The diversity and integration of biological network motifs
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The Diversity and Integration of Biological Network Motifs. Seminars in Bioinformatics Martin Akerman 31/03/08. Biological Networks. Questions: Which are the most common motifs among biological networks? How do these motifs interrelate?. Biological Network motifs.

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The Diversity and Integration of Biological Network Motifs

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The diversity and integration of biological network motifs

The Diversity and Integration of Biological Network Motifs

Seminars in Bioinformatics

Martin Akerman

31/03/08


Biological networks

Biological Networks

  • Questions:

  • Which are the most common motifs among biological networks?

  • How do these motifs interrelate?


Biological network motifs

Biological Network motifs

Single Input Model (SIM)

Autoregulation (AR)

Dense Overlapping Regulon (DOR)

Feed Forward Loops (FFL)

Regulating and Regulated Feedback Loops (RFL)

BiFan

Diamond

Cascade


Single input model sim

Single Input Model (SIM)

  • 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 sim1

Single Input Model (SIM)

  • The SIMs can generate temporal programs of expression:

Last-In First-Out (LIFO) Program


Lifo program in arginine biosynthesis

LIFO Program in Arginine Biosynthesis


First in first out fifo program

First-In First-Out (FIFO) Program

Kxz1

Kxz2

[X]

Kxz3

Kxz3

Kxz2

[Y]

Kxz1

[Z1]

[Z2]

[Z3]

Kxz1>Kxz2>Kxz3K’xz1<K’xz2<K’xz3

Time


Fifo program in flagella biosynthesis

FIFO program in Flagella Biosynthesis


Fifo program is governed by a ffl

FIFO program is governed by a FFL


Multi input ffl in neuronal networks

Multi-input FFL in Neuronal Networks

Noxious Chemicals

Nose Touch

Nose Touch

FLP

ASH

AVD

AVA

Backward movement


Multi input ffl in neuronal networks1

Multi-input FFL in Neuronal Networks

The change in voltage of Y

X1

X2

The change in voltage of Z

Y

Z


Interlocking feed forward loops

Interlocking Feed forward loops

Bacillus Subtilis sporuation process


Dense overlapping regulon dor

Dense Overlapping Regulon (DOR)


How do network motifs integrate

FFLs and SIMs are integrated within DORs

How do Network Motifs Integrate?

Where are the XYZ ?

A Master Regulators Layer (lots of Auto-Reg.)

A single DOR Layer

The E.coli Transcription Network (partial)


Signal transduction cascades

Signal Transduction Cascades


The diversity and integration of biological network motifs

Popular Motifs in Signal Transduction Cascades

X

X1

X2

Y1

Y2

Y1

Y2

Z

BiFan

Diamond

Generalization of DOR

X1

X2

X

X1

X2

Multi-layer Perceptrons

(multi-DORs)

Y1

Y2

Y1

Y2

Y1

Y2

Z

Z1

Z2

Z1

Z2


Multi layer perceptorns in signal transduction cascades

Multi Layer Perceptorns in Signal Transduction Cascades

X2

X2

P

X1

X1

P

Y2

Y2

P

Y1

Y1

P

Z2

Z2

P

Z1

Z1

P


Dynamics of signal transduction cascades

Dynamics of Signal Transduction Cascades

X1

X2

Y

At Steady State

X2

0.5

,

Activation Threshold

X1

0.5


Dynamics of signal transduction cascades1

Dynamics of Signal Transduction Cascades

“AND” gate

“OR” gate

X1

X2

X1

X2

Y

Y

X2

X2

0.5

0.5

X1

0.5

X1

0.5


Dynamics of signal transduction cascades2

Dynamics of Signal Transduction Cascades

“OR” gate

X1

X2

1.5

1.5

0.7

0.7

Y1

Y2

Z

Y1

Y2

2

2

Z

“AND” gate

X1

X2

1.5

1.5

0.7

0.7

Y1

Y2

Z

Y1

Y2

0.6

0.6

Z


Dynamics of signal transduction cascades3

Dynamics of Signal Transduction Cascades

X1

X2

1.5

1.5

0.7

0.7

Y1

Y2

Z

Y1

Y2

2

-3

Z

X1

X2

1.7

0.7

1.7

0.7

Z

Y1

Z

Y2

Y1

Y2

2

-3

Z


Dynamics of signal transduction cascades4

Dynamics of Signal Transduction Cascades

  • 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


Feed back loops

Feed Back Loops

Z

X

Y

X

Y

  • X transcriptionally activates X.

  • Y inhibits X.

  • Z transcriptionally activates X and Y

  • X forms a complex with Y.

  • X phosphorylates Y.

Power

Heater

Temperature

-

(Fast) Protein-Protein Interactions

Thermostat

(Slow) Transcriptional Interactions


Feed back loops produce oscillation

Feed Back Loops Produce Oscillation

Cdc20 oscillator controls Cell Cycle

Mutation of the Drosophila CWO gene


Developmental transcription netwroks

Developmental Transcription Netwroks

The TF expression profile in a

developing Drosophila embryo


Developmental transcription netwroks1

Developmental Transcription Netwroks

Two-node Feedback Loops

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).


Developmental transcription netwroks2

Developmental Transcription Netwroks

Regulating Feedback Loops

X

Y

X

Y

Z

Z

Double Negative Loops

Double Positive Loops

Z

Z

X

Y

X

Y

Regulated Feedback Loops


Developmental transcription netwroks3

Developmental Transcription Netwroks

Regulated Feedback Loops as a Memory Element


Developmental transcription netwroks4

Developmental Transcription Netwroks

Cascades

X

Y

Z

X

Y

Z


Summary

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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