Transcript Presentation

Network Motifs
Today’s plan
• Understand what are “network motifs”
and what they are used for
• See some examples of motifs and their
functionality
• Discuss a study that showed how a
miRNA also can be integrated into motifs
Introduction - Biological Networks
• Complex biological systems may be represented and analyzed
as computable networks
• The networks include nodes and edges (the connections
between the nodes)
Introduction - Biological Networks
• For example: food web, neuron synaptic connections,
transcription regulation network
Simple Building Blocks of Complex Networks –
Milo, Shen-Orr, Itzkovitz, Kashtan, Chklovskii, Alon
Science, 2002
Why define simple building blocks?
Network Motifs
• Network motifs can be thought of as recurring circuits of
interactions from which the networks are built
• Those circuits occurring in complex networks at numbers
that are statistically significantly higher than those in
randomized networks
Algorithm for detecting network motifs
Connected patterns identified as
Network Motifs
Connected patterns
identified as Network
Motifs
Network Motifs:
theory and experimental approaches
Alon, Nature Review Genetics, 2007
Negative Autoregulation - NAR
• Speeds up the response time* of gene circuits
• Uses a strong promoter to obtain a rapid initial rise
• Can reduce cell- cell variability
*Response time – the
time it takes to reach
halfway between the
initial and final levels
Positive Autoregulation - PAR
• Slows the response time
• Reaches its steady state in an S-shaped curve
• Tends to increase cell- cell variability
Positive Autoregulation - PAR
• Weak PAR makes the cell cell distribution of protein
concentration to be broader than in case of simply regulated
gene
• Strong PAR can lead to bimodal distributions – the concentration
of the protein is low in some cells, and high in others
Different configurations of
Feedforward loops – FFL
Coherent feedforward loop – type I
(AND gate)
• Sign sensitive delay element – shows a delay when the
transcription turns ON (addition of Sx), and doesn’t
when the transcription turns OFF (removal of Sx)
Coherent feedforward loop in E.coli
Incoherent Feedforward loop- type I
(AND gate)
• Can generate a pulse of Z expression in response to a step
stimulus of Sx
• Similar to the NAR (negative autoregulation) system,
shows faster response time for the concentration of Z
PAR
NAR
Single Input Modules - SIM
• Regulator X regulates a group of target genes – usually
with shared function
• Allow coordinated expression of the group
Single Input Modules - SIM
Convergent evolution of
Network Motifs
• Two genes that have similar functions stem from a
common ancestor gene – called gene homology
• This is reflected in a significant degree of sequence
similarity between the genes
Organism a
Organism b
Network Motifs in developmental
transcription networks
Bacillus subtilis spore
Transcription regulation network
in E.coli
MicroRNA-Mediated Feedback and Feedforward
Loops Are Recurrent Network Motifs in Mammals
Tsang, Zhu, Van Oudenaarden
Cell, 2007
TF
TF
Transcriptional regulation
Transcriptional regulation
miRNA
Post-transcriptional regulation
miRNA
Target Gene
Missing data
Missing data
Target mRNA
miRNA
Weak
repression
Gene
But..
If you combine both transcription regulation (TF)
and post-transcriptional regulation (miRNA), you
get multiple levels regulation with greater strength
miRNA
TF
Gene
Two classes of miRNA- containing circuits
Type I circuits
The transcription rate of the miRNA (m)
and the target gene (T) are positively
correlated
Type II circuits
The transcription rate of the miRNA (m)
and the target gene (T) are negatively
correlated
miRNA seed
Step 1: Creation of 2 lists for each miRNA
1. Ranked list of genes based on the
extent of their expression correlation
miRNA
GeneA
GeneB
GeneC
.
.
.
.
GeneZ
Genes with the most
expression correlation
to the miRNA
Genes with the lowest
correlation to the miRNA
2. List of target genes based on the
TargetScanS algorithm
miRNA
GeneA
GeneB
GeneC
.
.
.
.
GeneZ
Predicted genes had a high
correlation / anti correlation
• 75% of the miRNAs checked, have a
significantly higher number of predicted
targets (p < 0.05) in the top or bottom ten
percentile of ranked expression correlation list
• Just 8% of the miRNA show significant
enrichment for genes in the middle ten
percentile
Correlated
Anti-correlated
Step 2: computing conservation enrichment
• They developed a method that avoids target prediction
and is independent of 3’UTR lengths
• The new measure derives from observations that putative
miRNA binding sites have a higher probability of being
evolutionarily conserved
High correlated / anti correlated genes had
high conservation enrichment scores
• 67% of the miRNAs had a significant conservation
enrichment score in their top or bottom 10%
• Only 8% had a significant enrichment score in their
middle 10%
Type I circuits –
m and T positively coregulated
Facts that supports
Type I circuits
• High correlation between
gene and miRNA
expression levels
• The miRNA and the
target gene had a
complementary seed
Type I circuits
Type II circuits –
m and T negatively coregulated
Facts that supports
Type II circuits
• Strong anti- correlation
between gene and
miRNA expression
levels
• The inhibition of the
miRNA (by itself) on the
target mRNA levels is
limited
Type II circuits
Integration of transcription regulation
and post-transcriptional regulation
networks
Regulation by miRNA
Transcription regulation
miRNA
Transcription factor
Transcription regulation
miRNA regulation
Integrated
networkloop
Multi-layer
feed-forward
For conclusion
• It is of value to detect and understand network motifs in
order to gain insight into their dynamical behavior
• As more systems are investigated, it is likely that more
complicated cases will be found: this is an open field for
research
• Today, there’s much more data of targeted miRNAs genes
and miRNAs transcription regulation (by TFs).
• Studied are still trying to integrate between the two levels of
regulation and find motifs that include both miRNAs and TFs