotifsNegative Autoregulation Positive Autoregulation
1. Noise Suppression2. Accelerated Response3. High Fidelity Amplifier4. Feedback Oscillation
1. Bistability2. Memory Unit
Relaxation Oscillator
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Network Motifs
Memory unit where bothunits are either on or off
Memory unit: when one unitis off the other unit is on
Double Positive Feedback Double Negative Feedback
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Network Motifs
1. Noise rejection2. Pulse shifter
1. Pulse generator2. Concentration detector3. Response time accelerator
Coherent Feedforward InCoherent Feedforward
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Network Motifs
Memory unit that recordsan event in Z
Memory unit that where nodes switch in opposite directions due to an event in Z
Regulated Double Negative Feedback
Regulated Double Positive Feedback
Z Z
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Network Motifs
1. Pulse Train Generator2. Temporal Sequencer – Last in last out,ie the last gene activated is the last genedeactivated.
Multi-Output FFL SIM – Single Input Module
1. Master/Salve Regulator2. Temporal Sequencer – Last in first out, ie. The last gene activated is the first gene deactivated
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Feed-forward Networks
Copyright © 2013: Sauro
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Feed-forward Networks
Copyright © 2013: Sauro
1. Estimating the frequency of each isomorphic subgraph in the target network.
2. Generating a suitable random graph to test the significance of the frequency data.
3. Compare the target network with the random graph.
Occurrences of the feed-forward loop motifs as generated by the software MAVisto [1]. The displayed network is part of yeast data supplied with the MAVisto software. The software is very straight forward to use and will identify a wide variety of motifs. Other similar tools include FANMOD and the original tool mFinder.
F. Schreiber and H. Schwobbermeyer. MAVisto: a tool for the exploration of network motifs. Bioinformatics, 21(17):3572–3574, 2005.
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Feed-forward Circuits
Activate
Repress
The sign of an interactioncan be determined either from basicbiochemistry studies or by looking at microarray expression profiles.
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Feed-forward Circuits
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Feed-forward Circuits
Relative abundance of different FFL types in Yeast and E. coli. Data taken fromMangan et al. 2003.
I1
C1
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Copyright (c) 2008 16
Feed-forward CircuitsDynamic Properties
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First Translate Non-stoichiometric Network into a Stoichiometric Network
C1
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First Translate Non-stoichiometric Network into a Stoichiometric Network
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?
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Feed-forward CircuitsDynamic Properties
What does this actually mean?
AND GATE? OR GATE?Or something else?
Input A Input B AND OR XOR
1 1 1 1 0
1 0 0 1 1
0 1 0 1 1
0 0 0 0 0
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Feed-forward CircuitsCoherent Type I Genetic Network: AND Gate
C1
AND GATE
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Feed-forward CircuitsCoherent Type I Genetic Network
Copyright (c) 2013
NOTE THE DELAYS.
Delay
No Delay
TimeTime
P1 P1
P3 P3
Noise Rejection Circuit
Narrow Pulse Wide Pulse
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Feed-forward CircuitsCoherent Type I Genetic Network
Copyright (c) 2013
p = defn cell $G2 -> P2; Vmax2*P1^4/(Km1 + P1^4); P2 -> $w; k1*P2; $G3 -> P3; Vmax3*P1^4*P2^4/(Km1 + P1^4*P2^4); P3 -> $w; k1*P3; end;
p.Vmax2 = 1;p.Vmax3 = 1;
p.Km1 = 0.5;p.k1 = 0.1;
p.P1 = 0;p.P2 = 0;p.P3 = 0;
p.ss.eval;println p.sv;
// Pulse width// Set to 1 for no effect// Set to 4 for full effecth = 1;
p.P1 = 0.3;m1 = p.sim.eval (0, 10, 100, [<p.Time>, <p.P1>, <p.P3>]);p.P1 = 0.7; // Input stimulusm2 = p.sim.eval (10, 10 + h, 100, [<p.Time>, <p.P1>, <p.P3>]);p.P1 = 0.3;m3 = p.sim.eval (10 + h, 40, 100, [<p.Time>, <p.P1>, <p.P3>]);
m = augr (m1, m2);m = augr (m, m3);graph (m);
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Feed-forward CircuitsCoherent Type I Genetic Network
Copyright (c) 2013
OR GATE
Question: What behavior would you expect if the feed-forward network is governed by an OR gate?
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Feed-forward CircuitsCoherent Type I Genetic Network
Copyright (c) 2013
OR GATE
Question: What behavior would you expect if the feed-forward network is governed by an OR gate?
