Network Motifs and Modules
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.
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.
Network Motifs
Negative Autoregulation
Coherent
Feedforward
Positive Autoregulation
InCoherent
Feedforward
Double Positive Feedback
Double Negative Feedback
Delay or
ultrasensitivity unit
Network Motifs
Multi-Output FFL
Bi-Fan
Regulated Double
Negative Feedback
Dense
Overlapping
Regulons
Regulated Double
Positive Feedback
SIM – Single
Input Module
Network Motifs
Negative Autoregulation
1. Noise Suppression
2. Accelerated Response
3. High Fidelity Amplifier
4. Feedback Oscillation
Positive Autoregulation
1. Bistability
2. Memory Unit
Relaxation Oscillator
Network Motifs
Double Positive Feedback
Memory unit where both
units are either on or off
Double Negative Feedback
Memory unit: when one unit
is off the other unit is on
Network Motifs
Coherent Feedforward
1. Noise rejection
2. Pulse shifter
InCoherent Feedforward
1. Pulse generator
2. Concentration detector
3. Response time accelerator
Network Motifs
Regulated Double
Positive Feedback
Regulated Double
Negative Feedback
Z
Z
Memory unit that records
an event in Z
Memory unit that where nodes switch
in opposite directions due to an event in Z
Network Motifs
Multi-Output FFL
1. Pulse Train Generator
2. Temporal Sequencer – Last in last out,
ie the last gene activated is the last gene
deactivated.
SIM – Single Input Module
1. Master/Salve Regulator
2. Temporal Sequencer – Last in first out,
ie. The last gene activated is the first
gene deactivated
Feed-forward Networks
Copyright © 2013: Sauro
Feed-forward Networks
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.
Copyright © 2013: Sauro
Feed-forward Circuits
The sign of an
interaction
can be determined
either from basic
biochemistry studies
or by looking at
microarray
expression profiles.
Activate
Repress
Copyright (c) 2013
13
Feed-forward Circuits
Copyright (c) 2013
14
Feed-forward Circuits
C1
I1
Relative abundance of different FFL types in Yeast and E. coli. Data taken from
Mangan et al. 2003.
Copyright (c) 2013
15
Feed-forward Circuits
Dynamic Properties
Copyright (c) 2008
16
First Translate Non-stoichiometric
Network into a Stoichiometric Network
C1
Copyright (c) 2013
17
First Translate Non-stoichiometric
Network into a Stoichiometric Network
C1
?
Copyright (c) 2013
18
Feed-forward Circuits
Dynamic Properties
What does this actually mean?
AND GATE?
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
OR GATE?
Or something else?
Copyright (c) 2013
19
Feed-forward Circuits
Coherent Type I Genetic Network: AND Gate
C1
AND GATE
Copyright (c) 2013
20
Feed-forward Circuits
Coherent Type I Genetic Network
Noise Rejection
Circuit
No Delay
P1
Narrow Pulse
P1
Wide Pulse
P3
Time
P3
Delay
Time
NOTE THE DELAYS.
Copyright (c) 2013
21
Feed-forward Circuits
Coherent Type I Genetic Network
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 effect
h = 1;
p.P1
m1 =
p.P1
m2 =
p.P1
m3 =
= 0.3;
p.sim.eval (0, 10, 100, [<p.Time>, <p.P1>, <p.P3>]);
= 0.7; // Input stimulus
p.sim.eval (10, 10 + h, 100, [<p.Time>, <p.P1>, <p.P3>]);
= 0.3;
p.sim.eval (10 + h, 40, 100, [<p.Time>, <p.P1>, <p.P3>]);
m = augr (m1, m2);
m = augr (m, m3);
graph (m);
Copyright (c) 2013
22
Feed-forward Circuits
Coherent Type I Genetic Network
Question: What behavior would
you expect if the feed-forward
network is governed by an OR
gate?
