Seminar in Bioinformatics, Winter 2011
Network Motifs
AN INTRODUCTION TO SYSTEMS BIOLOGY
URI ALON
CHAPTERS 5-6
BY
ELIAD EINI
&
YASMIN ADMON
Table of Content
Chapter
5
A very short remainder for the previous chapter
The Single-Input Module (SIM) network motif
Temporal networks
Topological generalization of network motifs
Signal integration and combinatorial control:
Bi-fans and Dense Overlapping Regulons (DORs)
Network motifs and global structure of sensory transcription networks
Table of Content
Chapter
6
Network motifs in developmental
Transcription Networks
Network motifs in Signal
Transduction Networks
Network motifs in Neuronal
Networks
Composite Network Motifs
Chapter 5
Temporal Programs and
the Global Structure of
Transcription Networks
A short remainder
We have seen that transcription networks contain
recurring network motifs that can perform specific
dynamical functions.
We examined two of this motifs in details: autoregulation and feed-forward loop (FFL).
What’s next?
In this chapter we will complete our survey of motifs in
sensory transcriptional networks.
We will see that sensory transcription networks are
largely made of just four families of networks: autoregulation and FFL (we have already studied), Single
Input Module (SIM) and Dense Overlapping Regulons
(DORs).
We have seen network motifs
before, is there something special
you are going to show us?
The Single-Input Module
Network Motif (SIM)
 In the SIM network motif, a master transcription
factor X controls a group of target genes, Z , Z ,..., Z , like
we can see in the picture.
1
2
n
 Each of the target genes has only one input.
 No other transcription factors regulates any of the
genes.
 The regulation signs (activation/repression) are the same of all genes in the SIM.
 The master transcription factor X is usually autoregulatory.
Seems great, but how do you know that SIM is
a motif?
As we saw in the lecture of chapters 3-4, in order to
recognize a pattern as a motif, we should compare it to
a random network. A random network (ER) have a
degree sequence (distribution of edges per node) that
is Poisson, so there are exponentially few nodes that
have many more edges than the mean connectivity
𝜆. Thus ER networks have very few large SIMs.
So what is the function of SIMs? What can it do?
 The most important task of SIM is to
control a group of genes according to
the signal sensed by the master
regulator.
 The genes in a SIM always have a
common biological function:
For example, SIMs often regulates genes that participate in specific
metabolic pathways as shown in this figure.
 Other SIMs control group of genes that respond to a specific stress
(DNA damage, heat shock, etc.) These genes produce proteins that
repair the different forms of damage caused by the stress.
 SIMs can control group of genes that together make up a protein
machine (such as ribosome).
SIM and Temporal Programs
An example for a Temporal Program
Few words about evolution
 There are many examples of SIMs that regulate the
same gene systems in different organisms.
 The master regulator in the SIM is often different in
each organism, despite the fact that the target genes
are highly homologous.
What does it mean?
What happened in the evolution point of
view?
It means that rather than duplication of ancestral SIM to
create the modern SIM, since this mechanism is useful, it was
kept during generations and preserved against mutations.
Topological generalization of network motifs
It is very difficult to recognize motifs on large graphs:
Simple topological generalization of FFL
An example of FIFO order in multi-output FFL
How does it works?
FIFO’s tresholds
Signal integration and combinatorial control:
Bi-fans and Dense Overlapping Regulons (DORs)
Do you remember the large number of 4-nodes
possible sub-graphs?
Only 2 of them were real motifs:
Dense Overlapping Regulons (DORs)
Network motifs and global structure of sensory
transcription networks
After we learnt about motifs,
we can locate the motifs on
E-coli’s network and draw it in
a much simple way
Chapter 6
Network motifs in developmental
Transcription Networks
Network motifs in Signal Transduction
Networks
Network motifs in Neuronal Networks
Composite Network Motifs
Network motifs in developmental Transcription Networks
 Governs the fates of cells, as an egg
Developmental
Transcription
Networks
develops into a multi-cellular
organism.
 In all Multi-cellular organisms and
in many microorganisms, cells
undergo differentiation process –
they can change into other cell types.
 Developmental transcription
networks control these
differentiation processes.
Network motifs in developmental Transcription Networks
 the Timescale on which the networks
What is the
difference
between
Sensory and
Developmental
Transcription
Networks?
need to operate.


