Universidad de TalcaUniversidad de Talca4ta Jornada de Investigación y Asistencia Técnica4ta Jornada de Investigación y Asistencia Técnica

“Systems Biology”:el nuevo Desafío

J.A. AsenjoCentre for Biochemical Engineering and Biotechnology

Systems Biology and Cell Dynamics Seminars

University of Chile

Diciembre 2005

Biotecnología de Futuro:un Desafío para Chile

Systems Biology

We haven’t the money, so we’ve got to think

Ernest Lord Rutherford, 1871 - 1937

• Edward Jenner (1749 –1823): “cowpox” – smallpox – Vacuna viruela

• 1850 Luis Pasteur: Microorganismos: fermentación no es espontánea

• 1928: Alejandro Flemming : Penicilina

• 1939: Florey, Chain purificación de penicilina y producción masiva

USA-Pfizer Producción de ácido cítrico

levadurasfermentaciónEsterilización (descubrió los microorganismos)

(Enzimas)

• 1945: Premio Nobel: Flemming, Florey, Chain

azúcar

levadura

CO2 + H2O

alcohol

• 60’s - 70’s Ingeniería Genética

• 80’s INSULINA: Ingeniería genética de E.coli y S.cerevisiae Insulina comercial recombinante

• Hoy: Eli-Lilly– Novo-Nordisk

• 90’s: tpA

• Vacunas: Contra hepatítis B (Merck, Chiron)

Sida

• 1990 Sally y Dolly

• Terapia celular y génica

• Enzimas criofílicas

Biotecnología• Nueva Biología Molecular• Proteínas “Clonadas”• Ingeniería de Proteínas• Genómica Funcional• Ingeniería Metabólica (Metabolómica)

• Nuevos Productos Terapéuticos• Nuevas Vacunas• Nuevas Enzimas Industriales• Nuevos Microorganismos• Cultivo de Tejidos, Terapia Génica

Is there a Rational Method to Purify Proteins?

from Expert Systems to Proteomics

J.A. Asenjo

University of Chile

The Combinatorial Characteristic of Choosing the Sequence of Operations for Protein Purification

ThirdStage

C1

C2

C3

C5

C6

n thStage

n1

n2

n3

n5

n6

SecondStage

B1

B2

B3

B4

B5

B6

FirstStage

A1

A2

A3

A4

A6

1) Ion Exchange Chromatography

3) Affinity Chromatography

4) Aqueous Two-Phase Separation

5) Gel Filtration

2) Hydrophobic Interaction Chromatography

6) HPLC

Facts Rules

Knowledge base Working memory

Knowledgeacquisition

subsystem

ControlInference

Inference engine

User interface

Explanation subsystem

Expert or Knowledge

engineer

User

The architecture of a knowledge based expert system. (taken from Asenjo, Herrera and Byrne, 1989)

Determination of the Resolution Between Two Peaks

V2-V1

½(W1+W2)RS =

SC RS

=

DF DF

SC RS

V1

V2

W1 W2

Ab

sorb

ance

Time

The model of database components for main protein contaminants in one of the production streams to be used in the selection of optimal separation operation

CHARGE

PROTEINS

PRODUCT

CONTAMINANT 1

CONTAMINANT 2

CONTAMINANT 3

CONTAMINANT 4

CONTAMINANT N

pH 4.0 pH 4.5 . . . . pH 9.5 pH 10.0

PROPERTYCONCENTRATION

MOLECULAR WEIGHT

ISOELECTRIC POINT

HYDROPHO- BICITY

CONTAMINANT 5 . . . . . .

Concentration, molecular weight, hydrophobicity and charge at different pHs, for the main proteins (“contaminants” of the product) in Escherichia coli. Data from Woolston (1994)

