Ppt interactivo biologia de sistemas

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Ppt interactivo biologia de sistemas

  1. 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. 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. 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. 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. 5. Nueva Biología Molecular Proteínas “Clonadas” • Ingeniería Genética – Enzimas de Restricción – Plasmidos Producción & Purificación de Proteínas
  6. 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. 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. 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. 9. Systems Biology
  10. 10. We haven’t the money, so we’ve got to think Ernest Lord Rutherford, 1871 - 1937
  11. 11. Ogni parte ha inclinazione di ricongiungersi al suo tutto per fuggire dalla sua imperfezione Leonardo da Vinci (Cod.Atl, fol 59 recto)
  12. 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. 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. 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. 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. 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. 17. Is there a Rational Method to Purify Proteins? from Expert Systems to Proteomics J.A. Asenjo University of Chile
  18. 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. 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. 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. 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. 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. 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).
  24. 24. 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
  25. 25. Proteínas Cuatro niveles de estructura: desde 1 dimensión a 3 dimensiones Desde análisis estructural a análisis funcional
  26. 26. Ingeniería de Proteínas
  27. 27. Ingeniería de Proteínas • Low Temperature Proteases (Cryophilic, Psycrophilic) • for detergents • for food applications • for medical applications
  28. 28. Proteasa criofílica antártica
  29. 29. Proteasa criofílica antártica
  30. 30. Mutagénesis al azar (random) Evolución dirigida “Gene shuffling”
  31. 31. Ingeniería Metabólica y Metabolómica
  32. 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 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.
  33. 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 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
  34. 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. 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. 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. 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. 38. P+ Gluc/Eth
  39. 39. Discrete mathematical models applied to genetic regulation of metabolic networks
  40. 40. Microarrays Metabolic Flux Analysis Gene network Metabolic network Models Traditional technologies
  41. 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. 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. 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. 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. 45. Cells for Cell Transplant - neural cells (subst. nigra) - stem cells Vectors for Gene Therapy -gutless adenovirus vectors
  46. 46. Terapia Génica • Alcoholism • Osteoporosis • Parkinson • Cancer (e. breast - gene BRCA-1) • Arthritis • Hemochromatosis • Alzheimer
  47. 47. Vector de Primera Generarión Vector de Tercera Generación o “gutless”
  48. 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. 49. 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

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