Transcript Canadian Bioinformatics Workshops www.bioinformatics.ca Day 1, Section 3

Canadian Bioinformatics Workshops
www.bioinformatics.ca
Day 1, Section 3
Day 1, Section 3
Constraint-based Models of
Metabolism
R. Mahadevan,
Assistant Professor, Department of Chemical
Engineering and Applied Chemistry, Institute
of Biomaterials and Biomedical Engineering
University of Toronto
Day 1, Section 3
Outline
• Introduction/ Motivation for Modeling
• Constraint-based Modeling
• Applications of CBM
Day 1, Section 3
Driving Forces for in silico Models
High Throughput Data
Cellular Complexity
Data volume & cellular
complexity demand
formulation of in silico models
HT technologies enable us
to study cells as systems
HT technologies have forced
our view of cells as systems
Systems Biology
Day 1, Section 3
Systems Biology Approach
•
“study of how the parts work together to form a functioning biological system”
(Church & coworkers, 2003)
Determine
system
components
(genes, protein,
metabolites)
Identify
component
interactions
(enzyme-substrate,
protein-DNA)
Develop
system model
(qualitative,
quantitative)
Model-based
design,
Hypothesisdriven
discovery
System Biology Process
Experimental Tools
Computational Tools
High-throughput Techniques:
Sequencing, Omics, Interaction data
Bioinformatics:
Genome annotation, Motif finding
Phenotypic studies:
Physiology, Deletion analysis,
Environmental perturbation
Network reconstruction:
Data mining, Probability theory
Instrumentation:
Novel cellular sensors, manipulators
Day 1, Section 3
Iterative modeling:
Dynamics, Optimization, Model
validation
System Biology Tools
700
"Systems Biology"
"Systems Biology" and "Model"
600
"Systems Biology" and "Experiment/Experimental"
"Systems Biology" and "Experiment/Experimental" and "Model"
No. of Articles
500
400
300
200
100
0
1997
1998
1999
2000
2001 2002 2003
Publication Year
2004
2005
Growth in Systems Biology Research
Day 1, Section 3
2006
2007
Era of In silico models
• Over 353 micro-organisms sequenced (TIGR’s
CMR database)
• Genome sequence alone has provided limited
information on the phenotype (e.g., genes for
O2 utilization, glucose uptake )
• Abundance of this sequence information
motivates the in silico modeling era
• Genome-scale models available for a variety of
microbes relevant to biotech, pharma industry
Day 1, Section 3
Applications of Models
• Discovery through combined computation
and experimentation
– Functional genomics
– Integrative data analysis
– Physiology Analysis (Hypothesis generation)
• Design
– Metabolic Engineering
– Protein Production
– Bioprocess Optimization
Day 1, Section 3
Methods to Interrogate Models
Price, et al
Nat Rev
Microbiol.
Oct 2004
Sophisticated
methods to
evaluate
systemic
properties for
Discovery and
Design based on
the Network
Day 1, Section 3
2D Annotation: Networks
Metabolic
Regulatory
Signaling
1) Network reconstruction first
step in the analysis of network
function
2) All network interactions are
biochemical interactions:
covalent bonds or weak
interactions
Protein-Protein
Day 1, Section 3
Protein-DNA
Network Interactions
• Interactions have specific properties
– Chemical Interactions (Covalent Bonds): e.g
metabolic networks
•
•
•
•
Stoichiometry (mostly invariant)
Kinetic rates (condition dependent)
Thermodynamics (determines directionality)
Hard links (easier to determine)
– Physical Interactions (Hydrogen bonding):
protein-protein networks
• Soft links that can form readily
• Can be condition dependent and difficult to reconstruct
with confidence
Day 1, Section 3
Network Interactions: Continued
• Estimate of Network Components Possible
(e.g. no. of genes, proteins, metabolites (?))
• Interactions can be combinatorial
– e.g interaction network between 1000 proteins
has close to half a million elements ! (1e6-1000)/2
[(n2-n)/2 interactions for n proteins ?]
– Not all interactions occur (Selection guided by
evolution to accomplish spec. mechanisms!)
– Required for complexity in phenotypes (~30,000
genes in humans ?)
Day 1, Section 3
Constraints in Biology
• Physico-chemical constraints
– Enzyme capacity (Vmax)
– Conservation (mass, energy, charge )
• Environment
– Substrate availability (donor/acceptors)
– pH, temperature, pressure etc...
