1. Network Motifs See some examples of motifs and their functionality Discuss a study that showed how a miRNA also can be integrated into motifs Today’s plan Understand what are “network motifs” and what they are used for

2. 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)

3. For example: food web, neuron synaptic connections, transcription regulation network Introduction - Biological Networks

4. Simple Building Blocks of Complex Networks – Milo, Shen-Orr, Itzkovitz, Kashtan, Chklovskii, Alon Science, 2002

5. Why define simple building blocks?

6. 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

7. Algorithm for detecting network motifs

8. Connected patterns identified as Network Motifs

10. Network Motifs: theory and experimental approaches Alon, Nature Review Genetics, 2007

11. Negative Autoregulation - NAR *Response time – *Response time – the time it takes to reach halfway between the initial and final levels Speeds up the response time* of gene circuits Uses a strong promoter to obtain a rapid initial rise Can reduce cell- cell variability

12. Positive Autoregulation - PAR Slows the response time Reaches its steady state in an S-shaped curve Tends to increase cell- cell variability

13. 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

14. Different configurations of Feedforward loops – FFL

15. Coherent feedforward loop – type I 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)

16. Coherent feedforward loop in E.coli

17. Incoherent Feedforward loop- type I 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 NAR PAR

18. Single Input Modules - SIM Regulator X regulates a group of target genes – usually with shared function Allow coordinated expression of the group

19. Single Input Modules - SIM

20. 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

21. Network Motifs in developmental transcription networks Bacillus subtilis spore

22. Transcription regulation network in E.coli

23. MicroRNA-Mediated Feedback and Feedforward Loops Are Recurrent Network Motifs in Mammals Tsang, Zhu, Van Oudenaarden Cell, 2007 TF Target mRNA Transcriptional regulation Target Gene miRNA Post-transcriptional regulation miRNA TF Transcriptional regulation Missing data

24. Gene miRNA Weak repression But.. Gene miRNA TF If you combine both transcription regulation (TF) and post-transcriptional regulation (miRNA), you get multiple levels regulation with greater strength

25. 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

26. miRNA seed Step 1: Creation of 2 lists for each miRNA 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 1. Ranked list of genes based on the extent of their expression correlation miRNA GeneA GeneB GeneC. GeneZ

27. 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

28. 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

29. 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%

30. Type I circuits – m and T positively coregulated Type I circuits Facts that supports Type I circuits High correlation between gene and miRNA expression levels The miRNA and the target gene had a complementary seed

31. Type II circuits 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 – m and T negatively coregulated

32. Integration of transcription regulation and post-transcriptional regulation networks Transcription factor Transcription regulation miRNA regulation miRNA Regulation by miRNA Transcription regulationIntegrated network Multi-layer feed-forward loop

33. 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