Seminar in Bioinformatics, Winter 2011 Network Motifs

1. Seminar in Bioinformatics, Winter 2011Network Motifs An Introduction to Systems Biology Uri Alon Chapters 5-6 By EliadEini & Yasminadmon

2. Table of Content

3. Table of Content

4. Chapter 5 Temporal Programs and the Global Structure of Transcription Networks

5. 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: auto-regulation and feed-forward loop (FFL).

6. 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: auto-regulation and FFL (we have already studied), Single Input Module (SIM) and Dense Overlapping Regulons (DORs).

7. We have seen network motifs before, is there something special you are going to show us?

8. The Single-Input Module Network Motif (SIM) • In the SIM network motif, a master transcription factor X controls a group of target genes, , like we can see in the picture. • 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.

9. 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 Thus ER networks have very few large SIMs.

10. 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).

11. SIM and Temporal Programs

12. An example for a Temporal Program

13. 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.

14. 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.

15. Topological generalization of network motifs It is very difficult to recognize motifs on large graphs:

16. Simple topological generalization of FFL

17. An example of FIFO order in multi-output FFL

18. How does it works?

19. FIFO’s tresholds

20. 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:

21. Dense Overlapping Regulons (DORs)

22. 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

24. Chapter 6

25. Governs the fates of cells, as an egg 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 developmentalTranscription Networks DevelopmentalTranscription Networks

26. the Timescale on which the 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 developmentalTranscription Networks What is the difference between Sensory andDevelopmentalTranscription Networks?

27. Network motifs in developmentalTranscription Networks • Reminder: The response time of each stage in cascades is governed by the degradation/dilution rate of the protein at that stage: • 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. Long transcription cascades and developmental timing

28. 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. subtilisSporulation network

29. B. Subtilissporulation 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.

30. Interlocked Feed-Forward Loops In B. SubtilisSporulation Network To initiate the sporulation process, a starvation signal Sx activates X1 Incoherent Type-1 FFL Coherent Type-1 FFL

31. Interlocked Feed-Forward Loops In B. SubtilisSporulation Network Incoherent Type-1 FFL Coherent Type-1 FFL

32. Interlocked Feed-Forward Loops In B. SubtilisSporulation Network Incoherent Type-1 FFL Coherent Type-1 FFL

33. Interlocked Feed-Forward Loops In B. SubtilisSporulation Network Incoherent Type-1 FFL Coherent Type-1 FFL

34. Interlocked Feed-Forward Loops In B. SubtilisSporulation 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.

35. Chapter 6

36. Sense and process information from the 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 Signal Transduction Networks

37. Diamond Network motifs In Signal Transduction Networks Bi-fan Signaling networks show two strong 4-node motifs

38. Network motifs In Signal Transduction Networks Toy Model for protein kinase preceptrons

39. Protein kinase cascades are usually made of layers, usually three. • This forms multi-layer perceptronsthat can integrate input from several receptors Network motifs In Signal Transduction Networks Multi-layer perceptrons In protein kinase cascades