Institute for Cell
     Dynamics and
Biotechnology: A Center
  for Systems Biology
Systems Biology
Systems Biology
                   Holistic Description of Cellular Functions


                                          ...
Goal of the Institute

• To conduct frontier research in cell function and
  dynamics and to develop models of important b...
Key Features of the Institute

• Development of novel approaches in the field of
  Systems Biology aimed at reaching origi...
Applied Research

• Development of enzymes with high activity at low temperatures

• Development of mammalian cell culture...
Key Associate Scientists

• Juan A. Asenjo       • Yedy Israel
  (Dir.)               • Carlos A. Jerez
• Barbara A. Andre...
Young Researchers/Postdocs

•   Paula Aracena        •   Pablo Moisset
•   Miguel Arredondo     •   Rodrigo Lecaros
•   Fr...
How

•   Multidisciplinary collaborations

•   Improved interdisciplinary training

•   Extensive international network wi...
Second Workshop
December 2008 at Marbella
We haven’t the money, so we’ve got to
               think

 Ernest Lord Rutherford, 1871 - 1937
Training and Interactions with
                         Industry

•   Enzymes
•   Biomining and bioremediation
•   Gene an...
Training and Interactions with
                         Industry

• Interactions with Industry in Chile and overseas
    ...
International Scientific Network

• Pedro Alzari (protein                   • Chris Lowe (protein purification and
    cry...
External Advisory Board

• Roger Kornberg, Nobel           • John E. Lisman, Volen Center
  Laureate, Stanford University ...
Institute for Cell
     Dynamics and
Biotechnology: A Center
  for Systems Biology
Metabolomics and Protein
      Engineering
Protein Engineering
Cold-
       Cold-Active enzymes from
              Antarctica

1. Trypsin-like Protease from Krill – US Patent
   granted...
Cryophilic Enzymes
• Protease with High Activity at low
  Temperature for Detergents

• 12% of the Market
    •          =...
Protein Engineering:

Random Mutagenesis

(Directed Evolution)

“error-prone” PCR
Activity vs. Assay
used for screening
Random Mutagenesis (directed
        evolution)

   Saturation Mutagenesis

       Gene Shuffling

   3-D Models (homology...
Increasing the Thermostability of a
             Xylanase using a Homology model

• Background
• Phsycrophilic xylanase, c...
Results of simulation of molecular
                                   dynamics




       8

       7

       6

       5
...
Selection of amino acids to mutate using
           a model of comparative compaction

• the program compares the density ...
Ser221
4,5
                                                     Mutant Ser221
                       4
                          ...
Effect of structural flexibility on the
                   cryophilicity of enzymes

• The aim is to identify elements rel...
Electrostatic
                 Compaction
 Interactions




Atomic            Hydrogen bond
Fluctuations      networks
Characterisation of mutant L52A
          160

          140

          120                                               ...
Metabolomics of Recombinant Yeast




• Metabolic Flux Analysis
• Microarrays of Gene Expression
• Integration of Gene Exp...
RNA                    SOD
                                                                       71 -aa RIB 5P
        ...
Metabolic Flux Analysis
                       Metabolic Flux Balance
                         dX/dt = S v - b
    in SS: ...
Strain P+                                                                                                                 ...
Microarrays of Gene Expression
      GeneChip from Affimetrix
    (6,871 genes of S. cerevisiae)
Conclusions


• (Glucose        Ethanol): It is CLEARLY not possible to correlate
  quantitative mRNA expression levels wi...
Viral Vectors for the Treatment of Alcoholism:
           use of Metabolic Flux Analysis for Cell
             Cultivation...
• Human Embryo Kidney (HEK) cells
• Adenovirus: vectors for gene therapy
• 26% of clinical trials
• Advantages : concentra...
Cell Growth MFA and MFA for virus synthesis


                                        GLUCOSE                             ...
Conclusions


• Using Fed-batch culture and medium with low glucose
  concentration (based on MFA to lower lactate) a high...
Mouse Embryonic Stem Cell
             Differentiation



Key steps in in vitro
embryonic stem cell
differentiation is
lar...
Conclusions


• Interesting correlations between metabolic fluxes
  and expression patters in the genes of the pyruvate to...
Study of model
dynamics

