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Humboldt- Universität zu Berlin Edda Klipp Systembiologie 9 – Signal Transduction Sommersemester 2010 Humboldt-Universität zu Berlin Institut für Biologie.

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Presentation on theme: "Humboldt- Universität zu Berlin Edda Klipp Systembiologie 9 – Signal Transduction Sommersemester 2010 Humboldt-Universität zu Berlin Institut für Biologie."— Presentation transcript:

1 Humboldt- Universität zu Berlin Edda Klipp Systembiologie 9 – Signal Transduction Sommersemester 2010 Humboldt-Universität zu Berlin Institut für Biologie Theoretische Biophysik

2 Humboldt- Universität zu Berlin Modeling of Signal Transduction Before: Metabolismus - Mass transfer Now: Signal transduction - Information transfer Typical Signals: Hormones, pheromones Heat, cold, osmotic pressure concentration of certain substances (K, Ca, cAMP,..) nutrient availability Interactive Animation of MAP Kinase Signal Transduction

3 Humboldt- Universität zu Berlin Typical Mechanism Signal Activation of receptor at membran Internalization of signals G-Protein, Phosphorelay Signal transmission Activation of transcription factors Transcription, Translation, Protein function biochemical response Gen mRNAProtein Downregulation of signal

4 Humboldt- Universität zu Berlin Yeast Signaling Pathways

5 Humboldt- Universität zu Berlin Signaling Pathway Components

6 Humboldt- Universität zu Berlin Rezeptors transmembrane receive signal and transmit it conformation change active or inactive form Simple concept: H + R HR K D = H R. H - Hormone R - Receptor HR - Hormone-receptor-complex Typical values : K D = M … M Ligand Extrazellular space Intrazellular space Membrane Receptor, Binding site Rezeptor, zytosolische Domaine inactiveactive

7 Humboldt- Universität zu Berlin Receptor, Extended Model RiRi RsRs RaRa L visvis vsivsi vsavsa vasvas vpivpi v di v ai vpsvps v ds v da Differential equations Rate expressions ?? Mass action Hill kinetics

8 Humboldt- Universität zu Berlin Receptor, Model of Yi et al. RiRi RsRs RaRa L visvis vsivsi vsavsa vasvas vpivpi v di v ai vpsvps v ds v da Time RsRs RaRa Number of Molecules +L

9 Humboldt- Universität zu Berlin G-Proteine: small G-proteins Differential equationsConservation relations GDPGTP GDP ++ z.B. Ras-Protein GDP Ras GTP Ras GDP GTP GEF GAP PiPi v1v1 v2v2 GEF – Guanine nucleotide exchange factor GAP – GTPase-activating protein

10 Humboldt- Universität zu Berlin G-Proteine: small G-proteins e.g. Ras-Protein GDP Ras GTP Ras GDP GTP GEF GAP PiPi v1v1 v2v2 GTP Ras GAP GEF GAP GEF Differential equations Mass action Michaelis Menten GEF or GAP =1 (const.), other varying from 0 to 10 Enzyme concentration

11 Humboldt- Universität zu Berlin G-Proteins: small G-proteins Differential equations e.g. Ras-Protein GDP Ras GTP Ras GDP GTP GEF GAP PiPi v1v1 v2v2 GTP Ras GEF GAP sigmoidal dependence Ultrasensitivity Switch-like regulation GTP Ras Enzyme: GEF Enzyme concentration

12 Humboldt- Universität zu Berlin G-Proteins: small G-proteins e.g. Ras-Protein GDP Ras GTP Ras GDP GTP GEF GAP PiPi v1v1 v2v2 GTP Ras Enzym: GEF GTP Ras Zeit GEF: 0 x x=0.5 x=1.0 x=1.5 x=2.5 x=2.0

13 Humboldt- Universität zu Berlin G-Protein GDP G GTP G GDP G GDP active receptor PiPi signal G PiPi slow fast RGS GTP v ga vh1vh1 vh0vh0 v sr Time G Number of Molecules G GDP G GTP G Differential equationsConservation relations GDP GTP + GDP +

14 Humboldt- Universität zu Berlin Phosphorelay-System Asp His Sln1 ATP ADP PiPi i Ypd1 Ssk1-P PiPi PiPi PiPi high osmolarity ? Ypd1-P Ssk1 Asp Example: Sln1 pathway, Phosphorelay system His Asp - Transmits individual phosphate groups

15 Humboldt- Universität zu Berlin Phosphorelay-System k A, B, C A-P A ADP ATP B B-P C-P C P k1k1 k2k2 k3k3 k4k4 Three component system Two components One component Time Dependence of steady state values Of stress strength Temporal behavior, Stress – no Stress A, B, C