1. No delay on activation.2. Delay on deactivation.
3. Pulse Stretcher and Shifter
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Feed-forward CircuitsCoherent Type I Genetic Network
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OR GATETime
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Feed-forward CircuitsCoherent Type I Genetic Network
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p = defn cell $G2 -> P2; Vmax2*P1^4/(Km1 + P1^4); P2 -> $w; k1*P2; $G3 -> P3; Vmax3*(P1^4 + P2^4)/(Km1 + P1^4 + P2^4); P3 -> $w; k1*P3; end;
p.Vmax2 = 1;p.Vmax3 = 0.1;
p.Km1 = 0.5;p.k1 = 0.1;
p.P1 = 0;p.P2 = 0;p.P3 = 0;
p.ss.eval;println p.sv;
// Pulse width// Set to 1 for no effect// Set to 4 for full effecth = 90;
p.P1 = 0.3;m1 = p.sim.eval (0, 50, 1000, [<p.Time>, <p.P1>, <p.P3>]);p.P1 = 0.8; // Input stimulusm2 = p.sim.eval (50, 50 + h, 1000, [<p.Time>, <p.P1>, <p.P3>]);p.P1 = 0.3;m3 = p.sim.eval (50 + h, 200, 1000, [<p.Time>, <p.P1>, <p.P3>]);
m = augr (m1, m2);m = augr (m, m3);graph (m);
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Feed-forward CircuitsIncoherent Type I Genetic Network
I1
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Incoherent Type I Genetic NetworkPulse Generator
P3 comes down even though P1 is still high !
I
P3
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Incoherent Type I Genetic NetworkPulse Generator
P3 comes down even though P1 is still high !
Time
P1, P3
P1
P3
Pulses are not symmetric because the rise and fall times are not the same.
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Incoherent Type I Genetic Network Digital Pulse Generator
Pulses are symmetric because the rise and fall times are the same.
AND
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Incoherent Type I Genetic Network Pulse Generator
One potential problem, if the base line for P3 is not at zero, the off transition will result in an inverted pulse. Avoid this by arranging the base line of P3 to be at zero.
TIME
Inverted Pulse
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Incoherent Type I Genetic Network Pulse Generator
p = defn cell $G1 -> P2; t1*a1*P1/(1 + A1*P1); P2 -> $w; gamma_1*P2; $G3 -> P3; t2*b1*P1/(1 + b1*P1 + b2*P2 + b3*P1*P2^8); P3 -> $w; gamma_2*P3; end;
p.P2 = 0;p.P3 = 0;p.P1 = 0.01;p.G3 = 0;p.G1 = 0;
p.t1 = 5;p.a1 = 0.1;p.t2 = 1;p.b1 = 1;p.b2 = 0.1;p.b3 = 10;p.gamma_1 = 0.1;p.gamma_2 = 0.1;
// Time course response for a step pulse
p.P1 = 0.0;m1 = p.sim.eval (0, 10, 100, [<p.Time>, <p.P1>, <p.P3/1>]);p.P1 = 0.4; // Input stimulusm2 = p.sim.eval (10, 50, 200, [<p.Time>, <p.P1>, <p.P3/1>]);
m = augr (m1, m2);graph (m);
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Incoherent Type I genetic NetworkSteady State Concentration Detector
I1
Circuit is off at low concentration, off at high concentrationsbut comes on intermediate concentrations. Width of the peakcan be controlled by the cooperativity transcription binding.
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Incoherent Type I genetic NetworkConcentration Detector
Take the pulse generator model and use this code to control it:
// Steady state responsen = 200;m = matrix (n, 2);for i = 1 to n do begin m[i,1] = p.P1; m[i,2] = p.P3; p.ss.eval; p.P1 = p.P1 + 0.005; end;graph (m);
I1
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Incoherent Type I genetic NetworkResponse Accelerator
Making this strongermakes the initial risego faster.
Then, bring the overshoot down to the desired steady state with the repression feed-forward.
An Introduction to Systems Biology: Design Principles of Biological Circuits.
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Summary
1. Persistence detector. Does not respond to transient signals.
AND: Delay on start, no delay on deactivate.
2. Pulse stretcher and shifter.
OR: No delay on start, delay on deactivate.
1. Pulse generator
2. Concentration detector.
3. Response time accelerator.
C1 I1
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Sequence Control – Temporal ProgramsMore Complex Arrangement
Parallel Concentration Detecting Feed-Forward Networks –Generating Pulse Trains
The kinetics can be arranged so thateach successive feed-forward loop peaks at a later time.
P3 rises first, followed by P5.
This allows pulse trains to be generated.
……
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Nested FFLs
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Input
Output 1
Output 2
Output 3
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Nested FFLs - Counters
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Input
Output 1
Output 2
Output 3
Friedland, A. E. et al. Synthetic gene networks that count. Science 324, 1199–1202 (2009).
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Nested FFLs - Counters
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Lte0-1: Constitutive promoterT7 RNAP: T7 RNA PolymeraseP_T7: T7 RNAP PromoterGFP: Green fluorescent protein
P_BAD: Arabinose Operator
taRNA/cr - Riboregulator
Friedland, A. E. et al. Synthetic gene networks that count. Science 324, 1199–1202 (2009).