67 nodes
  28 genes
  21 enzymes
  18 regulators / biochemical
  compounds

Ficticious Regulators...
Different colours represent
different genetic regulation
mechanisms:
 Blue: Glucose repression
(gluconeogenic genes)
 Red:...
MFA of Bioleaching Microorganisms




• Acidithiobacillus ferrooxidans (62 reactions)

• Leptospirilum ferrooxidans

• Lep...
Leptospirilum ferrooxidans
     (82 reactions/equations)
Development of a novel biofilm model
                   for bioleaching

Objectives
• Understanding the kinetics of leachi...
Scheme of the proposed model

   O2, CO2                    Liquid     H2O          O2
 O2 and      SO42-   Bacteria
   CO...
Biochemical chalcopyrite leaching:
 comparison of low and high iron
   concentrations in bulk liquid

              Low ir...
Typical simulation of simultaneous
chalcopyrite leaching and microorganism
                 growth
                • Fe3+ ...
Main Conclusions

•   Embedded microorganisms are responsible of decreasing diffusion
    limitations in the solid layer b...
ICDB
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ICDB

  1. 1. Institute for Cell Dynamics and Biotechnology: A Center for Systems Biology
  2. 2. Systems Biology
  3. 3. Systems Biology Holistic Description of Cellular Functions Functional Analysis Inductive Metabolic Networks Regulatory Networks Bottom-Up Top-Down Connection Signalling Networks of "Modules" Modular Aggregation Deductive of Components Single Component Analysis Biological Information/Knowledge
  4. 4. Goal 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 • A multidisciplinary team of bioengineers, cell and molecular biologists, mathematicians, biochemists, chemists and computer scientists
  5. 5. Key Features of the Institute • Development of novel approaches in the field of Systems Biology aimed at reaching original solutions to traditional biological problems • Impact on important scientific problems (Basic Research) • Application of the know-how of the different groups of the Institute to the solution of problems important to society (Applied Research)
  6. 6. Applied Research • Development of enzymes with high activity at low temperatures • Development of mammalian cell culture for production of monoclonal antibodies and therapeutic proteins • Development of improved microorganisms for biomining • Development of methods for the mass production of cells for transplant and adenoviral vectors for gene therapy • Development of medications for the treatment of alcoholism and nicotine dependence • Development of fluorescent microbial sensors to monitor arsenic and other toxic heavy metals
  7. 7. Key Associate Scientists • Juan A. Asenjo • Yedy Israel (Dir.) • Carlos A. Jerez • Barbara A. Andrews • Marco T. Núñez • Juan Bacigalupo • Iván Rapaport • Bruce K. Cassels • Gonzalo Navarro • Carlos Conca • Christian González
  8. 8. Young Researchers/Postdocs • Paula Aracena • Pablo Moisset • Miguel Arredondo • Rodrigo Lecaros • Francisco Chávez • Álvaro Olivera-Nappa • Paulette Conget • Axel Osses • Miguel Reyes • Patricio Cumsille • Magdalena Sanhueza • Ricardo Delgado • Patricio Sáez • Gonzalo Encina • Julio Salazar • Angélica Fierro • Oriana Salazar • Ziomara Gerdtzen • Amalia Sapag • Nicolas Guiliani • Lorena Sülz • Patricio Iturriaga • Gerald Zapata • Eduardo Karahanian • Cristian Salgado • M. Elena Lienqueo • Fernando Ezquer • Casilda Mura • Javier Wolnitzky
  9. 9. How • Multidisciplinary collaborations • Improved interdisciplinary training • Extensive international network with state-of-the- art experimental facilities • During the second year the institute exceeded all its main objectives including the support and training of 71 Ph.D. students, postdocs and young scientists (58 the first year).