Sensory transcription networks need to make rapid
decisions that are shorter then a cell generation time.
In Contrast, Transcription Networks works on a slow
timescale of one or more cell generations.
 The reversibility of the networks’
actions.


Sensory transcription networks need to make reversible
decisions.
Developmental transcription networks often need to make
irreversible decisions.
We will see that these differences lead to new
network motifs, that appear in Developmental
transcription networks, but not in Sensory
transcription networks.
Network motifs in developmental Transcription Networks
Long
transcription
cascades and
developmental
timing
• Reminder: The response time of each stage in
cascades is governed by the degradation/dilution
rate of the protein at that stage: T  log(2)
1
2

• For stable proteins, this response time is on the
order of cell generation time.
• Developmental networks work on this timescale,
because cell fates are assigned with each cell
division.
Interlocked Feed-Forward Loops
 In developmental networks, FFLs often form parts of
larger and more complex circuits.
 Can we still understand the dynamics of such large
circuits based on the behavior if the individual FFL?
 Example - the well mapped B. subtilis Sporulation
network
B. Subtilis sporulation process
 Bacillus subtilis – single celled bacterium. When starved,
it stops dividing and turns into a durable spore.
 The sporulation process involves hundred of genes that
are turned ON and OFF in a series of temporal waves.
 The network that regulates sporulation is made of several
transcription factors arranged in a linked coherent and
incoherent type-1 FFLs.
Interlocked Feed-Forward Loops In B. Subtilis
Sporulation Network
To initiate the sporulation process,
a starvation signal Sx activates X1
Incoherent
Type-1 FFL
Coherent
Type-1 FFL
Interlocked Feed-Forward Loops In B. Subtilis
Sporulation Network
Incoherent
Type-1 FFL
Coherent
Type-1 FFL
Interlocked Feed-Forward Loops In B. Subtilis
Sporulation Network
Incoherent
Type-1 FFL
Coherent
Type-1 FFL
Interlocked Feed-Forward Loops In B. Subtilis
Sporulation Network
Incoherent
Type-1 FFL
Coherent
Type-1 FFL
Interlocked Feed-Forward Loops In B. Subtilis
Sporulation Network - Summary
 The combination of FFLs in the
sporulation process network
results in a tree wave temporal
pattern.
 This design can generate finer
temporal programs within each
groups of genes.
 The dynamics of multi-output
FFLs can be understood by based
on the dynamics of each of the
constituent 3 node FFL.
Chapter 6
Network motifs in developmental
Transcription Networks
Network motifs in Signal
Transduction Networks
Network motifs in Neuronal
Networks
Composite Network Motifs
Network motifs In Signal Transduction Networks
 Sense and process information from the
Signal
Transduction
Networks
environment, and accordingly regulate the
activity of transcription factors or other
effector proteins.
 Elicit rapid responses.
 Composed of interactions between signaling
proteins, which are represented as nodes in
the network, whereas the edges signify
directed interaction.
 The structure of signaling networks is a
subject of current research, and yet fully
understood. We will focus on one distinct
motif that is found in signaling networks, and
not in transcription networks.
Network motifs In Signal Transduction Networks
Signaling
networks
show two
strong
4-node motifs
Bi-fan
Diamond
Network motifs In Signal Transduction Networks
Toy Model for
protein kinase
preceptrons
Network motifs In Signal Transduction Networks
 Protein kinase cascades are usually made of
Multi-layer
perceptrons
In protein
kinase
cascades
layers, usually three.
 This forms multi-layer perceptrons that can
integrate input from several receptors