• Regulatory constraints
– Availability of proteins
• Spatial constraints
– DNA arrangement
– Local concentrations
Day 1, Section 3
Biological Networks: Summary
• Complex networks (with biochemical and
physical interactions) characterized by
uncertainty
• Combinatorial interactions that can vary in
time
• Even biological networks have to satisfy
specific constraints
• Networks can be modular with specific
functional attributes
Day 1, Section 3
2D Annotation: Networks
Metabolic
Regulatory
Signaling
Focus on
Metabolic
Networks due to
ease of their
Reconstruction
Protein-Protein
Day 1, Section 3
Protein-DNA
Model-Building Process
(e.g.,Metabolism)
Determine
network
components
Define links
between
components
Quantitative
calculations for
modeling
Day 1, Section 3
Process of Metabolic Reconstruction
(Covert et al., 2001)
Selecting Pathway for Construction
Identifying all genes in genome for
selected pathways
gene/transcript
polypeptide
Reviewing sequence annotation
(focused BLAST searching in-house)
protein
reaction
Cross Validation to external references
(NCBI, CMR and JGI)
Associating genes-proteins-reactions
Complete Network Reconstruction
Day 1, Section 3
Constraint-based Modeling of
Metabolism: Parameters
•
ATP maintenance parameters
–
–
•
Biomass composition
–
•
Growth and non-growth associated ATP terms
Calculated by regression with experimental data
Determined from data
Metabolite transport rates
–
–
Limiting substrate uptake rates (inputs to the model)
Obtained from measurements
Day 1, Section 3
Determining ATP Maintenance
Parameters
Experimental data
Experimental data
Growth Associated
Maintenance
Substrate
Uptake Rate
Growth Yield
(Biomass/Substrate)
Substrate
Requirement for
Non-Growth
Maintenance
Growth Rate
•
•
•
Growth Rate
Determine the maintenance parameters based on data
Calculate from biomass protein composition, ATP required for protein synthesis
and other cellular process
Incorporate in the biomass reaction
Day 1, Section 3
Representing Biomass Composition in
the Model
Nutrients
Uptake Rates
Carbohydrates
Acetate, Fumarate,
Ammonium, etc.
Lipids
Nutrients
Carbohydrates
Lipids
Energy
Purines
Energy
Purines
Amino Acids
Amino Acids
Biomass
Constraints imposed on the
metabolic
Protein network
content
Optimization:
%w biomass
Maximize Z   ci vi  c.v
i Asp, Arg, Cys, Gln, Glu, Gly, His,
Asn,
Subject
46% to Ala,
S .v  0,  j  v j   j
Reaction
Ile, Leu, Lys, Met, Pro, Phe, Ser, Thr, Trp, Tyr, Val
RNA content
10%
DNA content
4%
ATP, GTP, CTP, UTP
dATP, dGTP, dCTP, dTTP
Carbohydrate content
15 %
glycogen
Lipid content
15%
stearate, myristate, palmitate, iso-C 14,C15,C16,C17
Other
10%
Day 1, Section 3
UDP-N-acetyl-D-glucosamine, UDP-Nacetylmuramate, Alanine, Glutamate, etc.
Mathematical Representation of a
Biochemical Network
Stoichiometric Matrix
reaction
metabolite
S=
-1
0
-1
0
-1
0
0
0
1
-1
0
0
0
-1
0
0
0
1
1
-1
0
0
-1
0
0
0
0
1
0
0
0
-1
0
0
0
0
-1
0
0
0
1
0
0
0
1
0
0
0
S matrix: concise and systematic representation of the all the reactions, metabolites and their
interactions
Day 1, Section 3
Obtaining the Stoichiometric Matrix
(S)
• S obtained from the list of reactions reconstructed from the
combination of genomics, biochemistry and physiology
chemical reaction:
Reaction list
aA + cC
vi
eE + hH
compounds
vi
vi: reaction
a,b,e,h: stoichiometric coefficients (invariant)
Section 3
A,C, E,Day
H:1, compounds/metabolites
A
-a
B
0
C
-c
D
0
E
+e
F
0
G
0
H
+h
Representation as a
column in a matrix:
Representation of the 2D
Annotation: Metabolic Networks
• Mathematical representation of the components and their
interactions required for calculations
• Define component concentration vector
– x = [x1,x2,…,xm]
m: number of metabolites
