1. “Systems Biology” (Biología
de Sistemas) y la Revolución
de la Biotecnología
Juan A. Asenjo
Instituto de Dinámica Celular y Biotecnología
(ICDB):
un Centro para Biología de Sistemas
Universidad de Chile

2. • 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
levadurasfermentación
Esterilización (descubrió los microorganismos)
(Enzimas)
• 1945: Premio Nobel: Flemming, Florey, Chain
azúcar
levadura
CO2 + H2O
alcohol

3. Obtención de Plasmidos
2.- Sacar plasmidio desde bacteria
1.- Se cuenta con bacterias que contienen plasmidos
Cromosoma
Plasmido
Bacteria
Plasmidos
Poración
Producción & Purificación de Proteínas

4. • 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

5. Nueva Biología Molecular
Proteínas “Clonadas”
• Ingeniería Genética
– Enzimas de Restricción
– Plasmidos
Producción & Purificación de Proteínas

6. Obtención de Plasmidos
2.- Sacar plasmidio desde bacteria
1.- Se cuenta con bacterias que contienen plasmidos
Cromosoma
Plasmido
Bacteria
Plasmidos
Poración
Producción & Purificación de Proteínas

7. Principales pasos en la Clonación de
un Segmento de DNA Foráneo
Producción & Purificación de Proteínas
1.- Obtención del DNA foráneo
2.- Corte con Enzimas de restricción del plasmido
Extremos
cohesivos
Extremos
cohesivos
Plasmido
Corte
Plasmido Cortado
(Enzimas de
Restricción)
Extremos
cohesivos

8. 4.b.- Introducción del plasmido Recombinante en célula huesped
⇒
Permeasa
4.- Transformación
4a.- Permeabilización de la célula mediante permeasa
Producción & Purificación de Proteínas

9. Systems Biology

10. We haven’t the money, so we’ve got to
think
Ernest Lord Rutherford, 1871 - 1937

11. Ogni parte ha
inclinazione di
ricongiungersi al
suo tutto
per fuggire dalla
sua imperfezione
Leonardo da Vinci
(Cod.Atl, fol 59 recto)

12. The part always
has a tendency
to reunite with
its whole in order
to escape from
its imperfection
Leonardo da Vinci
(Cod.Atl, fol 59 recto)

13. Systems Biology
Holistic Description of Cellular Functions
Connection
of "Modules"
Modular Aggregation
of Components
Single Component Analysis
Functional Analysis
Metabolic Networks
Regulatory Networks
Signalling Networks
Biological Information/Knowledge
Deductive
Inductive
Top-DownBottom-Up

14. Goal of the InstituteGoal of the InstituteGoal of the Institute
To conduct frontier research in cell
function and dynamics and to develop
models of important biological systems
using a modern Systems Biology
approach
To conduct frontier research in cellTo conduct frontier research in cell
function and dynamics and to developfunction and dynamics and to develop
models of important biological systemsmodels of important biological systems
using a modernusing a modern Systems BiologySystems Biology
approachapproach

15. Holistic ApproachHolistic ApproachHolistic Approach
A multidisciplinary team of
bioengineers, cell and molecular
biologists, mathematicians, biochemists,
chemists and computer scientists
AA multidisciplinary teammultidisciplinary team ofof
bioengineers, cell and molecularbioengineers, cell and molecular
biologists, mathematicians, biochemists,biologists, mathematicians, biochemists,
chemists and computer scientistschemists and computer scientists

16. Modelación Matemática y Optimización
• Biocombustibles: Bioetanol y Biodiesel
• Bioconversión de Celulosa a Metabolitos: Enzimas,
Levadura
• Metabolómica: elucidación de Vías Metabólicas para
acumulación de Polímeros Biodegradables (PHB) a
partir de Metano (bacterias metanotróficas)

17. Is there a Rational Method to
Purify Proteins?
from Expert Systems to
Proteomics
J.A. Asenjo
University of Chile

18. The Combinatorial Characteristic of Choosing the
Sequence of Operations for Protein Purification
Third
Stage
C1
C2
C3
C5
C6
n th
Stage
n1
n2
n3
n5
n6
Second
Stage
B1
B2
B3
B4
B5
B6
First
Stage
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

19. FactsRules
Knowledge base Working
memory
Knowledge
acquisition
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)

20. Determination of the Resolution Between Two Peaks
V2-V1
½(W1+W2)
RS =
SC α RS
η =
DF DF
SC α RS
V1
V2
W1 W2
Absorbance
Time

21. 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
PROPERTY
CONCENTRATION
MOLECULAR
WEIGHT
ISOELECTRIC
POINT
HYDROPHO-
BICITY
CONTAMINANT 5
......