OR GATE
Copyright (c) 2013
23
Feed-forward Circuits
Coherent Type I Genetic Network
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
OR GATE
Copyright (c) 2013
24
Feed-forward Circuits
Coherent Type I Genetic Network
Time
OR GATE
Copyright (c) 2013
25
Feed-forward Circuits
Coherent Type I Genetic Network
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 effect
h = 90;
p.P1
m1 =
p.P1
m2 =
p.P1
m3 =
= 0.3;
p.sim.eval (0, 50, 1000, [<p.Time>, <p.P1>, <p.P3>]);
= 0.8; // Input stimulus
p.sim.eval (50, 50 + h, 1000, [<p.Time>, <p.P1>, <p.P3>]);
= 0.3;
p.sim.eval (50 + h, 200, 1000, [<p.Time>, <p.P1>, <p.P3>]);
m = augr (m1, m2);
m = augr (m, m3);
graph (m);
Copyright (c) 2013
26
Feed-forward Circuits
Incoherent Type I Genetic Network
I1
Copyright (c) 2013
27
Incoherent Type I Genetic Network
Pulse Generator
P3
I
P3 comes down even though P1 is still high !
Copyright (c) 2013
28
Incoherent Type I Genetic Network
Pulse Generator
P1, P3
P3
P1
Pulses are not
symmetric because
the rise and fall
times are not the
same.
Time
P3 comes down even though P1 is still high !
Copyright (c) 2013
29
Incoherent Type I Genetic Network
Digital Pulse Generator
AND
Pulses are symmetric
because the rise and
fall times are the same.
Copyright (c) 2013
30
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
Copyright (c) 2013
31
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
p.P3
p.P1
p.G3
p.G1
I1
=
=
=
=
=
0;
0;
0.01;
0;
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
m1 =
p.P1
m2 =
= 0.0;
p.sim.eval (0, 10, 100, [<p.Time>, <p.P1>, <p.P3/1>]);
= 0.4; // Input stimulus
p.sim.eval (10, 50, 200, [<p.Time>, <p.P1>, <p.P3/1>]);
m = augr (m1, m2);
graph (m);
Copyright (c) 2013
32
Incoherent Type I genetic Network
Steady State Concentration Detector
I1
Circuit is off at low concentration, off at high concentrations
but comes on intermediate concentrations. Width of the peak
can be controlled by the cooperativity transcription binding.
Copyright (c) 2013
33
Incoherent Type I genetic Network
Concentration Detector
Take the pulse generator model and use this
code to control it:
I1
// Steady state response
n = 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);
Copyright (c) 2013
34
Incoherent Type I genetic Network
Response Accelerator
Making this stronger
makes the initial rise
go faster.
Then, bring the
overshoot down to
the desired steady
state with the
repression feedforward.
Copyright (c) 2013
An Introduction to Systems
Biology: Design Principles of
35
Biological Circuits.
Summary
C1
1. Persistence detector. Does not
respond to transient signals.
I1
1. Pulse generator
2. Concentration detector.
AND: Delay on start, no delay on
deactivate.
3. Response time accelerator.
2. Pulse stretcher and shifter.
OR: No delay on start, delay on
deactivate.
Copyright (c) 2013
36
Sequence Control – Temporal Programs
More Complex Arrangement
Parallel Concentration Detecting Feed-Forward Networks –
Generating Pulse Trains
The kinetics can be arranged so that
each successive feed-forward loop
peaks at a later time.
……
P3 rises first, followed by P5.
This allows pulse trains to be
generated.
Copyright (c) 2013
37
Input
Nested FFLs
Output 1
Output 2
Output 3
Copyright (c) 2013
38
Input
Nested FFLs - Counters
Output 1
Friedland, A. E. et al. Synthetic gene networks
that count. Science 324, 1199–1202 (2009).
Output 2
Output 3
Copyright (c) 2013
39
Nested FFLs - Counters
Lte0-1: Constitutive promoter
T7 RNAP: T7 RNA Polymerase
P_T7: T7 RNAP Promoter
GFP: Green fluorescent protein
Friedland, A. E. et al. Synthetic gene networks
that count. Science 324, 1199–1202 (2009).