Contaminant

Cont_1

Cont_2

Cont_3

Cont_4

Cont_5

Cont_6

Cont_7

Cont_8

Cont_9

Cont_10

Cont_11

Cont_12

Cont_13

pH 7

q G

-2.15

-3.50

-0.85

-1.73

-3.07

-3.05

-1.00

-3.32

-0.21

-0.53

0.05

0.50

1.50

g/litre

weight

11.29

7.06

4.63

5.58

4.83

2.48

7.70

6.80

7.53

6.05

3.89

1.48

0.83

pI 1

4.67

4.72

4.85

4.92

5.01

5.16

5.29

5.57

5.65

6.02

7.57

8.29

8.83

Da

Mol wt 2

18,370

85,570

53,660

120,000

203,000

69,380

48,320

93,380

69,380

114,450

198,000

30,400

94,670

*

hydroph 3

0.71

0.48

0.76

1.50

0.36

0.36

0.48

0.93

0.63

0.06

pH 4

q A

1.94

2.35

1.83

3.29

4.08

5.22

3.96

10.90

1.09

10.40

0.33

5.17

11.70

pH 4,5

q B

0.25

0.29

0.67

1.38

1.83

3.17

3.16

5.81

0.55

5.94

0.03

4.22

7.94

pH 5

q C

-0.80

-1.17

0.04

-0.03

0.04

1.02

1.12

2.78

0.26

3.15

0.05

3.20

5.39

pH 5,5

q D

-1.41

-2.17

-0.30

-0.69

-1.17

-0.72

-0.58

0.77

0.10

1.51

0.05

2.25

3.73

pH 6

q E

-1.76

-2.83

-0.49

-1.07

-1.92

-1.90

-1.36

-0.81

-0.03

0.56

0.05

1.46

2.66

pH 6,5

q F

-1.97

-3.24

-0.65

-1.34

-2.46

-2.60

-1.34

-2.18

-0.12

-0.05

0.05

0.87

1.97

pH 8,5

q J

-2.67

-3.64

-1.50

-2.75

-5.65

-4.24

-2.84

-4.31

-0.32

-1.72

-1.57

0.08

0.51

pH 7,5

q H

-2.33

-3.63

-1.90

-2.30

-3.90

-3.46

-0.95

-4.12

-0.28

-0.99

-0.69

0.30

1.13

pH 8

q I

-2.45

-3.68

-1.34

-2.85

-4.98

-3.90

-1.59

-4.45

-0.32

-1.43

-0.97

0.20

0.80

Charge4 (Coulomb per molecule x 1E25)

* Hydrophobicity expressed as the concentration (M) of ammonium sulphate at which the protein eluted. (Higher values represent lower hydrophobicity). 1 Measured by isoelectric focusing using homogeneous poolyacrylamide gel in Phast System. 2Molecular weight was measured by SDS-PAGE with PhastGel media in Phast System.3Hydrophobicity was measured by hydrophobic interaction chromatography using a phenyl-superose gel in an FPLC and a gradient elution from 2.0 M to 0.0 M (NH4)2SO4 in 20 mM Tris buffer.4Charge was measured by electrophoretic titration curve analysis with PhastGel IEF 3-9 in a Phast System.

DFi

DFi

B

CAS

A

B

b

DFi

B

CA S

DFi

C

S

A

B

D

b´

Representation of the peaks of a chromatogram as triangles, showing how the variation in the value of DF leads to different concentrations of the contaminant protein in the product. The triangle on the left corresponds to the product protein and the triangle of the right corresponds to the peak of the protein being separated (contaminant).

Estructura de las Proteínas

• Estructura Primaria: secuencia lineal de aa

• Estructura Secundaria: algunos aa interactuan

• Estructura Terciaria: cadenas de aa interligadas

• Estructura Nativa: proteína se encuentra activa

• Proteína denaturada: – No tiene actividad– No posee puentes disúlfuro

Producción & Purificación de Proteínas

Proteínas

Cuatro niveles de estructura: desde 1 dimensión a 3 dimensiones

Desde análisis estructurala análisis funcional

Ingeniería de Proteínas

Proteasa criofílica antártica

Ingeniería de Proteínas

• Proteasas activas a baja temperatura (Criofílicas, Psicrofílicas)

• para detergentes

• para industria de alimentos

• Para aplicaciones médicas

Ingeniería de Proteínas• Estudios de Relación Estructura-Función

• Mutagénesis Sitio-Dirigida

• Mutagénesis al Azar

Mutagénesis al azar (random)

Evolución dirigida

“Gene shuffling”(“barajar” genes)

Actividad vs. Análisis utilizado para “screening”

