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Protein Networks Week 5

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Protein Networks. Week 5. A simple example of protein dynamics: protein synthesis and degradation Using the law of mass action, we can write the rate equation. S = signal strength (e.g. concentration of mRNA) R = response magnitude (e.g. concentration of protein). Linear Response. - PowerPoint PPT Presentation

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Page 1: Protein Networks

Protein Networks

Week 5

Page 2: Protein Networks

Linear Response• A simple example of protein

dynamics: protein synthesis and degradation

• Using the law of mass action, we can write the rate equation.

• S = signal strength (e.g. concentration of mRNA)

• R = response magnitude (e.g. concentration of protein)

Page 3: Protein Networks

Linear Response

Page 4: Protein Networks

Protein Cycles20% of the human protein-coding genes encode components of signalingpathways, including transmembrane proteins, guanine-nucleotide binding proteins (G proteins), kinases, phosphatases and proteases.

The identification of 518 putative protein kinase genes and 130 proteinphosphatases suggests that reversible protein phosphorylation is a central regulatory element of most cellular functions.

Page 5: Protein Networks

Abundance of Kinases

Species # of putative kinases

Saccharomyces cerevisiae 121

Drosophila melanogaster 319

C. elegans 437

Arabidopsis thal 1049

Human 518

Data from http://www.kinexus.ca

Page 6: Protein Networks

The Simple Cascade

v 1

v2

Page 7: Protein Networks

Conservation laws

Page 8: Protein Networks

Hyperbolic Response

Assume linear kinetics

Page 9: Protein Networks

Hyperbolic Response

Page 10: Protein Networks

Sigmoidal Response

Assume saturable kinetics

Page 11: Protein Networks

Sigmoidal Response

Assume saturable kinetics

Page 12: Protein Networks

Sigmoidal ResponseMemoryless Switch

Assume saturable kinetics

Page 13: Protein Networks

Fundamental Properties

X

E1

E2

Ultrasensitivity

Kms = 0.5

Page 14: Protein Networks

Fundamental Properties

X

E1

E2

Ultrasensitivity

Kms = 0.1

Page 15: Protein Networks

Fundamental Properties

X

E1

E2

Ultrasensitivity

Kms = 0.02

Page 17: Protein Networks

Digital CircuitsIn ultrasensitive mode, cascades can be

used to build Boolean circuits.

Page 18: Protein Networks

Basic Logic GatesNAND Gate – fundamental building block of all logic circuits

A B C

0 0 1

0 1 1

1 0 1

1 1 0

CBA

Page 19: Protein Networks

Basic Logic GatesNOT Gate

A B

0 1

1 0BA

BA

Page 20: Protein Networks

Basic Logic GatesNOT Gate

A

B

Page 21: Protein Networks

Ring Oscillator

Page 22: Protein Networks

NAND Gate

CBA

B

C

A

Page 23: Protein Networks

Memory UnitsBasic flip-flop

R = resetS = setQ = output

Page 24: Protein Networks

Memory Units

Clocked RS flip-flop

R = resetS = setC = clockQ = output

Page 25: Protein Networks

Counters

0 0 0 0

1 0 0 0

0 1 0 0

1 1 0 0

0 0 1 0

1 0 1 0

0 1 1 0

1 1 1 0

Binary Counter

etc

Clock RS flip-flop

Clock input

Page 26: Protein Networks

Arithmetic

Half Adder (No carry input)A B Sum Carry

0 0 0 0

0 1 1 0

1 0 1 0

1 1 0 1

Page 27: Protein Networks

Sigmoidal ResponseMultiple Cycles

Assume linear kinetics

S3

Page 28: Protein Networks

Sigmoidal ResponseBistable Switches

Cell-signalling dynamics in time and spaceBoris N. Kholodenko Nature Reviews Molecular Cell Biology 7, 165-176 (March 2006) |

