Motif Mining from Gene Regulatory Networks

1. Motif Mining from Gene Regulatory Networks Based on the publications of Uri Alon’s group …presented by Pavlos Pavlidis Tartu University, December 2005

2. Gene Regulatory Networks • From Wikipedia Gene regulatory network is a collection of DNA segments in a cell which interact with each other and with other substances in the cell, thereby governing the rates at which genes in the network are transcribed into mRNA • From DOE Gene regulatory networks (GRNs) are the on-off switches and rheostats…dynamically orchestrate the level of expression for each gene….

3. Why networks can regulate Gene Expression? • U. Alon and his group, stresses the importance of the building blocks of the network. • These building blocks are called motifs

4. Motifs • They are called also n-node subgraphs in a directed graph (The work has also been extended for undirected graphs) • They are characterized from the number n of the nodes and the relations between them – directed edges

5. The 13 different 3-node subgraphs

6. Feed Forward Loop It regulates rapidly the production of Z

7. In what motifs they are interested • Not in biologically significant • They don’t know a priori if a motif is biologically significant • They can calculate statistical significance • The probability that a randomized network contains the same number or more instances of a particular motif must be smaller than P. Here P is 0.01.

8. Randomized Network • A randomized network is not completely randomized.It has some properties: • The same number of nodes as in the real network • For each node the number of the incoming and outgoing edges equals to the real network.

9. Representation of the network as a matrix M Randomization: Select randomly two cells which are 1 e.g A(1,3), B(2,1). If A’(1, 1) and B’(2, 3) are 0 then swap Goal : The randomized network must have the same sum in columns and in rows Columns: The number of outgoing edges Rows: The number of incoming edges

10. One more requirement: If we are looking for n-node subgraphs, then the number of n-1 node subgraphs must be the same in real and randomized networks This is doneto avoid assigning high significance to a structure only because of the fact that it includes ahighly significant substructure.

11. Significance of a motif • Three requirements • P < 0.01 P was estimated (or bounded) by using 1000 randomized networks. • The number of times it appears in the real network with distinct sets of nodes is at least U = 4. • The number of appearances in the real network is significantly larger than in the randomized networks: Nreal – Nrand > 0.1Nrand (Why??).

13. What did they find • That in biological systems as in E.coli or in S.cerevisiae only some certain types of motifs are statistically important. • When they studied other systems such as:Food webs. The database of seven ecosystem food websNeuronal networks: the neural system of C.elegans WWW OTHER KIND OF MOTIFS WHERE STATISTICALLY IMPORTANT

14. FFL SIM DOR

15. FFL • Biological Example • the L-arabinose utilization system: • Crp is the general transcription factor and AraC the specific transcription factor.

16. The real model

17. FFL • Coherent • Incoherent • Important for the speed of response

18. Software mDraw              Network visualization tool (mfinder and network motifs visualization tool embedded)