Proteasa criofílica antártica

MetabolómicaIngeniería Metabólica

• Systems Biology: qué viene después de la Genómica

• Uso de Análisis de Flujos Metabólicos y Tecnología de Microarrays de Genes

GLUCGLUC

GLUC6PGLUC6P

FRUC6PFRUC6P

3PG3PG

GAPGAP

PIR PIR

PEPPEPACETACETEtOHEtOH

ACAC

RIBU5PRIBU5P

XIL5PXIL5PRIB5PRIB5P

GAPGAPSED7PSED7P

FRUC6PFRUC6P

aaaa

aaaa

aaaa

aaaa

aaaaaaaaE4PE4P

CARBCARB

ATP ADPATP ADP

RNARNA

OO22EE OO22

COCO22 COCO22EE

2

3

5

LIPLIP

AcCoAAcCoAmitmit

AcCoAAcCoAcitcit

FUMFUM AKGAKG

SUCCoASUCCoASUCSUC

MALMAL ISOCITISOCIT

OACOAC

SODSOD

SODSOD

SODSOD

SODSOD

SODSOD

PROTPROTPROTPROT

PROTPROT

PROTPROT

PROTPROT

6

7

9

13

11

10

10

76

77

70-aaOAC

69

71-aaOAC

17

16

15

14

73-AcCoA

30

70-aaAKG

71-aaAKG

70-aaPIR

PEP

PIR

74

31

3P G

28

2726

E4P

19 20

21

22

23

18 1

25

71-aaPIR

70-aa3PG

71-aaPE P

70-aaPE P

71-aa3PG

71-aaE 4P

70-aaE 4P

70-aaRIB 5P

71-aaRIB 5P

72-nuOAC

72-nuRIB5P

72-nu3P G

NHNH44EE NHNH44

78

LIPLIP

73-GAP

PROTPROTaaaa

RNARNA SODSOD

nunu

OAC

nunu

RI B5P

aaaa

Ac CoAci t

71-aaAcCoA

70-aaAcCoA

AK G

RNARNA

nunu

GLICGLIC

AcCoAAcCoAcitcit

24

75

4

8

Metabolómica

dX/dt = S v - bdX/dt = S v - b in SS: S v = b in SS: S v = b or or S r = 0 S r = 0 SScc r rcc + S + Smm r rmm = 0 = 0

Metabolic Flux AnalysisMetabolic Flux AnalysisMetabolic Flux BalanceMetabolic Flux Balance

AA

EE

BB

CC

DD FF

S r=0=S r=0=1-0100D

01-010C

001-1-1B54321

5

4

3

2

1

100D

010C

1-1-1B321

3

2

1

1-0D

01-C

00B54

5

4

+

SS StoichiometricStoichiometric Matrix Matrixrr Rate (Flux) vectorRate (Flux) vectorcc CalculatedCalculatedmm MeasuredMeasured

