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The Diversity and Integration of Biological Network Motifs Seminars in Bioinformatics Martin Akerman 31/03/08.

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Presentation on theme: "The Diversity and Integration of Biological Network Motifs Seminars in Bioinformatics Martin Akerman 31/03/08."— Presentation transcript:

1 The Diversity and Integration of Biological Network Motifs Seminars in Bioinformatics Martin Akerman 31/03/08

2 Biological Networks Questions: Which are the most common motifs among biological networks? How do these motifs interrelate?

3 Biological Network motifs BiFan Diamond Autoregulation (AR) Feed Forward Loops (FFL) Regulating and Regulated Feedback Loops (RFL) Single Input Model (SIM) Dense Overlapping Regulon (DOR) Cascade

4 The SIMs are common in sensory transcription networks: Genes from a same Pathway (Arginine synthesis). Genes responding to stress (DNA repair). Genes that assemble a same biological machine (ribosomal genes). Single Input Model (SIM)

5 The SIMs can generate temporal programs of expression: Single Input Model (SIM) Last-In First-Out (LIFO) Program

6 LIFO Program in Arginine Biosynthesis

7 First-In First-Out (FIFO) Program K xz1 >K xz2 >K xz3 K’ xz1

8 FIFO program in Flagella Biosynthesis

9 FIFO program is governed by a FFL

10 Multi-input FFL in Neuronal Networks FLPASH AVD AVA Nose Touch Noxious Chemicals Nose Touch Backward movement

11 Multi-input FFL in Neuronal Networks The change in voltage of Y The change in voltage of Z Y X1 X2 Z

12 Interlocking Feed forward loops Bacillus Subtilis sporuation process

13 Dense Overlapping Regulon (DOR)

14 How do Network Motifs Integrate? The E.coli Transcription Network (partial) A single DOR Layer FFLs and SIMs are integrated within DORs A Master Regulators Layer (lots of Auto-Reg.) Where are the X  Y  Z ?

15 Signal Transduction Cascades

16 Popular Motifs in Signal Transduction Cascades X1X2 Y1Y2 Generalization of DOR BiFan Y2 Z Y1 Diamond X1X2 Y1Y2 Z Y1Y2 Z1Z2 X Y1Y2 Z1Z2 X1X2 Multi-layer Perceptrons (multi-DORs) X

17 X1 P Y1 P Z1 P X2 P Y2 P Z2 P Multi Layer Perceptorns in Signal Transduction Cascades

18 Dynamics of Signal Transduction Cascades At Steady State, Activation Threshold Y X1 X2 X1 X2 0.5

19 Dynamics of Signal Transduction Cascades Y X1 X2 X1 X2 0.5 “AND” gate Y X1 X2 X1 X2 0.5 “OR” gate

20 Dynamics of Signal Transduction Cascades X1X1 X2X2 Y1Y1 Y2Y2 Z X1X1 X2X2 Y1Y1 Y2Y2 Z “AND” gate “OR” gate Y1Y1 Y2Y2 Z Y1Y1 Y2Y2 Z

21 Dynamics of Signal Transduction Cascades X1X1 X2X2 Y1Y1 Y2Y2 Z X1X1 X2X2 Y1Y1 Y2Y2 Z Y1Y1 Y2Y2 Z Y2Y2 Z Y1Y1 Z

22 Multi-layer perceptrons can show:  Discrimination : the ability to accurately recognize certain stimuli patterns.  Generalization : the ability to fill the gaps in partial stimuli patterns.  Graceful degradation : damage to elements of the perceptron or it connections does not bring the network to crashing halt Dynamics of Signal Transduction Cascades

23 Feed Back Loops XY Z (Fast) Protein-Protein Interactions (Slow) Transcriptional Interactions Z transcriptionally activates X and Y X forms a complex with Y. X phosphorylates Y. Y X X transcriptionally activates X. Y inhibits X. PowerHeater Thermostat Temperature -

24 Feed Back Loops Produce Oscillation Mutation of the Drosophila CWO gene Cdc20 oscillator controls Cell Cycle

25 Developmental Transcription Netwroks The TF expression profile in a developing Drosophila embryo

26 Developmental Transcription Netwroks X Y X Y Both X AND Y are ON at the same time. Genes regulated by X and Y belong to the same tissue (or strip). X OR Y is ON at a given time. Genes regulated by X and Y belong to different tissues (strips). Two-node Feedback Loops

27 Developmental Transcription Netwroks XY Z XY Z XY Z XY Z Regulating Feedback Loops Regulated Feedback Loops Double Positive LoopsDouble Negative Loops

28 Developmental Transcription Netwroks Regulated Feedback Loops as a Memory Element

29 Developmental Transcription Netwroks Cascades XYZXYZ

30 Summary Network motifs can function in  several biological processes (sensory systems, development).  different time scales (milliseconds, cell generations). Network motifs can produce temporal programs (LIFO, FIFO, oscillation). Motifs within a network may be arranged in organized structures (perceptrons, interlocking FFL). Different kinds of network may interact to generate regulation


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