• Define reaction flux vector
– v = [v1,v2,…,vn]
n: number of reactions
• Typically, m<n for metabolic networks
• Underdetermined systems with several degrees of freedom
Day 1, Section 3
S links metabolites and reactions
S
ST
S describes the interactions
among metabolites
(substrates, products)
Columns describe the
reaction stoichiometry
aA + cC
vi
eE + hH
Rows define connectivity
Day 1, Section 3
Sparsity
S is inherently sparse
Metabolites
Example:
Geobacter
sulfurreducens S has
2655 nonzero out of
3e5 elements (.8 %)
Some highly
connected
metabolites present
Day 1, Section 3
Reactions
Dynamic Mass Balance
• Mass balance around a metabolite, say pyruvate
 in
 out
pyr
Rate of accumulation of pyr = Production rate- Consumption rate
dx pyr
dt
• Generalizing
 vin  vout  Sv
v 
S  1  1 v   in 
vout 
dx
 Sv
dt
• S matrix transforms the reaction flux vector v = [v1,v2,…,vn] to
the time derivatives of concentration
Day 1, Section 3
Calculating Phenotypes Using a
Constraint-based Approach
Nutrients
Nutrients
Carbohydrates
Carbohydrates
Lipids
Lipids
Energy
Purines
Energy
Purines
Amino Acids
Constraints imposed on the
metabolic network
Amino Acids
Growth /Biomass
Optimization:
Composition
Maximize Z   ci vi  c.v
Subject to S .v  i0,  j  v j   j Physicochemical
Constraints
Convexity
Day 1, Section 3
Objective Function
• Typical objective function is growth rate
maximization
– Uptake rates are also required and are usually
specified
– Therefore, maximal yield solution is found
• However, this holds true only under optimal
condition
• Several conditions where this objective
function fails
– No clear alternative proposed
Day 1, Section 3
Cellular Objective of Optimality
Post-Adaptive
Evolution
Ibarra et al., 2001
Edwards et al., 2002
Day 1, Section 3
Evolution of deletion
mutants
(Fong & Palsson, 2004)
•
Knockout strains initially exhibit poor
growth
•
Eventually evolve to predicted growth
rates
Day 1, Section 3
Robustness Analysis to Gene
Deletions and Enzyme Defects
Biological Significance:
The impairment of an enzyme can
have a system wide effect and affect
the optimal growth rate achievable by
an organism.
Example:
Fluxes in E. coli have been analyzed
to study how a continuous impairment
of the enzyme will affect the predicted
optimal growth rate.
References
Edwards, J.S., and Palsson, B.Ø., "Robustness
Analysis of the Escherichia coli Metabolic Network",
Biotechnol Prog., 16: 927-939, (2000).
Mathematics
Oxygen Uptake Rate
Phenotypic Phase Planes
2.4
0.4
Phase Plane
Isoclines
Carbon Uptake Rate
Key References
Biological Significance: Can determine what
the optimal nutrient uptake rates to allow for
maximal biomass production (Line of
Optimality) and what uptake rates are not
feasible.
Mathematics: Shadow prices from the dual
solution are calculated for different uptake
rates. Shadow prices are constant within a
region, changes in shadow prices delineate
the different regions.
Edwards, J.S., Ibarra, R.U., and Palsson, B.Ø., "In silico predictions of Escherichi coli metabolic
capabilities are consistent with experimental data", Nature Biotechnology 19: 125-130(2001).
Edwards, J.S., Ramakrishna R., Palsson, B.Ø., “Characterizing the metabolic phenotype: A
phenotype phase plane analysis",Biotechnology and Bioengineering, 77(1): pp. 27-36 (2002).
Schilling,C.H., Edwards, J.S., Letscher, D.L., and Palsson, B.Ø., "Combining pathway analysis
with flux balance analysis for the comprehensive study of metabolic systems", Biotechnology
and Bioengineering 71: 286-306 (2001).
Ibarra, R.U., Edwards, J.S., and Palsson, B.Ø.; "Escherichia coli K-12 undergoes adaptive
evolution to achieve in silico predicted optimal growth," Nature, 420: pp. 186-189 (2002).
