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. Table of Content. Chapter 5. Temporal Programs and the Global Structure of Transcription Networks. A short remainder.
An Introduction to Systems Biology
Temporal Programs and the Global Structure of Transcription Networks
We have seen that transcription networks contain recurring network motifs that can perform specific dynamical functions.
We examined two of this motifs in details: auto-regulation and feed-forward loop (FFL).
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: auto-regulation and FFL (we have already studied), Single Input Module (SIM) and Dense Overlapping Regulons (DORs).
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 Thus ER networks have very few large SIMs.
The most important task of SIM is to
control a group of genes according to
the signal sensed by the master
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).
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 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.
It is very difficult to recognize motifs on large graphs:
Do you remember the large number of 4-nodes possible sub-graphs?
Only 2 of them were real motifs:
After we learnt about motifs, we can locate the motifs on E-coli’s network and draw it in a much simple way
Governs the fates of cells, as an egg develops into a multi-cellular organism.
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 developmentalTranscription Networks
What is the difference between Sensory andDevelopmentalTranscription Networks?
Long transcription cascades and developmental timing
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. subtilisSporulation network
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.
To initiate the sporulation process, a starvation signal Sx activates X1
Incoherent Type-1 FFL
Coherent Type-1 FFL
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.
Sense and process information from the environment, and accordingly regulate the activity of transcription factors or other effector proteins.
Signal Transduction Networks
Toy Model for protein kinase preceptrons
Multi-layer perceptrons In protein kinase cascades