22. 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.
2
Molecular weight was measured by SDS-PAGE with PhastGel media in Phast System.
3
Hydrophobicity 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.
4
Charge was measured by electrophoretic titration curve analysis with PhastGel IEF 3-9 in a Phast System.

23. Σ DFi
DFi
B
CA
S
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).

25. 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

26. Proteínas
Cuatro niveles de
estructura:
desde 1 dimensión
a 3 dimensiones
Desde análisis
estructural
a análisis funcional

27. Ingeniería de Proteínas

29. Ingeniería de Proteínas
• Low Temperature Proteases
(Cryophilic, Psycrophilic)
• for detergents
• for food applications
• for medical applications

30. Proteasa criofílica antártica

31. Proteasa criofílica antártica

32. Mutagénesis al azar (random)
Evolución dirigida
“Gene shuffling”

34. Ingeniería Metabólica y
Metabolómica

35. Metabolomics
Metabolic Flux
Analysis
GLUCGLUC
GLUC6PGLUC6P
FRUC6PFRUC6P
3PG3PG
GAPGAP
PIRPIR
PEPPEP
ACETACETEtOHEtOH
ACAC
RIBU5PRIBU5P
XIL5PXIL5PRIB5PRIB5P
GAPGAPSED7PSED7P
FRUC6PFRUC6P
aaaa
aaaa
aaaa
aaaa
aaaaaaaa
E4PE4P
CARBCARB
ATP ADPATP ADP
RNARNA
OO22
EE OO22
COCO22 COCO22
EE
υ2
υ3
υ5
LIPLIP
AcCoAAcCoAmitmit
AcCoAAcCoAcitcit
FUMFUM AKGAKG
SUCCoASUCCoASUCSUC
MALMAL ISOCITISOCIT
OACOAC
SODSOD
SODSOD
SODSOD
SODSOD
SODSOD
PROTPROTPROTPROT
PROTPROT
PROTPROT
PROTPROT
υ6
υ7
υ9
υ13
υ11
υ10
υ10
υ76
υ77
υ70-aaOAC
υ69
υ71-aaOAC
υ17
υ16
υ15
υ14
υ73-AcCoA
υ30
υ70-aaAKG
υ71-aaAKG
υ70-aaPIR
υPEP
υPIR
υ74
υ31
υ3P G
υ28
υ27
υ26
υE4P
υ19 υ20
υ21
υ22
υ23
υ18 υ1
υ25
υ71-aaPIR
υ70-aa3PG
υ71-aaPEP
υ70-aaPEP
υ71-aa3PG
υ71-aaE4P
υ70-aaE4P
υ70-aaRIB5P
υ71-aaRIB5P
υ72-nuOAC
υ72-nuRIB5P
υ72-nu3P G
NHNH44
EE NHNH44
υ78
LIPLIP
υ73-GAP
PROTPROTaaaa
RNARNA SODSOD
nunu
υOAC
nunu
υRI B5P
aaaa
υAc CoAcit
υ71-aaAcCoA
υ70-aaAcCoA
υAK G
RNARNA
nunu
GLICGLIC
AcCoAAcCoAcitcit
υ24
υ75
υ4
υ8
Gonzalez, R., Andrews, B.A. Molitor, J.
and Asenjo, J.A. (2003) Biotechnol.
Bioeng., 82, 152-169.

