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Metabolic Engineering IMetabolic Engineering I
Bi7430 Molecular Biotechnology
R e c o m m e n d e d s o u r c e s f o r s t u d y
In t r o d u c t i o n t o t h e m e t a b o l i c e n g i n e e r i n g ( ME )
S u i t a b l e h o s t o r g a n i s m s f o r ME
G e n e r a l wo r k f l o w o f ME p r o j e c t
Ma t h e m a t i c a l m o d e l l i n g o f m e t a b o l i c p a t h wa ys
D i s c u s s i o n
Outline
Sources for study: books
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Sources for study: journals
Sources for study: journals
Greg Stephanopoulos (MIT) Jay D. Keasling (Berkeley)
James C. Liao (UCLA) Huimin Zhao (University of Illinois)
Sources for study: web
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IUBMB-Nicholson Metabolic Pathways Chart (Sigma-Aldrich): ATP metabolism in mitochondria and chloroplast.
catabolic
pathways:
biodegradation
anabolic
pathways:
biosynthesis
Bailey , J.E. (1991) Science, 252:1668
Introduction: a bit of history
“...the improvement of cellular activities by manipulations of enzymatic, transport, and regulatory
functions of the cell with the use of recombinant DNA technology.”
Introduction: definition of ME
ME i s the p racti ce o f o pti mizi ng g en eti c and regu lato ry p ro cesses
within cells to increase the cells’ production of a certain substance.1
(or degradation of certain substance)
These processe s are seri es of bi ochemi cal reaction s t hat allow cell s t o
conv ert raw materials into molecules necessary f or the cell’s surv iv al.
MEs math emati call y mod el t hese reacti ons, cal cul at e a yield of usef ul
product s, and determine t he con strain ts f or t he production of t hese
product s.
MEs use co mpu tatio n al and exp eri mental to ol s t o ov ercom e t hese
const rai nt s and est abli sh co st effecti ve p rocess. Maxi mu m yi el d of
desired subst ance must be balanced wit h t he nat ural surv iv al needs of
the cell.
1Yang, Y.T. (1998) Electronic Journal of Biotechnology, ISSN 07117-3458
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definition of the problem
solution of the problem
pathway
Boyle, P.M. (2012) Metabolic Engineering, 14:223
MULT IDISCIPLINARIT Y:
bioinf ormatics, microbiology, molecular biology, biochemist ry, genetics,
mathematics. .. + common sense (team work!!!)
COMPLEXIT Y:
knowledge of behav ior of the entire metabolic pathway(s) in the context
of living organism (system s biology, all “omics” techniques)
SUSTAINABILIT Y:
replacement of f osil f uels by utilization of renewable resources in
env ironmentally f riendly processes
biosynthesi s of v alue-added chemicals (drugs) and biof uels
biodegradatio n of toxic chemicals and bioremediati on of polluted sites
Introduction: characteristics
ME in context of recent world
global chemistry market estimated at 2,292 billion US$1
industrial biotechnology market estimated only 50 billion
US$ (does not include pharmaceutical biotechnol ogy and
biof uels)
great potential for growth in f ollowing decades (up to 20%
of global chemistry market could by cov ered by
biotechnol ogical product s by 2020)
1Ghisalba, O. (2010) Industrial Biotransformation. Encyclopedia of Industrial Biotechnology.
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Suitable host organisms for ME
DILEMMA TO BE SOLVED
microorgani sm s vs. multicellular organism s
prokaryot es vs. eukaryot es
heterotrophic vs. autotrophic organism s
plants vs. animals
in vivo vs. in vitro
What a hell...
PROS AND CONS
?