  10. 10. Second Workshop December 2008 at Marbella
  11. 11. We haven’t the money, so we’ve got to think Ernest Lord Rutherford, 1871 - 1937
  12. 12. Training and Interactions with Industry • Enzymes • Biomining and bioremediation • Gene and cancer therapy • Inhibition of iron uptake • Interactions with Industry in Chile and overseas  BiosChile  Ph.D. students carrying out their work together with company scientists (M. Salamanca, F. Reyes, A. Olivera-Nappa, M. Paz Merino)  Ph.D. students writing US patents (F. Reyes, J. P. Acevedo, L. Parra)  Collaboration of post-docs and young scientists (O. Salazar, A. Olivera-Nappa)
  13. 13. Training and Interactions with Industry • Interactions with Industry in Chile and overseas  Biosigma (CODELCO) Metabolomics, Biofilms  Mount Isa Mines Ltd. Biomining and  ESSAN S.A. bioremediation  Punta del Cobre S.A.  Grupo Bios Enzymes  Merck (Gene Therapy?)  Recalcine Gene Therapy • Training in US Biotech Companies  Chiron, Bayer, Genentech, Amgen • Ph.D. students working in industry  Avecia, IM2 (CODELCO), Diagnotec, Biosigma
  14. 14. International Scientific Network • Pedro Alzari (protein • Chris Lowe (protein purification and crystallography) affinity, high throughput methods) • Ioav Cabantchik (iron • Alan Mackay-Sim (stem cells) accumulation) • John Caprio (neuroscience) • David E. Nichols (medicinal chemistry) • Douglas Clark (protein engineering, enzymology) • Steve Oliver (yeast systems biology) • Caleb E. Finch (ageing) • Diego Restrepo • Peter Gray (mammalian cell culture) (chemotransduction) • Eckart D. Gundelfinger • Wolfgang Sand (biomining (neuroscience) mechanisms) • Vassily Hatzimanikatis • James Tiedje (environmental (systems and mathematical biology) microbiology) • Wei-Shou Hu (animal cell culture • Susan Wonnacott (nicotinic and mathematical models) receptors) • Donald F. Hunt (high throughput proteomics) • Jim Liao (modelling metabolism)
  15. 15. External Advisory Board • Roger Kornberg, Nobel • John E. Lisman, Volen Center Laureate, Stanford University for Complex Systems, Brandeis School of Medicine, USA University, USA • Douglas • Matthias Reuss, Systems Lauffenburger, Systems Biology, University of Stuttgart, Biology, MIT, USA Germany • F. Ivy Carroll, Director of • Terry Papoutsakis, Organic and Medicinal Chemistry, Research Triangle Department of Chemical and Institute, USA Biological Engineering, Northwestern University, USA • Angela Stevens, Mathematical Biology, University of Heidelberg, Germany
  16. 16. Institute for Cell Dynamics and Biotechnology: A Center for Systems Biology
  17. 17. Metabolomics and Protein Engineering
  18. 18. Protein Engineering
  19. 19. Cold- Cold-Active enzymes from Antarctica 1. Trypsin-like Protease from Krill – US Patent granted. Medical applications. 2. Subtilisin-like Protease fron Pseudomonas sp. – US Patent filed. Use in detergent industry. 3. Xylanase from Psychrobacter sp. - US Patent filed. Use in biofuels industry.
  20. 20. Cryophilic Enzymes • Protease with High Activity at low Temperature for Detergents • 12% of the Market • = 81.000.000 dollars
  21. 21. Protein Engineering: Random Mutagenesis (Directed Evolution) “error-prone” PCR
  22. 22. Activity vs. Assay used for screening
  23. 23. Random Mutagenesis (directed evolution) Saturation Mutagenesis Gene Shuffling 3-D Models (homology) Site-Directed Mutagenesis