Page 29: Protein Networks

Sigmoidal ResponseOscillators

Cell-signalling dynamics in time and spaceBoris N. Kholodenko Nature Reviews Molecular Cell Biology 7, 165-176 (March 2006) |

Page 30: Protein Networks

Sigmoidal ResponseOscillators

Cell-signalling dynamics in time and spaceBoris N. Kholodenko Nature Reviews Molecular Cell Biology 7, 165-176 (March 2006) |

Page 31: Protein Networks

Sigmoidal ResponseOscillators

Cell-signalling dynamics in time and space Boris N. Kholodenko Nature Reviews Molecular Cell Biology 7, 165-176 (March 2006) |

Page 32: Protein Networks

Amplifiers – basic amplifier

Ktesibios, 270BC invented the float regulator to maintain a constant water flow which was in turn used as a measure of time.

http://www.control-systems.net/recursos/mapa.htm

Page 33: Protein Networks

Amplifiers – basic amplifier

Centrifugal fly-ball governor, introduced by Watt in 1788 to control the speed of the new steam engines.

http://visite.artsetmetiers.free.fr/watt.html

By 1868 it is estimated that 75,000 governors were in operation in England

Page 34: Protein Networks

Amplifiers – basic amplifier

Harold Black in 1927, invented the first feedback amplifier in order to solve the problem of signal distortion when American Telephone and Telegraph wanted to lay telephone lines all the way from the east to the west coast.

Page 35: Protein Networks

Amplifiers – basic amplifier

Amplifier (A)

Feedback (k)

Output (y)Input (u)

e = error

-

+ e

Page 36: Protein Networks

Amplifiers – basic amplifier

Amplifier (A)

Feedback (k)

Output (y)Input (u)e

-

+

Page 37: Protein Networks

Amplifiers – basic amplifier

Amplifier (A)

Feedback (k)

Output (y)Input (u)e

-

+

Page 38: Protein Networks

Amplifiers – basic amplifier

Amplifier (A)

Feedback (k)

Output (y)Input (u)e

-

+

Page 39: Protein Networks

Amplifiers – basic amplifier

Amplifier (A)

Feedback (k)

Output (y)Input (u)e

If kA > 0 then

-

+

Page 40: Protein Networks

Amplifiers – basic amplifier

Amplifier (A)

Feedback (k)

Output (y)Input (u)

1. Robust to variation in amplifier characteristics2. Linearization of the amplifier response3. Amplification of signal4. Preferential changes in input and output impedances5. Improved frequency response

741 op amp

Page 41: Protein Networks

Feedback – basic amplifier

Page 42: Protein Networks

Amplifiers – basic amplifier

Provided the feedback is below thethreshold to cause oscillations, feedbacksystems can behave as robust amplifiers.

Page 43: Protein Networks

Amplifiers – Synthetic Amplifier

Page 44: Protein Networks

Cascades as Noise Filters

Cascades can act assignal noise filters in the most sensitive region

Output

-50

-40

-30

-20

-10

0

10

20

30

0.001 0.01 0.1 1 10 100

Frequency

Mag

nitu

de

V1 = 1.7

V1 = 3.06

V1 = 3.4

V1 = 3.74

V1 = 5.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.5 1 1.5 2

V1 / V2

P2 /

(P1

+ P2

)

Page 45: Protein Networks

Why? Frequency Analysis

-35

-30

-25

-20

-15

-10

-5

00 0.5 1 1.5 2

V1/V2

Nr(

dv/d

s)L

Jacobian

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.5 1 1.5 2

V1 / V2

P2 /

(P1

+ P2

)

Page 46: Protein Networks

Homeostatic Systems – perfect adaptation

Simultaneous stimulation of input and output steps

Page 47: Protein Networks

ThursdaySimulating other kinds of ‘computational’ behavior

1. Adaptive systems2. Amplifiers and feedback regulation3. Feed-forward networks4. Low an high pass filter