Motif Mining from Gene Regulatory Networks

Based on the publications of Uri Alon’s group

…presented by Pavlos Pavlidis

Tartu University, December 2005

Gene Regulatory Networks

• From WikipediaGene 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 DOEGene regulatory networks (GRNs) are the on-off switches and rheostats…dynamically orchestrate the level of expression for each gene….

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

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

The 13 different 3-node subgraphs

Feed Forward LoopIt regulates rapidly the production of Z

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.

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.

Operon 1 Operon 2 Operon 3 Operon 4 Operon 5 Operon 6 …Operon 1 0 0 1 0 0 0Operon 2 1 0 0 1 0 0Operon 3Operon 4Operon 5 Mij:Operon 6 1 if the j operon produces a TFOperon 7 which ragulates operon iOperon 8Operon 9Operon 10 1Operon 11 operon 2 regulates Operon 12 operon 11Operon 13Operon 14Operon 15Operon 16Operon 17Operon 18

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

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 done to avoid assigning high significance to a structure only because of the fact that it includes a highly significant substructure.

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??).

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

FFL

SIM

DOR

FFL

• Biological Example– the L-arabinose utilization system:– Crp is the general transcription factor and

AraC the specific transcription factor.

The real model

FFL

• Coherent

• Incoherent

• Important for the speed of response

Software

mDraw             Network visualization tool(mfinder and network motifs visualization tool embedded)