  24. 24. Increasing the Thermostability of a Xylanase using a Homology model • Background • Phsycrophilic xylanase, complete sequence obtained, cloned and expressed in E. coli BL21(DE3)/pET22b(+). • Active at temperatures between 5ºC-40ºC, pH Optimum → 6 - 8 • Patent filed • Problem • Using directed evolution the Kcat was increased 3 times but there was no increase in thermostability. • Using a homolgy model of structure appropriate regions for mutations were found by simulation of molecular dynamics and degree of compaction.
  25. 25. Results of simulation of molecular dynamics 8 7 6 5 RMSD 4 3 2 1 0 Aminoácido RMSD: a measure of how much each amino acid can move
  26. 26. Selection of amino acids to mutate using a model of comparative compaction • the program compares the density of contact between equivalent residues in 2 groups of enzymes. • The density of contact is the number of atoms which can make contact with a residue. • Distance < 4,5 Å • Negative results indicate that the compaction in the cryophilic protein is smaller than in the mesophilic counterpart and these amino acids are therefore targets for mutagenesis. • The most promising target was SER221 as it is near to the active site and in a highly conserved region.
  27. 27. Ser221
  28. 28. 4,5 Mutant Ser221 4 25ºC 40ºC 3,5 Relative Absorbance 3 2,5 2 1,5 1 Mutation 0,5 S221T 0 0 50 100 150 200 250 300 350 400 Mutants Clones 98Å3 Cavity 86Å3 Reduction Ser Thr
  29. 29. Effect of structural flexibility on the cryophilicity of enzymes • The aim is to identify elements related to structural dynamics in enzyme molecules which could be responsible for their activity at low temperatures using algorithms to compare proteins with structural homology. • Model enzyme: Celulase from Bacillus agaradherans (Cel5A) • Comparison of structural and dynamic aspects • Electrostatic Interactions: salt bridges, hydrogen bonds • Compactation: density of contact • Average Atomic Fluctuations
  30. 30. Electrostatic Compaction Interactions Atomic Hydrogen bond Fluctuations networks
  31. 31. Characterisation of mutant L52A 160 140 120 Cel5A 100 80 L52A Activity 60 40 20 0 0 10 20 30 40 50 Temperatura Temperature 120 100 80 34,5 kD 60 40 20 0 1x 4x 8x 0 10 20 30 40 Time (min) - Tiempo (seg
  32. 32. Metabolomics of Recombinant Yeast • Metabolic Flux Analysis • Microarrays of Gene Expression • Integration of Gene Expression and Regulation with Metabolic Fluxes • Modelling Metabolic Fluxes and Gene Regulation
  33. 33. RNA SOD  71 -aa RIB 5P  72 -nuRIB5 P  70 -aa R IB 5P nu aa PRO T GLUC  RI B5 P RIBU5P  1 18  19  20  74 GLUC6P CARB RIB5P  21 XIL5P 2 FRUC6P LIP  23 SED7P GAP 4 3  22 GA P RNA Metabolomics FRUC6P  E4 P E4P  31 GAP 5  73 - nu  72 -nu3P G SOD  3P G aa GL IC 3PG aa  71 -aa 3 PG  70 -aa E 4P  71 -aa E 4P 6  70 -aa 3 PG PROT SOD PRO T  PEP  70 -aa PE P  27 PEP aa EtO H ACET  26 7  71 -aa PE P SOD  28 AC  PIR Metabolic Flux Analysis  73 -AcCoA  30 8 PIR aa  71 -aa PIR  70 -aa PIR LIP AcCoAcit 9 PROT  Ac CoAci t  71 -aa AcCo A 71 aa AcCoAmit SOD - aa O AC  75  78 Gonzalez, R., Andrews, B.A. Molitor, J.  70 -aa AcCo A OA C aa  25  10 NH4 E NH4  70 -a a  OA C and Asenjo, J.A. (2003) Biotechnol. PRO T OAC CO2  76 CO2 E  10 Bioeng., 82, 152-169. MAL  17 ISOCIT  72 -nuOA C AcCoAcit RNA nu  11  16  24 FUM AKG  AK G  69 PROT ATP ADP  13  70 -aa AKG  15 aa O2 E  77 O2  71 -aa A KG SOD SUC SUCCoA  14
  34. 34. Metabolic Flux Analysis Metabolic Flux Balance dX/dt = S v - b in SS: S v = b or S r = 0  S c rc + Sm rm = 0 S Stoichiometric Matrix  C E r Rate (Flux) vector   A B c Calculated  m Measured  D F 1  2  3  4  5 1 1  2  3 1 4 5 4 B 1 -1 -1 0 0 B 1 -1 -1 B 0 0 S r=0= C 0 1 0 -1 0 2 C 0 1 0 2 + C -1 0 5 D 0 0 1 0 -1 3 D 0 0 1 3 D 0 -1 4 5
