universidad de talca 4ta jornada de investigación y asistencia técnica “systems biology”: el...
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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
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