P_BAD: Arabinose Operator
taRNA/cr - Riboregulator
Copyright (c) 2013
40
GFP: Green Fluorescent Protein
A 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
41
Input
Nested FFLs - Counters
Output 1
Output 2
Output 3
Copyright (c) 2013
42
Riboregulators
Nature Biotechnology 22, 841 - 847 (2004)
Published online: 20 June 2004; | doi:10.1038/nbt986 Engineered
riboregulators enable post-transcriptional control of gene expression
Farren J Isaacs, Daniel J Dwyer, Chunming Ding, Dmitri D Pervouchine,
Charles R Cantor & James J Collins
Copyright (c) 2013
43
Using RNA to Control
Modular:
crRNA can be
inserted upstream
of any gene
 Can change
levels of cisrepression and
trans-activation
with different
promoters (tried
with PLAC also)
driving expression
of taRNA and
crRNA transcripts
Unfolds hairpin to expose RBS
Copyright (c) 2013
(non-coding RNA [ncRNA])
44
Riboregulators
Copyright (c) 2013
45
Other Motifs
1. Single-input Module (SIM)
2. Auto-regulation
Copyright (c) 2013
46
Sequence Control – Temporal Programs
Single-input Module (SIM)
E1
E2
E3
Input: X
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
Copyright (c) 2013
Biology: Design Principles of
47
Biological Circuits.
Temporal Order Control of
Bacterial Flagellar Assembly
Driven by a proton gradient.
Runs at approximately
6,000 to 17,000 rpm. With the
filament attaching rotation is
slower at 200 to 1000 rpm
Can rotate in both directions.
Approximately 50 genes
involved in assembly of the
motor and control circuits.
http://www.youtube.com/watch?v=0N09BIEzDlI
Copyright (c) 2013
48
Temporal Order Control of Flagellar
Assembly
An Introduction to Systems
Biology: Design Principles of
Biological Circuits.
Copyright (c) 2013
49
Temporal Order Control of Flagellar
Assembly
Copyright (c) 2013
50
Temporal Order Control of Metabolic
Pathways - Arginine
Copyright (c) 2013
51
Temporal Order Control of Metabolic
Pathways Arginine
Early
Late
Red means more expression
of that particular gene.
Copyright (c) 2013
52
Temporal Order Control of Metabolic
Pathways Methionine
Copyright (c) 2013
53
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.
Copyright (c) 2013
54
Auto Regulation
Copyright © 2013: Sauro
Auto-regulation – Negative Feedback
Copyright (c) 2013
56
Auto-regulation – Positive Feedback
Copyright (c) 2013
57
Negative Feedback - Homeostasis
V1
V1, V2
P
Negative Feedback - Homeostasis
V1
V2
V1, V2
Steady State!
P
Negative Feedback - Homeostasis
V2
V1, V2
V2
V1
P
P is very sensitive to changes in V2 (k2)
Negative Feedback - Homeostasis
V2
V1
V1, V2
V2
P
P is less sensitive to changes in V2 (k2)
Negative Feedback - Homeostasis
V2 = 0.3
V1
V2 = 0.2
V1, V2
V2 = 0.1
S1
P is much less sensitive to changes in V2 (k2)
Auto-regulation – Negative
Feedback Response Accelerator
Strong Feedback
+ strong input
promoter
P
Weak Feedback
Input, I
Copyright (c) 2013
63
Amplifiers
Output, P
Input, I
Amplifiers
Amplifiers
The Effect of Negative Feedback
No Feedback
Output, P
Input, I
Amplifiers
The Effect of Negative Feedback
Negative Feedback
stretches the response
and reduces the gain,
but what else?
No Feedback
Output, P
Input, I
With Feedback
Output, P
Input, I
Simple Analysis of Feedback
yi
A
k
yo
Simple Analysis of Feedback
yi
A
k
Solve for yo:
yo
Simple Analysis of Feedback
yi
A
k
Solve for yo:
yo
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.
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.
Summary of Negative Feedback
1. Noise Suppression
2. Accelerated Response
3. High Fidelity Amplifier
4. Feedback Oscillation