36. dX/dt = S v - bdX/dt = S v - b
in SS: S v = bin SS: S v = b oror S r = 0S r = 0  SScc rrcc + S+ Smm rrmm = 0= 0
Metabolic Flux AnalysisMetabolic Flux Analysis
Metabolic Flux BalanceMetabolic Flux Balance
AA
EE
BB
CC
DD FF
νν11
νν33
νν22
νν55
νν44
S r=0=S r=0=
1-0100D
01-010C
001-1-1B
54321 ννννν
5
4
3
2
1
ν
ν
ν
ν
ν
100D
010C
1-1-1B
321 ννν
3
2
1
ν
ν
ν
1-0D
01-C
00B
54 νν
5
4
ν
ν
+
SS StoichiometricStoichiometric MatrixMatrix
rr Rate (Flux) vectorRate (Flux) vector
cc CalculatedCalculated
mm MeasuredMeasured

37. P+ GLUC
GLUCGLUC
GLUC6PGLUC6P
FRUC6PFRUC6P
3PG3PG
GAPGAP
PIRPIR
PEPPEP
ACETACETEtOHEtOH
RIBU5PRIBU5P
XIL5PXIL5PRIB5PRIB5P
GAPGAPSED7PSED7P
FRUC6PFRUC6P
aaaa
aaaa
aaaa
aaaa
E4PE4P
CARBCARB
3.844
4.169
6.256
RNARNA
GLICGLIC
SODSOD
SODSOD
SODSOD
PROTPROT
PROTPROT
6.151
6.122
0.029
0.138
0.208
2.232
0.105
4.130 4.267
0.029
0.234 0.325
0.177
0.148
0.559 4.611
0.017
0.048
0.004
0.025
0.028
0.0040.025
0.006
0.0060.042
0.019
LIPLIP
0.002
PROTPROTaaaa
RNARNA SODSOD
nunu
nunu
0.057
0.177

38. P+ GLUC
GLUCGLUC
GLUC6PGLUC6P
FRUC6PFRUC6P
3PG3PG
GAPGAP
PIRPIR
PEPPEP
ACETACETEtOHEtOH
ACAC
RIBU5PRIBU5P
XIL5PXIL5PRIB5PRIB5P
GAPGAPSED7PSED7P
FRUC6PFRUC6P
aaaa
aaaa
aaaa
aaaa
aaaa
aaaa
E4PE4P
CARBCARB
ATP ADPATP ADP
RNARNA
OO22
EE OO22
COCO22 COCO22
EE
3.844
4.169
6.256
LIPLIP
AcCoAAcCoAmitmit
AcCoAAcCoAcitcit
FUMFUM AKGAKG
SUCCoASUCCoASUCSUC
MALMAL ISOCITISOCIT
OACOAC
RNARNA
GLICGLIC
SODSOD
SODSOD
SODSOD
SODSOD
SODSOD
PROTPROT
PROTPROT
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
0.0040.025
0.006
0.006
0.022
0.042
0.019
NHNH44
EE NHNH44
0.724
LIPLIP
0.002
PROTPROTaaaa
RNARNA SODSOD
nunu
nunu
0.174
nunu
0.057
aaaa
0.063
0.014
0.046
1.470
1.470
1.470
1.345
1.349
1.349
1.397
1.397
0.177
PIRPIR
PEPPEP
ACETACETEtOHEtOH
ACAC
aaaa
aaaa
aaaa
aaaa
aaaa
ATP ADPATP ADP
RNARNA
OO22
EE OO22
COCO22 COCO22
EE
LIPLIP
AcCoAAcCoAmitmit
AcCoAAcCoAcitcit
FUMFUM AKGAKG
SUCCoASUCCoASUCSUC
MALMAL ISOCITISOCIT
OACOAC
SODSOD
SODSOD
SODSOD
SODSOD
PROTPROT
PROTPROT
PROTPROT
PROTPROT
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
0.137
4.130 4.267
0.247
0.017
0.004
0.025
0.0040.025
0.022
NHNH44
EE NHNH44
0.724nunu
0.174
aaaa
0.063
0.014
0.046
1.470
1.470
1.470
1.345
1.349
1.349
1.397
1.397

39. 0
3
6
9
12
15
0 9 18 27 36 45
Time, h
Glucose,g/L
0.0
0.7
1.4
2.1
2.8
3.5
Cells,EthanolandSOD,g/L
Strain P+Strain P+ Strain PStrain P--
0
3
6
9
12
15
0 9 18 27 36 45
Time, h
Glucose,g/L
0.0
0.7
1.4
2.1
2.8
3.5
CellsandEthanol,g/L
0.0
0.3
0.6
0.9
1.2
1.5
0 9 18 27 36 45
Time, h
TotalProteinandCarbohydrates,g/L
0.00
0.05
0.10
0.15
0.20
0.25
TotalRNA,g/L
Strain P+Strain P+ Strain PStrain P--
0.0
0.3
0.6
0.9
1.2
1.5
0 9 18 27 36 45
Time, h
TotalProteinandCarbohydrates,g/L
0.00
0.05
0.10
0.15
0.20
0.25
TotalRNA,g/L