  35. 35. Strain P+ Strain P- P- 15 3.5 15 3.5 12 2.8 12 2.8 Cells, Ethanol and SOD, g/L Cells and Ethanol, g/L Glucose, g/L Glucose, g/L 9 2.1 9 2.1 6 1.4 6 1.4 3 0.7 3 0.7 0 0.0 0 0.0 0 9 18 27 36 45 0 9 18 27 36 45 Time, h Time, h Strain P+ Strain P- P- 1.5 0.25 1.5 0.25 Total Protein and Carbohydrates, g/L Total Protein and Carbohydrates, g/L 1.2 0.20 Total RNA, g/L 1.2 0.20 0.9 0.15 Total RNA, g/L 0.9 0.15 0.6 0.10 0.6 0.10 0.3 0.05 0.3 0.05 0.0 0.00 0 9 18 27 36 45 0.0 0.00 Time, h 0 9 18 27 36 45 Time, h
  36. 36. Microarrays of Gene Expression GeneChip from Affimetrix (6,871 genes of S. cerevisiae)
  37. 37. Conclusions • (Glucose Ethanol): It is CLEARLY not possible to correlate quantitative mRNA expression levels with cell function shown by MFA • Comparing the P- (and P+) when Stat/Eth, underexpression generalized as biosynthetic machinery of the cell shuts down. • Comparing P+/P- on Ethanol, in P+ underexpression in many genes in central pathways indicating a decrease in respiratory metabolism compared to P-. • When growing on ethanol, the PPP and amino acid biosynthesis pathways show repression of genes important in the synthesis of glutamate, glutamine, proline and glycine. This is evidence that there will be less protein synthesis in P+ compared to P-.
  38. 38. Viral Vectors for the Treatment of Alcoholism: use of Metabolic Flux Analysis for Cell Cultivation and Vector Production • Ponga aquí su texto
  39. 39. • Human Embryo Kidney (HEK) cells • Adenovirus: vectors for gene therapy • 26% of clinical trials • Advantages : concentration, size of insert, infectivity • Design of culture medium based on cellular requeriments using MFA (minimize Lactate synthesis) • Design of culture medium based on MFA for synthesis of adenoviral vectors based on virus composition/stoichiometry
  40. 40. Cell Growth MFA and MFA for virus synthesis GLUCOSE GLUCOSE GLY SER GLY SER ser-pyr glc-biom glc-biom ser-pyr glc-pyr glc-pyr pyr-lac pyr-ala pyr-lac pyr-ala LACTATE PYRUVATE ALA LACTATE PYRUVATE ALA BIOMASS BIOMASS ASN ASP ASN ASP pyr-acc pyr-acc aa-acc LYS, ILE, aa-acc asp-oaa asp-oaa LYS, ILE, ACCoA ACCoA LEU, TYR LEU, TYR CoA gln-biom gln-biom CoA mal-pyr mal-pyr OAA aa-biom OAA aa-biom AA AA oaa-akg glu-akg gln-glu oaa-akg glu-akg gln-glu AKG GLU GLN AKG GLU GLN mal-oaa mal-oaa MAL MAL aa-TCA aa-ab aa-TCA akg-suc akg-suc Adv fum-mal aa-glu fum-mal aa-glu HIS, ARG, suc-fum SUCCoA HIS, ARG, FUM PRO suc-fum SUCCoA FUM PRO PYR, OAA, AKG, CO2 Flux aa-suc MAL, GLY, HIS, CO2 PYR, OAA, AKG, CO2 Flux tyr-fum PHE TYR MET, ILE, ARG, VAL, TYR, tyr-fum aa-suc MAL, GLY, HIS, CO2 THR, VAL LYS PHE TYR MET, ILE, ARG, VAL, TYR, THR, VAL LYS = - = - aa (total) aa (cons) aa (prod) DL/DG aa (total) aa (cons) aa (prod) L/G
  41. 41. Conclusions • Using Fed-batch culture and medium with low glucose concentration (based on MFA to lower lactate) a higher cell concentration is obtained as lactate accumulation is minimized. • Comparison of cells in suspension culture in low-glucose medium fed-batch vs. batch culture in original medium  DLac/DGluc similar • Specific growth rate similar • Maximum cell concentration 160% more • Specific glucose consumption rate 50% lower • Improved Medium for Adenovirus Production
  42. 42. Mouse Embryonic Stem Cell Differentiation Key steps in in vitro embryonic stem cell differentiation is largely unknown