0

3

6

9

12

0 9 18 27 36 45

Tiempo, h

Glu

cosa

0.0

0.8

1.6

2.4

3.2

Cél

ulas

y E

tano

l

[GLUC] g/L[X], g/L[EtOH] g/L

Fermentation Profiles: strain PFermentation Profiles: strain P --

0.0

0.3

0.6

0.9

1.2

0 9 18 27 36 45

Tiempo, h

Prot

eína

Tot

al y

Car

bohi

drat

os T

otal

es

0.00

0.06

0.12

0.18

0.24

RN

A T

otal

[CARB] g/L

[PROT] g/L

[RNA] g/L

Profiles of Cell Components: strain P+Profiles of Cell Components: strain P+

0

3

6

9

1 2

1 5

0 9 1 8 2 7 3 6 4 5T im e, h

Glu

cose

, g/L

0 .0

0 .7

1 .4

2 .1

2 .8

3 .5

Cells

, Eth

anol

and

SO

D, g

/L

S tr a in P +S tr a in P + S tr a in PS tr a in P --

0

3

6

9

1 2

1 5

0 9 1 8 2 7 3 6 4 5

T im e, h

Glu

cose

, g/L

0 .0

0 .7

1 .4

2 .1

2 .8

3 .5

Cells

and

Eth

anol

, g/L

0 .0

0 .3

0 .6

0 .9

1 .2

1 .5

0 9 1 8 2 7 3 6 4 5T im e, h

Tota

l Pro

tein

and

Car

bohy

drat

es, g

/L

0 .0 0

0 .0 5

0 .1 0

0 .1 5

0 .2 0

0 .2 5

Tota

l RN

A, g

/L

S tr a in P +S tr a in P + S tr a in PS tr a in P --

0 .0

0 .3

0 .6

0 .9

1 .2

1 .5

0 9 1 8 2 7 3 6 4 5T im e, h

Tota

l Pro

tein

and

Car

bohy

drat

es, g

/L

0 .0 0

0 .0 5

0 .1 0

0 .1 5

0 .2 0

0 .2 5

Tota

l RN

A, g

/L

P+ GLUC

GLUCGLUC

GLUC6PGLUC6P

FRUC6PFRUC6P

3PG3PG

GAPGAP

PIR PIR

PEPPEP

ACETACETEtOHEtOH

ACAC

RIBU5PRIBU5P

XIL5PXIL5PRIB5PRIB5P

GAPGAPSED7PSED7P

FRUC6PFRUC6P

aaaa

aaaa

aaaa

aaaa

aaaa

aaaa

E4PE4P

CARBCARB

ATP ADPATP ADP

RNARNA

OO22EE OO22

COCO22 COCO22EE

3.844

4.169

6.256

LIPLIP

AcCoAAcCoAmitmit

AcCoAAcCoAcitcit

FUMFUM AKGAKG

SUCCoASUCCoASUCSUC

MALMAL ISOCITISOCIT

OACOAC

RNARNA

GLICGLIC

SODSOD

SODSOD

SODSOD

SODSOD

SODSOD

PROTPROTPROTPROT

PROTPROT

PROTPROT

PROTPROT

6.151

6.122

1.470

8.850

3.564

0.079

8.988

0.025

0.121

0.102

0.166

0.097

0.023

0.069

0.029

0.138

0.208

2.232

0.105

0.137

4.130 4.267

0.029

0.234 0.325

0.177

0.148

0.559 4.611

0.247

0.017

0.048

0.004

0.025

0.028

0.004 0.025

0.006 0.006

0.022

0.042

0.019

NHNH44EE NHNH44

0.724

LIPLIP

PROTPROTaaaa

RNARNASODSOD

nunu

nunu

0.174

nunu0.057

aaaa0.063

0.014

0.046

1.470

1.470

1.470

1.345

1.349 1.349

1.397

1.397

0.177

P+ GLUC

GLUCGLUC

GLUC6PGLUC6P

FRUC6PFRUC6P

3PG3PG

GAPGAP

PIR PIR

PEPPEP

ACETACETEtOHEtOH

ACAC

RIBU5PRIBU5P

XIL5PXIL5PRIB5PRIB5P

GAPGAPSED7PSED7P

FRUC6PFRUC6P

aaaa

aaaa

aaaa

aaaa

aaaa

aaaa

E4PE4P

CARBCARB

ATP ADPATP ADP

RNARNA

OO22EE OO22

COCO22 COCO22EE

3.844

4.169

6.256

LIPLIP

AcCoAAcCoAmitmit

AcCoAAcCoAcitcit

FUMFUM AKGAKG

SUCCoASUCCoASUCSUC

MALMAL ISOCITISOCIT

OACOAC

RNARNA

GLICGLIC

SODSOD

SODSOD

SODSOD

SODSOD

SODSOD

PROTPROTPROTPROT

PROTPROT

PROTPROT

PROTPROT

6.151

6.122

1.470

8.850

3.564

0.079

8.988

0.025

0.121

0.102

0.166

0.097

0.023

0.069

0.029

0.138

0.208

2.232

0.105

0.137

4.130 4.267

0.029

0.234 0.325

0.177

0.148

0.559 4.611

0.247

0.017

0.048

0.004

0.025

0.028

0.004 0.025

0.006 0.006

0.022

0.042

0.019

NHNH44EE NHNH44

0.724