16 Humboldt- Universität zu Berlin Phosphorelay-System B C-P B-P C v3v3 v4v4 A-P A v2v2 v1v1 Concentration C Concentration, a.u. Rate constant k 4 Time a.u. k 1 =10 k 1 = C BA Dynamics Steady State

17 Humboldt- Universität zu Berlin MAP Kinase Cascade = Mitogen activated protein kinase cascade MAPKKKK MAPKKK inactive MAPKKK active MAPKK inactive MAPKK active MAPK inactive MAPK active Signal

18 Humboldt- Universität zu Berlin MAP Kinase Cascade - Equations

19 Humboldt- Universität zu Berlin MAP Kinase Cascade - Equations k – Kinase, p - Phosphatase Steady state Sigmoidale dependence of concentration of activated MAP kinase on concentration of input signal.

20 Humboldt- Universität zu Berlin MAPK Cascade: Impact of Kinases and Phosphatase k=1 k=2 k=3 k=4 k=5 k= p= k=1 p=0.5 p=0.3 p=0.4 p=0.1 p=0.2 Time, a.u. MAPK-PP, a.u. Time, a.u. A B C D MAPK-PP, a.u. k – Kinase, p - Phosphatase

21 Humboldt- Universität zu Berlin MAPKP 2 MAPKP 2 (t) Time MAPKKKK=0.1 k = 0.04 k = 0.36 k = 0.16 k = 0.64 k = 1 k/pk/p MAPKKKK= Sigmoide input/output dependence - Signal amplification Time coursesSteady states MAP Kinase Cascade – Parameter Dependence k – Kinase, p - Phosphatase

22 Humboldt- Universität zu Berlin MAPK Cascade: Control P 1,0 P1P1 1 2 P0P0 P 2,0 P2P2 3 4 P 3,0 P3P Rates P1,0 P1 P2,0 P2 P3,0 P positive none negative

23 Humboldt- Universität zu Berlin MAPK Cascade: Control P 1,0 P 0 P 1,0 P1XP1X P0P0 P1P1 P 2,0 P 1 P 2,0 P2XP2X P2P2 P 3,0 P 2 P 3,0 P3XP3X P3P3 with complex formation Rates P1,0 P0 P1,0 P1 P1X P2,0 P1 P2,0 P2 P2X P3,0 P2 P3,0 P3 P3X X – phosphatase positive none negative

24 Humboldt- Universität zu Berlin MAPK-Cascade with Feedback and Michaelis-Menten Kinetics: Oscillations

25 Humboldt- Universität zu Berlin MAP Kinase Cascade – Scaffolding MAPKKK MAPKK MAPK Ste5 Ste11 Ste7 Fus3 Scaffold

26 Humboldt- Universität zu Berlin MAP Kinase Cascade – Scaffolding Ste5 Ste11 Ste7 Fus3 Double Phosphorylation of each protein

27 Humboldt- Universität zu Berlin Quantitative Measures for Signaling P 1,0 P1P1 v 1f v 1r P 2,0 P2P2 P 3,0 P3P3 v 2r P0P0 v 2f v 3f v 3r Time, a.u. Concentration, a.u. A1A1 1 1 P1P1 P1P1 max t1t1 (a)(b) Transition time Signal durationAmplitude Heinrich et al., T.A. Mol.Cell, 2002

28 Humboldt- Universität zu Berlin Crosstalk & Signal Integration Schaber, Kofahl, Kowald & Klipp, 2006, FEBS J. Signal Receptor AReceptor B Target ATarget B X – function of amplitude, timing or integral of response Measures of crosstalk S e > 1 S e < 1 S i > 1 S i < 1 Mutual signal inhibition Mutual signal amplification Dominance of extrinsic signal Dominance of intrinsic signal Pheromone Pathway Filamentous Growth Pathway Crossactivation Mutual signal amplification Crossinhibition Dominance of intrinsic signal

29 Humboldt- Universität zu Berlin Crosstalk P 1A,0 P 1A v 1Af v 1Ar P 2A,0 P 2A P 3A,0 P 3A v 2Ar = P 0A v 2Af v 3Af v 3Ar P 1B,0 P 1B v 1Bf v 1Br P 2B,0 P 2B P 3B,0 P 3B = P 0B v 2Bf v 3Bf v 3Br (a) v 2Br P 1A P 2A P 3A P 1B P 2B P 3B P 1A P 2A P 3A P 1A P 2A P 3A Time a.u Concentration a.u. Time a.u Concentration a.u. P 1B P 2B P 3B k i = 1 k i = 10 Concentration a.u.