Algae
Beer , L.L. (2009) Curr.Opin.Biotech., 20:264
v ery promissing f or f uture
production of H2 , biof uels (lipids) and
biodegradable plastics (starch)
microalgae (Chlamydomonas reinhardtii,
Volvox carteri)
(+) high eff iciency in conv ersion of sun
energy, thriv e in salt water
(-) lack of genetic tools (random
mutagenesi s, miRNA), low productiv ity
Mammalian cells
http://www.cancer.cam.ac.uk/
60 – 70% of recombinant protein
biopharmaceuti cal s (MAbs - Herceptin)
approv ed cell lines: CHO, BHK, HEK-293, NS0,
PERC6 (f ed-batch)
ME f ocused on improv ement of product titers
(cell density) and quality
(+) posttranslational modif ications, human-like
system, metabolic div ersity
(-) high complexity and sensitiv ity (apoptosi s),
slow growth, cumulation of by-product s
(ammonia, lactate), nutrition requirement s
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Yeast
baker ’s yeast Saccharomyces cerevisiae, the
oldest and best known host f or biotechnology
(bread, wine, beer, ethanol)
f ood industry, biof uel producti on (f ermentation
product s ethanol and glycerol)
starch and cellulose utilization, production of
lactic acids, terpenoi ds etc.
(+) known genome (13 Mb), eukaryoti c microbe
(posttransl ational modif ., cheap cultiv ation),
secretion, number of genetic tools av ailable
(-) recombinant strains not accepted by public
(GMO in f ood), cumulation of by-product s
http://en.wikipedia.org/
Fungi
http://deferre.blogspot.cz/
used f or thousands of years in traditional
biotechnol ogies (koji f ungi in Japan)
Aspergillus niger (citric acid), A. oryzae (αAmylase),
A. terreus (statins)
penicillin, heterologous enzymes, conv ersion
of biomass to commodity chemicals (outlook)
(+) low pH tolerance (potential f or production
of organic acids), metabolic div ersity
(-) lack of genetic tools, f ormation of byproduct
s, complex protein processing
Streptomyces
G+ bacteria with mycelial habit
producers of antibiotics (50 %), anticancerous
agents (bleomycin), enzymes (degradation of
cellulose)
(+) non-pathogenic, rich in secondary
metabolites, protein excretion, expression of
genes with high GC content, cheap cultiv ation,
degradors, decomposers
(-) mycelial growt h limits mass transf er, lack of
genetic tools (random mutagenesi s and
screeni ng = not GMO), large genomes with many
regulatory proteins
http://streptomyces.nih.go.jp/
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Bacillus subtilis
G+ bacterial model, sporulation
production of antibiotics, v itamins (H,B1, B2,
B5, B6), f ood enzymes (50% of world market,
serine proteases)
(+) non-pathogenic, known genome (4.2 Mb),
established genetic tools, secretion of
enzymes, experience with large-scale
f ermentations
(-) high complexity of the secretion processes,
no endogenous plasmids and instability of
recombinant ones – chromosom al expressi on
preff ered
http://www.astrobio.net/
Pseudomonas putida
G-, P. putida KT2440, saprophytic soil
bacterium
best characterized Pseudomonas, model
organism f or bioremediation applications and
constructi on of bacterial chassi s
TOL plasmid - natural ability to degrade
solv ents (toluene, xylene), design of pathways
f or biodegradati on of organic pollutant s
(+) non-pathogenic, known genome (6.18 Mb),
metabolic v ersatility
(-) so f ar less popular bacterial model than E.
coli
http://www. bacmap.wishartlab.com
Escherichia coli
G-, (E. coli K-12) most important bacterial
model organism (“workhorse”)
(+) rapid growt h on simple synthetic media,
f ast doubling time (20-30 min), high cell
densities, high product content (up to 30% of
dry cell mass) well known genome (4.6 Mb),
div erse genetic tools, dev eloped cultiv ation
strategi es, industrial (and public) acceptance
(-) no glycosilation (and some other pt.
modif ications), acetate production at high
glucose f luxes, limited protein secretion,
thorough aeration needed
http://www.telegraph.co.uk/
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E. coli..a bit of history
Smolke, C.D. (2009) The Metabolic Engineering Handbook: Fundamentals
discovery of plasmids
discovery of restriction enzymes and ligases
first recombinant DNA
commercial production of insulin (Genentech)
commercial production of AA (Thr, Phe, Trp), by ME
commercial production of 1,3-propanediol (Genencor,
Du Pont), by ME
Smolke, C.D. (2009) The Metabolic Engineering Handbook: Fundamentals
E. coli: biosynthetic potential
+ heterologous
expression
In vitro ME
cell-f ree system s, promissing area in
ME and syntheti c biology
work with CFE or purif ied component s
in vivo systems are too complex (1,3propanediol
production in E. coli, 15
years, 575 scientist s)
(+) lower complexity, better def ined
system s, no membranes (f aster
transport), higher f lexibility, no toxicity
problems, no GMO
(-) cells needed anyway, lower enzymes’
concentrati ons than in cell, recycling?