LIPLIP

0.002

PROTPROTaaaa

RNARNA SODSOD

nunu

nunu

0.174

nunu0.057

aaaa0.063

0.014

0.046

1.470

1.470

1.470

1.345

1.349 1.349

1.397

1.397

0.177

P+ GLUC

GLUCGLUC

GLUC6PGLUC6P

FRUC6PFRUC6P

3PG3PG

GAPGAP

PIR PIR

PEPPEP

ACETACETEtOHEtOH

ACAC

RIBU5PRIBU5P

XIL5PXIL5PRIB5PRIB5P

GAPGAPSED7PSED7P

FRUC6PFRUC6P

aaaa

aaaa

aaaa

aaaa

aaaa

aaaa

E4PE4P

CARBCARB

ATP ADPATP ADP

RNARNA

OO22EE OO22

COCO22 COCO22EE

3.844

4.169

6.256

LIPLIP

AcCoAAcCoAmitmit

AcCoAAcCoAcitcit

FUMFUM AKGAKG

SUCCoASUCCoASUCSUC

MALMAL ISOCITISOCIT

OACOAC

RNARNA

GLICGLIC

SODSOD

SODSOD

SODSOD

SODSOD

SODSOD

PROTPROTPROTPROT

PROTPROT

PROTPROT

PROTPROT

6.151

6.122

1.470

8.850

3.564

0.079

8.988

0.025

0.121

0.102

0.166

0.097

0.023

0.069

0.029

0.138

0.208

2.232

0.105

0.137

4.130 4.267

0.029

0.234 0.325

0.177

0.148

0.559 4.611

0.247

0.017

0.048

0.004

0.025

0.028

0.004 0.025

0.006 0.006

0.022

0.042

0.019

NHNH44EE NHNH44

0.724

LIPLIP

0.002

PROTPROTaaaa

RNARNASODSOD

nunu

nunu

0.174

nunu0.057

aaaa0.063

0.014

0.046

1.470

1.470

1.470

1.345

1.349 1.349

1.397

1.397

0.177

Parameter

Strain

Flux of ATP Synthesis

mmol ATP/ gCel./ h

Yield

mol ATP/ mol Glucose

% ATP in Respira-

tory Chain

P+ 21.50 4.66 36.09

P- 33.45 6.92 47.86

•• Synthesis of ATPSynthesis of ATP

•• Fluxes CalculatedFluxes Calculated

Metabolic Flux AnalysisMetabolic Flux Analysis

P+

Gluc/Eth

P+ Gluc/Eth

Discrete mathematical models applied to genetic regulation of

metabolic networks

Objectives

Development of a discrete model that will integrate genetic and metabolic networks

Correlate data of Microarrays and Metabolic Flux Analysis

Where: Adaptation of E. coli to different nutrients

Benefits: Understanding of biochemical interactions

Discover regulators and genes

Genes regulando el metabolismo

0 Inactivo

1 Activo

1 / 2 / 3 Activo

0

1 / 2 / 3

Estados

Señales = Biochemicals / Reguladores

-1 / -2 / -3

Flujo Metabólico de Enzima

-1 Inactivo

Gen

Signal2 GeneSignal1

EnzComp B1 Enz1

Enz2 /Signal2

SignalEnz1 /

Signal1

Estudio de dinámica del modelo

67 nodos28 genes21 enzimas18 reguladores / compuestos bioquímicos

Reguladores Ficticios para que modelo alcance Fenotipos

AlgoritmoDefinir combinación de sustratosGenerar 105 vectores aleatoriosActualizar en forma paralela Alcanzar atractor

Cultivo de Tejidos

- tejidos

- células (e.g. sanguíneas)

- órganos

Células para Terapia Celular

Vectores para Terapia Génica

Terapia Génica

• Alcoholism

• Osteoporosis

• Parkinson

• Cancer (e. breast - gene BRCA-1)

• Arthritis

• Hemochromatosis

• Alzheimer

Reduction of Ethanol Intakeafter Gene Therapy

0,2

0,35

0,5

0,65

0,8

0,95

1,1

1,25

1,4

1,55

1,7

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36

DAYS

ET

HA

NO

L IN

TA

KE

(g/

kg)

AdV-control

AdV-ALDH-AS

Vector de Primera Generarión

Vector de Tercera Generación o “gutless”

We haven’t the money, so we’ve got to think

Ernest Lord Rutherford, 1871 - 1937