30 Humboldt- Universität zu Berlin Crosstalk P 1A,0 P 1A v 1Af v 1Ar P 2A,0 P 2A P 3A,0 P 3A v 2Ar = P 0A v 2Af v 3Af v 3Ar P 1B,0 P 1B v 1Bf v 1Br P 2B,0 P 2B P 3B,0 P 3B = P 0B v 2Bf v 3Bf v 3Br v 2Br P 1A P 2A P 3A P 1A P 2A P 3A P 1A P 2A P 3A Concentration a.u. Time a.u Concentration a.u. k i = 1 k i = 10 Concentration a.u. I = P max = t max = I = P max = t max = I = P max = t max = Integrated Response Timing of Response S i (P max ) = 0.97 S e (I) = S i (I) = 0.91 S e (P max ) = 0.34 S e (t max ) = S i (t max ) = 1.04 Mutual amplification Dominance of intrinsic signal Maximal Response

31 Humboldt- Universität zu Berlin Integration of Signaling Pathways Fus3 phosphorylation in MAPKcascade 6 -repeated Fus3 phosphorylation 10-Kss1 phosphorylation in MAPKcascade 21-Kss1 release from Ste12Tec1 complex Response coefficients of 6 Time/min PREsFREs

32 Humboldt- Universität zu Berlin Putting all together : the Pheromone pathway a a a a MATa-cells MAT -cells

33 Humboldt- Universität zu Berlin MATa-cells MAT -cells Putting all together: the Pheromone pathway a a a a

34 Humboldt- Universität zu Berlin Pheromone pathway Ste50 Cdc42 Ste5 G G P P Nucleus Cytoplasm Bem1 Cdc24 Plasma membrane Extracellular space Ste20 Ste2 G G G G Ste11 Ste7 Fus3 Far1 Cdc24 P P Cln Cdc28 Far1 Cdc24 Far1 Actin G Ste20 Cdc42 G Cdc24 Bem1 Bar1 active GTP GDP G Sst2 Ste12 Dig1 Dig2 Kss1 Ste12 Dig1 Dig2 Kss1 Ste12 Ste2

35 Humboldt- Universität zu Berlin Pheromone pathway Cdc42 Ste5 G G Bem1 Cdc24 Extracellular space Ste20 Ste2 G G G G GTP GDP G Sst2 Ste2 Nucleus Cytoplasm Far1 Cdc24 P P Cln Cdc28 Far1 Cdc24 Bar1 active Ste12 Dig1 Dig2 Kss1 Ste12 Dig1 Dig2 Kss1 Ste12 P P Fus3 Plasma membrane Far1 Actin G Ste20 Cdc42 G Cdc24 Bem1 Ste50 Ste11 Ste7 Fus3

36 Humboldt- Universität zu Berlin Pheromone pathway Ste50 Cdc42 Ste5 G G P P Bem1 Cdc24 Plasma membrane Extracellular space Ste20 Ste2 G G G G Ste11 Ste7 Fus3 Far1 Actin G Ste20 Cdc42 G Cdc24 Bem1 GTP GDP G Sst2 Ste2 Nucleus Cytoplasm Far1 Cdc24 P P Cln Cdc28 Far1 Cdc24 Bar1 active Ste12 Dig1 Dig2 Kss1 Ste12 Dig1 Dig2 Kss1 Ste12

37 Humboldt- Universität zu Berlin Pheromone pathway Ste50 Cdc42 Ste5 G G P P Nucleus Cytoplasm Bem1 Cdc24 Plasma membrane Extracellular space Ste20 Ste2 G G G G Ste11 Ste7 Fus3 Far1 Cdc24 P P Cln Cdc28 Far1 Cdc24 Far1 Actin G Ste20 Cdc42 G Cdc24 Bem1 Bar1 active GTP GDP G Sst2 Ste12 Dig1 Dig2 Kss1 Ste12 Dig1 Dig2 Kss1 Ste12 Ste2

38 Humboldt- Universität zu Berlin Pheromone pathway: structural parts Ste2 G Fus3Sst2 Ste12Bar1 MAPK scaffold Far1Cdc28 Plasma membrane Gene expression Complex formation Signaling cascade G protein cycle Receptor activation Pheromone

39 Humboldt- Universität zu Berlin Pheromone pathway: structural parts Ste2 G Fus3Sst2 Ste12Bar1 MAPK scaffold Far1Cdc28 Plasma membrane Gene expression Complex formation Signaling cascade G protein cycle Receptor activation Pheromone Yu et al., Nature, 2008

