1. Systems Biology (Biologa de Sistemas) y la Revolucin de la Biotecnologa Juan A. Asenjo Instituto de Dinmica Celular y Biotecnologa (ICDB): un Centro para Biologa de Sistemas Universidad de Chile 2. Edward Jenner (1749 1823): cowpox smallpox Vacuna viruela 1850 Luis Pasteur: Microorganismos: fermentacin no es espontnea 1928: Alejandro Flemming : Penicilina 1939: Florey, Chain purificacin de penicilina y produccin masiva USA-Pfizer Produccin de cido ctrico levadurasfermentacin Esterilizacin (descubri los microorganismos) (Enzimas) 1945: Premio Nobel: Flemming, Florey, Chain azcar levadura CO2 + H2O alcohol 3. Obtencin de Plasmidos 2.- Sacar plasmidio desde bacteria 1.- Se cuenta con bacterias que contienen plasmidos Cromosoma Plasmido Bacteria Plasmidos Poracin Produccin & Purificacin de Protenas 4. 60s - 70s Ingeniera Gentica 80s INSULINA: Ingeniera gentica de E.coli y S.cerevisiae Insulina comercial recombinante Hoy: Eli-Lilly Novo-Nordisk 90s: tpA Vacunas: Contra hepattis B (Merck, Chiron) Sida 1990 Sally y Dolly Terapia celular y gnica Enzimas crioflicas 5. Nueva Biologa Molecular Protenas Clonadas Ingeniera Gentica Enzimas de Restriccin Plasmidos Produccin & Purificacin de Protenas 6. Obtencin de Plasmidos 2.- Sacar plasmidio desde bacteria 1.- Se cuenta con bacterias que contienen plasmidos Cromosoma Plasmido Bacteria Plasmidos Poracin Produccin & Purificacin de Protenas 7. Principales pasos en la Clonacin de un Segmento de DNA Forneo Produccin & Purificacin de Protenas 1.- Obtencin del DNA forneo 2.- Corte con Enzimas de restriccin del plasmido Extremos cohesivos Extremos cohesivos Plasmido Corte Plasmido Cortado (Enzimas de Restriccin) Extremos cohesivos 8. 4.b.- Introduccin del plasmido Recombinante en clula huesped Permeasa 4.- Transformacin 4a.- Permeabilizacin de la clula mediante permeasa Produccin & Purificacin de Protenas 9. Systems Biology 10. We havent the money, so weve 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. Modelacin Matemtica y Optimizacin Biocombustibles: Bioetanol y Biodiesel Bioconversin de Celulosa a Metabolitos: Enzimas, Levadura Metabolmica: elucidacin de Vas Metablicas para acumulacin de Polmeros Biodegradables (PHB) a partir de Metano (bacterias metanotrficas) 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 (W1+W2) RS = SC RS = DF DF SC 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. 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). 24. Estructura de las Protenas Estructura Primaria: secuencia lineal de aa Estructura Secundaria: algunos aa interactuan Estructura Terciaria: cadenas de aa interligadas Estructura Nativa: protena se encuentra activa Protena denaturada: No tiene actividad No posee puentes dislfuro Produccin & Purificacin de Protenas 25. Protenas Cuatro niveles de estructura: desde 1 dimensin a 3 dimensiones Desde anlisis estructural a anlisis funcional 26. Ingeniera de Protenas 27. Ingeniera de Protenas Low Temperature Proteases (Cryophilic, Psycrophilic) for detergents for food applications for medical applications 28. Proteasa crioflica antrtica 29. Proteasa crioflica antrtica 30. Mutagnesis al azar (random) Evolucin dirigida Gene shuffling 31. Ingeniera Metablica y Metabolmica 32. 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 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 27 26 E4P 19 20 21 22 23 18 1 25 71-aaPIR 70-aa3PG 71-aaPEP 70-aaPEP 71-aa3PG 71-aaE4P 70-aaE4P 70-aaRIB5P 71-aaRIB5P 72-nuOAC 72-nuRIB5P 72-nu3P G NHNH44 EE NHNH44 78 LIPLIP 73-GAP PROTPROTaaaa RNARNA SODSOD nunu OAC nunu RI B5P aaaa Ac CoAcit 71-aaAcCoA 70-aaAcCoA AK G RNARNA nunu GLICGLIC AcCoAAcCoAcitcit 24 75 4 8 Gonzalez, R., Andrews, B.A. Molitor, J. and Asenjo, J.A. (2003) Biotechnol. Bioeng., 82, 152-169. 33. 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 11 33 22 55 44 S r=0=S r=0= 1-0100D 01-010C 001-1-1B 54321 5 4 3 2 1 100D 010C 1-1-1B 321 3 2 1 1-0D 01-C 00B 54 5 4 + SS StoichiometricStoichiometric MatrixMatrix rr Rate (Flux) vectorRate (Flux) vector cc CalculatedCalculated mm MeasuredMeasured 34. 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 35. 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 36. 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 37. 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 38. P+ Gluc/Eth 39. Discrete mathematical models applied to genetic regulation of metabolic networks 40. Microarrays Metabolic Flux Analysis Gene network Metabolic network Models Traditional technologies 41. 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) 42. 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 43. 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 44. 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 45. Cells for Cell Transplant - neural cells (subst. nigra) - stem cells Vectors for Gene Therapy -gutless adenovirus vectors 46. Terapia Gnica Alcoholism Osteoporosis Parkinson Cancer (e. breast - gene BRCA-1) Arthritis Hemochromatosis Alzheimer 47. Vector de Primera Generarin Vector de Tercera Generacin o gutless 48. 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 49. La Revolucin de la Biotecnologa y la Ingeniera Juan A. Asenjo Centro de Ingeniera Bioqumica y Biotecnologa Instituto de Dinmica Celular y Biotecnologa (ICDB): Un Centro para Biologa de Sistemas Universidad de Chile