  43. 43. Conclusions • Interesting correlations between metabolic fluxes and expression patters in the genes of the pyruvate to lactate reaction, notable differences between the different differentiation conditions (EB: embryoid body formation, GEL: gelatin, and MAT: matrigel). • A major event occurs between days 4 and 5 of differentiation identified by changes in both metabolic fluxes and gene expression profiles.
  44. 44. Study of model dynamics 67 nodes 28 genes 21 enzymes 18 regulators / biochemical compounds Ficticious Regulators needed so model reaches Phenotypes Algorithm Define combination of substrates Generate105 aleatory vectors Actualize in parallel way Find atractor
  45. 45. Different colours represent different genetic regulation mechanisms: Blue: Glucose repression (gluconeogenic genes) Red: Positive regulation (glycolytic genes) Green: Repression (shift from glucose to ethanol) - Glycolytic genes are mainly constitutive with few exceptions: eg. enolase2. - Other genes from Microarray data: (-) gluc to eth.: pyk1, pyk2, pdc1, pdc5, pda2, adh1 (x10). - Rec. strain genes: protein and recombinant protein: eg. pdc1 (- ), 1lv6, ilv2, glt1, aat1 (+), aat2. - PPP gene: zwf1 (-) in gluc.
  46. 46. MFA of Bioleaching Microorganisms • Acidithiobacillus ferrooxidans (62 reactions) • Leptospirilum ferrooxidans • Leptospirilum ferriphilum • Ferroplasma acidiphilum
  47. 47. Leptospirilum ferrooxidans (82 reactions/equations)
  48. 48. Development of a novel biofilm model for bioleaching Objectives • Understanding the kinetics of leaching and bioleaching • Finding theoretically optimal microorganism parameters able to successfully recover metals to obtain more efficient microorganisms. Modelling approach: non-homogeneous biofilms • Simultaneous space and time scales for biofilm formation and growth, chalcopyrite leaching and passivation and precipitation of insoluble matter • Possible existence of non-homogeneous cross-gradient diffusional limitation mechanisms • Obligated inclusion of inorganic precipitates • Presence of contact chemical reaction phenomena (sulfur leaching)
  49. 49. Scheme of the proposed model O2, CO2 Liquid H2O O2 O2 and SO42- Bacteria CO2 Fe3+ 2 diffusive H2O O2 Fe2+ Biofilm gradient 1 Fe2+ Fe3+ Sulfur deposits S0 3 S2- Mineral 1: Aerobic S0 oxidation 2: Aerobic Fe2+ oxidation 3: Chemical S2- oxidation (chalcopyrite leaching)
  50. 50. Biochemical chalcopyrite leaching: comparison of low and high iron concentrations in bulk liquid Low iron Large effect of microorganisms on copper recovery High iron Small effect of microorganisms on copper recovery
  51. 51. Typical simulation of simultaneous chalcopyrite leaching and microorganism growth • Fe3+ is more abundant beneath the biofilm, and iron diffusion to the mineral surface is hindered by thicker sulfur layers, decreasing the concentration of Fe3+ near the mineral surface and slowing down the leaching rate. • Corrosion-like pits are observed in the sulfur layer beneath the microorganism colonies (biofilm) at later stages.
  52. 52. Main Conclusions • Embedded microorganisms are responsible of decreasing diffusion limitations in the solid layer by increasing its porosity, forming corrosion pits • A flat layer of microorganisms on the mineral surface acts by accelerating sulfur dissolution over iron oxidation • A flat biofilm morphology can be favored by low iron and high oxygen conc. • This morphology guarantees maximum supply of energy simultaneously for all the cells (biofilm and planctonic cells). Most convenient symbiotic association between sulfur-oxidizing biofilm bacteria and iron-oxidizing planctonic cells • It provides an explanation of natural evolutive tendency of bioleaching bacteria to form flat biofilms

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