40 Humboldt- Universität zu Berlin Pheromone pathway: time courses In comprehensive model: regulatory feedback loops are considered mutant phenotypes can be investigated Ste12 active Fus3PP G -Far1 Relative Concentration -factor / nM Far1- Cdc28 Graded response depending on concentration of - factor Polarized growth Cell Cycle arrest Kofahl & Klipp, Yeast, 2004

41 Humboldt- Universität zu Berlin Pheromone pathway: time courses Fus3-PP G Overexpression G G defect in binding G Overexpression G sst2 Sst2 mutant Sst2 gain of function

42 Humboldt- Universität zu Berlin Yu & Brent et al.: Experimental Data

43 Humboldt- Universität zu Berlin Yu & Brent et al.: Experimental Data DoRA – Dose Response Alignment

44 Humboldt- Universität zu Berlin Yeast Cell as an Osmometer Eriksson, Lab on Chip, 2006 Serge Pelet, ETH, Zürich Yeast cells shrink upon osmoshock Stress adaptation requires glycerol accumulation. MAPK Hog1 is considered a key player.

45 Humboldt- Universität zu Berlin Osmotic Stress Response Ypd1 High osmolarity Ssk1 Sln1 Ssk2 Pbs2 Hog1 mRNA Protein Glycerol Turgor Fps1 Construct network from literature data and experts knowledge Study properties of small modules, e.g. MAPK cascade, G protein cycles, … MKKK-P MKK-PPMKK MK-PPMK k p MKKKK MKKK k/p=1 k/p=2 k/p=3 k/p=4 k/p=5 k/p= Time, a.u. MAPK-PP, a.u. Parameter change Amplitude Duration Collect experimental data (time series!!!) Estimate model parameters Simulate: Agreement of model/experiment? Sensitivity analysis Prediction of hitherto untested scenarios - Deletion mutants - Compound overexpression - New experimental scenarios Transcriptome data – mRNA levels Proteome data – phosphorylation, concentration changes Metabolome data – concentration changes MAPK cascade Phosphorelay Gene regulation Metabolism Systems equations (Set of ODEs) r – number of reactions S i – metabolite concentrations v j – reaction rates n ij – stoichiometric coefficients Network properties Individual reaction properties

46 Humboldt- Universität zu Berlin Osmostress Response – Full Model Klipp, Nordlander, Krüger, Gennemark & Hohmann, Nature Biotechn, 2005

47 Humboldt- Universität zu Berlin Two Pathways for Stress Osmotic Response Ypd1 High osmolarity Ssk1 Sln1 Ssk2 Pbs2 Hog1 mRNA Protein Glycerol Turgor Fps1 Time / min mRNA Ssk1 Concentration, relative Hog1P 2 Gpd1 A WT Hog1P 2 Time / min wild type Fps1 open Ptp2 over Fps open+Ptp2 over Time / min mRNA Glyc in Concentration, relative Hog1P 2 Protein A Gpd1 Fps1 mutant Osmotic stress Klipp et al.,Nature Biotechn, 2005

48 Humboldt- Universität zu Berlin Osmotic stress model: Test cases Ypd1 High osmolarity Ssk1 Sln1 Ssk2 Pbs2 Hog1 mRNA Protein Glycerol Turgor Fps Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ ÷ ÷ ó Cells are competent to respond to a second shock. mRNA, relative 60 min 30 min 15 min Single Time/min mRNA, relative Repeated osmostress 60 min 30 min 15 min Single x x x x Klipp et al.,Nature Biotechn, 2005

49 Humboldt- Universität zu Berlin Osmotic stress response: What is the impact of specific components over time ? Ypd1 High osmolarity Ssk1 Sln1 Ssk2 Pbs2 Hog1 mRNA Protein Glycerol Turgor Fps1 Response coefficients Time-dependent Response Coefficients Related to Glycerol Concentration Closure of Fps1 Inhibition of Sln1 Strength of osmoshock Hog1 nuclear import Ssk1 dephosphorylation Glycerol export mRNA degradation Hog1 dephosphorylation Hog1 nuclear export Sln1 phosphorylation Hog1 phosphorylation Glycerol influx mRNA and protein production Time / min Glycerol concentration Ingalls & Sauro, JTB, 2003

50 Humboldt- Universität zu Berlin Signaling Pathways in Yeast

51 Humboldt- Universität zu Berlin Model Selection: Sho branch I

52 Humboldt- Universität zu Berlin Model Selection: Sho branch II Different architectures – which one explains data best?

