1. Institute for Cell
Dynamics and
Biotechnology: A Center
for Systems Biology

4. Systems Biology

5. 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

6. 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

7. 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)

8. 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

9. 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

10. 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

11. 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).

12. Second Workshop
December 2008 at Marbella

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

14. 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)

15. 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

16. 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)

17. 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

18. Institute for Cell
Dynamics and
Biotechnology: A Center
for Systems Biology

19. Metabolomics and Protein
Engineering

20. Protein Engineering

22. 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.

23. Cryophilic Enzymes
• Protease with High Activity at low
Temperature for Detergents
• 12% of the Market
• = 81.000.000 dollars

24. Protein Engineering:
Random Mutagenesis
(Directed Evolution)
“error-prone” PCR

26. Activity vs. Assay
used for screening

27. Random Mutagenesis (directed
evolution)
Saturation Mutagenesis
Gene Shuffling
3-D Models (homology)
Site-Directed Mutagenesis

28. 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.

29. 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

30. 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.

31. Ser221

32. 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

33. 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

34. Electrostatic
Compaction
Interactions
Atomic Hydrogen bond
Fluctuations networks

35. 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

36. 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

37. 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

38. 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

39. 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

40. Microarrays of Gene Expression
GeneChip from Affimetrix
(6,871 genes of S. cerevisiae)

41. 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-.

42. Viral Vectors for the Treatment of Alcoholism:
use of Metabolic Flux Analysis for Cell
Cultivation and Vector Production
• Ponga aquí su texto

43. • 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

44. 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

45. 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

46. Mouse Embryonic Stem Cell
Differentiation
Key steps in in vitro
embryonic stem cell
differentiation is
largely unknown

49. 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.

51. 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

52. 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.

55. MFA of Bioleaching Microorganisms
• Acidithiobacillus ferrooxidans (62 reactions)
• Leptospirilum ferrooxidans
• Leptospirilum ferriphilum
• Ferroplasma acidiphilum

56. Leptospirilum ferrooxidans
(82 reactions/equations)

57. 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)

58. 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)

59. 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

60. 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.

61. 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