40. RATIO P-/P+ GLUC
GLUCGLUC
GLUC6PGLUC6P
FRUC6PFRUC6P
3PG3PG
GAPGAP
PYRPYR
PEPPEP
ACETACETEtOHEtOH
ACAC
RIBU5PRIBU5P
XIL5PXIL5PRIB5PRIB5P
GAPGAPSED7PSED7P
FRUC6PFRUC6P
aaaa
aaaa
aaaa
aaaa
aaaa
E4PE4P
CARBCARB
RNARNA
COCO22 COCO22
EE
0.92
0.99
1.23
LIPLIP
AcCoAAcCoAmitmit
AcCoAAcCoAcitcit
OACOAC
RNARNA
GLYCGLYC
PROTPROT
PROTPROT
PROTPROT
1.23
1.23
1.60
1.38
1.82(1.39)
4.49
3.34
1.82(1.46)
1.60
1.39
1.60
0.36
1.23
3.73
1.00 1.09
1.60
1.63 1.82
1.80
1.84
1.74 1.05
1.40
1.82(1.16)
1.82(1.60)
1.82(1.60)
1.82(0.96)
1.11
1.11
1.11
NHNH44
EE NHNH44
1.33
LIPLIP
4.49
PROTPROTaaaa
RNARNA
nunu
nunu
1.32
nunu
1.10
aaaa
1.46
1.82(1.41)
1.60
1.60
1.61
1.80

42. P+
Gluc/Eth

43. Discrete mathematical models
applied to genetic regulation of
metabolic networks

44. Microarrays
Metabolic
Flux
Analysis
Gene network
Metabolic network
Models
Traditional
technologies

45. Phenomena to model
Genetic and metabolic
adaptation of E. coli to different
nutrients
Substrates: Glucose, Glycerol
and Acetate
Glycolysis and TCA
8 possible substrate combinations  8
Phenotypes
Phenomena has been
described using Microarrays
(MA) and Metabolic Flux
Analysis (MFA)

46. Building of discrete
functions of
activation
0 Inactive
1 Active
1 / 2 / 3 Active
0 
1 / 2 / 3 
States
Signal = Biochemicals / Regulators
-1 / -2 / -3 
Metabolic Flux of Enzyme
-1 Inactive
Gene
Signal2 GeneSignal1
EnzComp B1 Enz1
Enz2 /
Signal2
Signal
Enz1 /
Signal1

47. Study of model
dynamics
67 nodes
28 genes
20 enzymes
19 regulators / biochemical
compounds
Ficticious Regulators
needed so model
reaches Phenotypes
Algorithm
Define combination of substrates
Generate105 aleatory vectors
Actualize in parallel way
Find atractor

48. Network is
mathematically
simple
Depends on Glucose, Glycerol
and Acetate
Regulators transmit information
It was necessary to use
Ficticious Regulators
The strongest: Joker1
Who suggest:
Similar regulation mechanisms
Regulation dependent on PTS

49. Cells for Cell Transplant
- neural cells (subst. nigra)
- stem cells
Vectors for Gene Therapy
-gutless adenovirus vectors

50. Terapia Génica
• Alcoholism
• Osteoporosis
• Parkinson
• Cancer (e. breast - gene BRCA-1)
• Arthritis
• Hemochromatosis
• Alzheimer

51. Vector de Primera Generarión
Vector de Tercera Generación o “gutless”

52. Reduction of Ethanol Intake
after 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
ETHANOLINTAKE(g/kg)
AdV-control
AdV-ALDH-AS

57. La Revolución de la
Biotecnología
y la Ingeniería
Juan A. Asenjo
Centro de Ingeniería Bioquímica y Biotecnología
Instituto de Dinámica Celular y Biotecnología (ICDB):
Un Centro para Biología de Sistemas
Universidad de Chile