53 Humboldt- Universität zu Berlin Model Selection: Sho branch III

54 Humboldt- Universität zu Berlin Model Size – Skeleton Model Ypd1 High osmolarity Ssk1 Sln1 Ssk2 Pbs2 Hog1 mRNA Protein Glycerol Turgor Fps1 MAPK cascade Phosphorelay Gene regulation Metabolism Hog1P 2 Osmolarity ex mRNA TurgorGlycerol Fps1

55 Humboldt- Universität zu Berlin Oscillatory Input – Oscillatory Output

56 Humboldt- Universität zu Berlin Oscillatory Input – oscillatory output x – intracellular osmotic pressure y – nuclear Hog1

57 Humboldt- Universität zu Berlin Oscillatory Input – oscillatory output x – intracellular osmotic pressure y – nuclear Hog1

58 Humboldt- Universität zu Berlin Simplified, yet Comprehensive Model of Osmotic Stress Response Zi et al., PLoS ONE, 2010 Data from Mettetal et al., Science, 2008

59 Humboldt- Universität zu Berlin Signal Response Gain

60 Humboldt- Universität zu Berlin Glycerol Accumulation Depends on Stress and Nutritional Conditions Glycerol Time stressGlucose stress Glucose More stress, stronger response More stress, slower response More glucose, stronger response More glucose, faster response

61 Humboldt- Universität zu Berlin Flows Influencing Glycerol Glycerolflux Time Total Production Transcriptionally regulated Export Volume-regulated Net Production

62 Humboldt- Universität zu Berlin Systembiologie Systemische Betrachtung von biologischen Sachverhalten und Prozessen Zusammenspiel von Experiment und Theorie – iterative cycle Häufig: Erzeugung, Analyse und Interpretation großer Datenmengen Immer öfter: gezielte Erhebung von Daten zur Modellierung Modellierung: - verschiedene Modellierungsansätze haben ihre Stärken und Schwächen - ein Sachverhalt kann mit unterschiedlichen Modellen beschrieben werden - kein sinnvolles Modell ohne sinnvolle Fragestellung

63 Humboldt- Universität zu Berlin Signal-Motive S – Signal, R – Response S R Kinetik linear Michaelis-Menten Steady State Response linear hyperbolic Signal S (arbitrary units) Response R (arbitrary units) sigmoid hyperbolic linear Vgl.: Tyson, Chen & Novak, Current Op. Cell Biology, 2003

64 Humboldt- Universität zu Berlin Signal-Motive S – Signal, R – Response Kinetik linear Michaelis-Menten Steady StateResponse hyperbolic Signal S (arbitrary units) Response R (arbitrary units) sigmoid hyperbolic linear S R R0R0 sigmoid One loop Goldbeter-Koshland-Funktion Vgl.: Tyson, Chen & Novak, Current Op. Cell Biology, 2003

65 Humboldt- Universität zu Berlin Signal-Motive S – Signal, R – Response Kinetik linear Steady StateResponse Signal S (arbitrary units) Response R (arbitrary units) sigmoid hyperbolic linear sigmoid S R1R1 R0R0 R Two loops Vgl.: Tyson, Chen & Novak, Current Op. Cell Biology, 2003

66 Humboldt- Universität zu Berlin Signal-Motive S – Signal, R – Response Perfect adaptation Vgl.: Tyson, Chen & Novak, Current Op. Cell Biology, 2003

67 Humboldt- Universität zu Berlin Signal-Motive S – Signal, R – Response Mutual activation Vgl.: Tyson, Chen & Novak, Current Op. Cell Biology, 2003

68 Humboldt- Universität zu Berlin Signal-Motive S – Signal, R – Response Mutual inhibition Vgl.: Tyson, Chen & Novak, Current Op. Cell Biology, 2003

69 Humboldt- Universität zu Berlin Signal-Motive S – Signal, R – Response Negative feedback: homeostasis Vgl.: Tyson, Chen & Novak, Current Op. Cell Biology, 2003

70 Humboldt- Universität zu Berlin Signal-Motive S – Signal, R – Response Negative feedback: oscillations Vgl.: Tyson, Chen & Novak, Current Op. Cell Biology, 2003

71 Humboldt- Universität zu Berlin Signal-Motive S – Signal, R – Response Activator – Inhibitor Vgl.: Tyson, Chen & Novak, Current Op. Cell Biology, 2003

72 Humboldt- Universität zu Berlin Signal-Motive S – Signal, R – Response Substrate-depletion oscillator Vgl.: Tyson, Chen & Novak, Current Op. Cell Biology, 2003


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