Hodgman, C.E. (2011) Metabolic Engineering, 14:261
crude extracts
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Dhamankar , H. (2011) Curr.Opin.Struct.Biol., 21:1
General workflow of ME project
and Properties
Pathway design and selection
What is the main goal of the project?
ME can be applied to:
improve the yield and productiv ity of nativ e product s synthesized by
organism s
extend the range of subst rat es or improv e the uptake of subst rat e
(including biodegradation)
establish production of product s that are new to the host cell
Pathway design and selection
Identification of pathway of interest
Databases of anabolic and catabolic pathways
biosynthesi s: MetaCyc, KEGG
biodegradati on: UM-BBD Pathway Prediction System (183 pathways)
Literature search
NCBI Pubmed database, W eb of Science (W OS)
Visualizati on of metabolic networks
Cytoscape, GraphViz, Systrip
Visualizati on of reaction networks
CellDesigner, GLAMM
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http://metacyc.org/ http://umbbd.ethz.ch/predict/
Our model pathway
Synthetic pathway for biodegradation of 1,2,3-trichloropropane (TCP)
Dhamankar , H. (2011) Curr.Opin.Struct.Biol., 21:1
General workflow of ME project
and Properties
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Pathway enzyme properties
Enzyme databases:
BRENDA, BioCyc, KEGG
Enzyme properties:
BRENDA, ExPASy serv er
Searching for sequence homology:
BLAST (NCBI) – basic local alignment
Enzyme structure and its visualizatio n
Protein Data Bank (PDB), PyMOL
+ physical/chemical properties
of metabolites
kcat/Km kcat/Km
kcat/Km
kcat/Kmkcat/Km
Dhamankar , H. (2011) Curr.Opin.Struct.Biol., 21:1
General workflow of ME project
computational
and
experimental
tools
and Properties
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Pathway optimization
Original “historic” approach:
combination of random mutagenesi s with exhaustiv e screening of
candidates with improv ed production of desired compound
chemical (e.g. ethidium bromide) and physical (UV irradiation)
mutagens, “mutator strains” (Epicurian coli XL1-Red)
(+) success achiev ed in production of AA, antibiotics, v itamins.
(-) toxic agents, resulting v ariants with more undef ined mutations,
demanding screening
Pathway optimization
Recent strategi es try to be more rational and focused.
Underst andi ng of cellular metabolism requires the knowledge of :
1) The topology of the metabolic network (where - compartmen ts)
2) The concentration s of metabolites (what and how much - species)
3) The flow of metabolites through pathways (how fast – fluxes or
kinetics of enzymes)
4) Ov erall context of the liv ing cell (“-omics” techniques)
Study of 1, 2, 3 and 4 requires diff erent theoretical and experimental
methods and tools.
compartment
reaction species
fluxes or kinetics
whole-cell environment (studied by “-omics” techniques)
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FLUX is the reaction rate connecting two metabolites (A→B) or
rate at which materi al is processed through the whole pathway,
with unit [mol.dm-3 .s-1 ] .1
Fluxes are calculated in steady-state, DO NOT describe dynamics
of enzymatic reactions (kinetic parameters of enzymes needed)
Along with intracellular metabolite concentrati on s, f luxes def ine the
minimal inf ormation needed f or describing metabolism and cell
physiology.
Pathway flux
1Stephanopoulos , G. (1999) Metabolic Engineering, 1:1
PIPELINE
S A B C P
Metabolomics
Metabolomics completes the inf ormation f rom f lux analysis with
inf ormation about concentration s of metabolites.
METABOLOME is a quantitativ e set of all molecules present in the cell
at certain time.