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GFP: Green Fluorescent ProteinA protein of 238 amino acids that exhibits bright green light (at about 509nm) when exposed to light in the blue range (395 nm and 475 nm). Comes from the Jellyfish Aequorea victoria. Many derivatives now available, eg Azurite (blue),Venus (yellow), ECFP (cyan), RFP (red).
Advantages:1. Small, expressed in most if not all organisms.2. Is self-contained, doesn’t require other molecules to work
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Nested FFLs - Counters
Copyright (c) 2013
Input
Output 1
Output 2
Output 3
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Riboregulators
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Nature Biotechnology 22, 841 - 847 (2004) Published online: 20 June 2004; | doi:10.1038/nbt986 Engineered riboregulators enable post-transcriptional control of gene expressionFarren J Isaacs, Daniel J Dwyer, Chunming Ding, Dmitri D Pervouchine, Charles R Cantor & James J Collins
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Using RNA to Control
Copyright (c) 2013
Modular:crRNA can be inserted upstream of any gene Can change levels of cis-repression and trans-activation with different promoters (tried with PLAC also) driving expression of taRNA and crRNA transcripts
Unfolds hairpin to expose RBS
(non-coding RNA [ncRNA])
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Riboregulators
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Other Motifs
1. Single-input Module (SIM)
2. Auto-regulation
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Sequence Control – Temporal Programs
Single-input Module (SIM)
The simplest approach is to have different thresholds can be achieved by assigning a different K and Vmax to each expression rate law, easily generated through evolutionary selection. An Introduction to Systems
Biology: Design Principles of Biological Circuits.
Input: X
E1
E2E3
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Temporal Order Control of Bacterial Flagellar Assembly
Driven by a proton gradient.Runs at approximately6,000 to 17,000 rpm. With the filament attaching rotation is slower at 200 to 1000 rpm
Can rotate in both directions.
Approximately 50 genesinvolved in assembly of themotor and control circuits.
http://www.youtube.com/watch?v=0N09BIEzDlI
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Temporal Order Control of Flagellar Assembly An Introduction to Systems
Biology: Design Principles of Biological Circuits.
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Temporal Order Control of Flagellar Assembly
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Temporal Order Control of Metabolic Pathways - Arginine
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Temporal Order Control of Metabolic Pathways Arginine
Early Late
Red means more expression of that particular gene.
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Temporal Order Control of Metabolic Pathways Methionine
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Temporal Order Control of Metabolic Pathways Methionine
Increasing a pathway’s capacity by sequential ordering of expression is probably only employed when the pathway is empty.
For pathways already in operation, eg pathways like glycolysis, increasing the capacity is achieved by simultaneous increases. This is done to avoid wild swings in existing metabolite pools.
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Auto Regulation
Copyright © 2013: Sauro
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Auto-regulation – Negative Feedback
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Auto-regulation – Positive Feedback
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Negative Feedback - Homeostasis
V1, V2
V1
P
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Negative Feedback - Homeostasis
V1, V2
V1
P
V2
Steady State!
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Negative Feedback - Homeostasis
V1, V2
V1
P
V2
V2
P is very sensitive to changes in V2 (k2)
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Negative Feedback - Homeostasis
V1, V2
V1
P
V2
V2
P is less sensitive to changes in V2 (k2)
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Negative Feedback - Homeostasis
V1, V2
V1
V2 = 0.3
V2 = 0.2
V2 = 0.1
S1
P is much less sensitive to changes in V2 (k2)
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Auto-regulation – Negative Feedback Response Accelerator
Weak Feedback
Strong Feedback+ strong inputpromoter
Input, I
P
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Amplifiers
Input, I
Output, P
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Amplifiers
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Amplifiers
No Feedback
The Effect of Negative Feedback
Input, I
Output, P
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Amplifiers
No Feedback
The Effect of Negative Feedback
With Feedback
Input, I
Input, I
Output, P
Output, P
Negative Feedbackstretches the responseand reduces the gain, but what else?
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Simple Analysis of Feedback
A
k
yoyi
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Simple Analysis of Feedback
Solve for yo:
A
k
yoyi
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Simple Analysis of Feedback
Solve for yo:
A
k
yoyi
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Simple Analysis of Feedback
At high amplifier gain (A k > 1):
In other words, the output is completely independent of the amplifier and is linearly dependent on the feedback.
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Simple Analysis of Feedback
Basic properties of a feedback amplifier:
1. Robust to variation in amplifier characteristics.
2. Linearization of the amplifier response.
3. Reduced gain
The addition of negative feedback to a gene circuit will reduce the level of noise (intrinsic noise) that originates from the gene circuit itself.
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Summary of Negative Feedback
1. Noise Suppression2. Accelerated Response3. High Fidelity Amplifier4. Feedback Oscillation