Experimental analytical techniques: GC-MS, LC-MS, CE-MS, NMR.
Meyer, A. (2007) Current Opinion in Microbiology 10:246
Mathematical models of MPs
Mathematical models of metabolic pathways play a central role in
metabolic engineering.
Models help to identify reaction s which need to be modified to
improv e the perf ormance of the pathway.
Once such reaction(s) is (are) identif ied, experimen tal techniques
are applied in order to target correspond ing gene(s) or regulatory
mechanisms.
T here are three types of modelling of metabolism
1) Flux Balance Analysis (chemost at )
2) Metabolic Flux Analysis (chemost at)
3) Kinetic modelling (real conditions)
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Kinetic modelling
Kinetic models employ kinetic parameters of pathway enzymes to
describe dynamic behavior of metabolic pathway.
Parameters: concentrations of enzymes, Km , kcat or Vm ax, Ki, Kd ,
(BRENDA database).
Kinetic equations: e.g. Michaelis-Ment en mechanism
Application for simulation of the pathway reaction courses and
prediction of pathway behav iour
Computing tools: E-Cell, COPASI, CellDesigner, Scientist, Matlab
(+) the most realistic, dynamic descripti on of the system.
(-) missing kinetic data f or majority of enzymes, kinetic parameters
of ten measured in vitro in optimal (not physiological) conditions
Kinetic modelling: example
Kinetic modelling: example
Modelling of TCP pathway.
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Summary
KEY POINTs of ME project
selection of suitable host organism
thorough selection/design of the pathway
maximal knowledge of basic building blocks
(enzymes and metabolites) of target MP
mathematical modelling and metabolomics play a key role in
ME for definition of problematic reactions
genetic tools are used for solution of the problem (Lecture 2)
Discussion
AppendicesAppendices
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under continuous f eed of A, the
f luxes become stationary af ter
some time
MEs are interested in steadystate
fluxes (disadv antage)
in steady-st at e d[A]/dt = 0
A B
vin voutv1
v2CA
flux
CA
transient state
http://www.boomer.org/ (modified)
Pathway flux
SBML (Systems Biology Markup Language)
Standard (common) language used f or sharing mathematical models
describing metabolic networks (+ other biological networks, system s
and processes).
Structure of SMBL model
list of compartmen ts (in vivo v s. in vitro)
list of species inv olv ed in reaction
list of reactions: f or each reaction
- list of reactan ts
- list of products
- kinetic law (list of parameters)
Mathematical models: SBML
Mathematical models: SBML
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Used f or calculation of intracellular fluxes. Employ inf ormation about
intracelullar concentration s of metabolites (1 3 C labelling).
Results in proposal s f or deletion (knock-out s) or overexpression of
certain gene(s) coding f or pathway enzymes.
(+) can deal with genome-scal e metabolic networks, does not require
the knowledge of enzyme kinetics
(-) f luxes represent only static approximati on of dynamic and complex
reality in liv ing cell at certain conditions set up by experimenter.
Reliable in vivo metabolic models are still rare.
FBA and MFA
Flux Balance Analysis
FBA is completely theoretical concept.1
It is a direct application of linear programming to biological system s that
uses the stoichiometri c coefficients f or each reaction in the system as
the set of constrain ts f or the optimization. It simply requires that the
total f lux of any compound in the system is 0.
computation al tools: CellDesigner, MetNetMaker, COBRA toolbox
(requires MATLAB), models in SBML f ormat
1 see Wikipedia for detailed description
Becker, S.A. (2007) Nature protocols doi:10.1038/nprot.2007.99
An example of stoichiometric matrix for a network representing the top of
glycolysis prepared for FBA.
Becker, S.A. (2007) Nature protocols doi:10.1038/nprot.2007.99
Flux Balance Analysis
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The results of FBA can be represented identically as a vector of fluxes, or by weighting the
lines representing the reactions according to the flux they carry.
http://en.wikipedia.org/
Flux Balance Analysis
Wiechert, W. (2001) Metabolic Engineering 3:195
Tang, Y.J. (2009) Mass Spectrometry Reviews 28:362
Metabolic Flux Analysis
13C MFA workflow