Constraint-based Modeling Approach:
Issues
• Linear programming with growth rate
maximization generates flux distribution (Max vgro
s.t. Sv = 0)
•
Need to be aware that multiple optimal flux
distributions can exist for the same
environmental conditions
•
Alternate Optima (Degenerate solutions) can
be present
•
Same objective function value, different
solution (flux distribution)
v2
Increasing
Objective
0.78 hr-1
Day 1, Section 3
v1
Alternate
Optima
Growth Rate
Flux Variability Analysis
(Mahadevan and Schilling, 2003, Metabolic Engineering)
• MILP based algorithm exists to identify all alternate optimal
solutions (Lee et al., 2000)
– Can be intractable at the genome scale
– Need a computationally efficient and practical approach
• Flux Variability Analysis
– Objective function value obtained in the first trial incorporated as
additional equality constraint
– Maximization and Minimization of individual fluxes specified as the
objective function
• LP solved for all the minimization and maximization of all fluxes
Case 1
Max vi
Case 2
Min vi
gro
s.t Sv  0  gro   Opt
gro
s.t Sv  0  gro   Opt
Day 1, Section 3
Alternate Optimal Flux Distribution
Characterization
v2
v2
v2max
v2max
min
v2min
v2
v1min
v1max
Different Maximum and
Minimum: Alternate Optima
v1min v1max
v1
v1
Maximum and Minimum are
equal: Unique solution
Optimal
region
Solution Characteristics:
1) Efficient algorithm (2n LPs required)
2) Distinct optima from 2n solutions
3) Subset of alternate optima identified
4) Identifies flux range of all reactions
Day 1, Section 3
Range
Condition Dependent Flux Variation
Growth on Glucose, Acetate, and Lactate
Glucose
Acetate
Lactate
Range of Variation
0.25
0
4
D
RD 4
N A
RD 1
N DD
R 1
N A
RD
N K9R
D
N K6R
D
N K2R
D 3
N DD
R
N E
RD 3
N A
RD 2
N D
RD 2
N DA
R
N K7R
D
N K5R
D
N K3R
D
N K2R
D
A K1R
D PD
A M
D
H
D TT
U
M PA
T
N
A
PP xtO
2
CO xtO
R R
FO T X
2 R
CO UP
R
FO F1
H
FD E 2
V
IL A1
T
V
A BR
LA
A
Fluxes
• Reactions associated with formate secretion show variation for growth on
glucose
• Range of flux variation condition dependent
Day 1, Section 3
Equivalent Reaction Sets
• Alternate optimal solutions due to the presence of
equivalent reaction sets
B
B
A
Loop
C
A
C
B
A
C
Equivalent
Reaction Sets
• Modified extreme pathway algorithm to determine all
equivalent reaction sets for growth on glucose
Day 1, Section 3
Equivalent Reaction Set Example for
Growth on Glucose
•50 equivalent reaction sets identified
•Equivalent reaction sets primarily in the
nucleotide metabolism, amino acid
metabolism
•Example in Ribonucleotide reductase
reactions
–Set of 4 reactions stoichiometrically
equivalent to the single reaction
–Net reaction: gtp+ trdrd -> dgtp +trdox
•Condition dependent realization of
equivalent reaction sets leads to alternate
optima
Day 1, Section 3
Application to Physiology
Analysis of Geobacter Metabolism
Day 1, Section 3
Geobacter sulfurreducens
•
G. sulfurreducens
– Important member of the dissimilatory metal
reducing bacteria (Geobacteraceae) (Caccavo et
al., 1994)
– Strictly anaerobic bacteria
– Grows with acetate as the electron donor and
metals (Fe), fumarate as electron acceptors
– Metabolism not well characterized (Galushko and
Schink, 2000)
– Genome sequencing completed at The Institute of
Genomic Research (TIGR)
– Applications in bioremediation and bioenergy
generation
Aquifer
surface
Decreasing
O2
Sugars
CO2
Fermentative
bacteria
Organic
acids
CO2
e-
Fe(III)
Day 1, Section 3
Geobacter
Fe(II)
G. sulfurreducens: Applications
Bioremediation
•Can reduce toxic soluble metals Co(III), Tc(VII), U(VI) to insoluble forms
•Clean-up of radioactive/contaminated sites
Acetate
Injection
Zone of U(VI) Removal
U(VI)
U(VI)
Threatened
Down-Stream
Water Resource
U(VI)
U(VI)
Soluble Uranium
Groundwater
DayFlow
1, Section 3
U(IV) U(IV)
Acetate
CO2
e-
Fe(III)
Fe(II)
U(VI)
U(IV)
U(IV)
Insoluble Uranium
G. sulfurreducens: Applications
Bioenergy generation
•Can respire on acetate and donate electrons to electrode
•Has been shown to recover 95% of electrons in acetate
(Bond and Lovley, 2003)
•Microbial fuel cell applications
Acetate
e-
Cell Membrane
CytP
Cytoplasm
CytIM
MQH2
Acetate
XH2
MQ
Anode
Electrons Generated
From Central Metabolism
Cathode
X
Motivates
need for improved understanding of metabolism
Day 1, Section 3
CO2
eAnode
Metabolic Network of G. sulfurreducens
Total Number of Genes: 3466
Included Genes: 588 (17 %)
Day 1, Section 3
Total Number of Model Reactions: 522
Percentage of the annotated genome: (29%) Total Number of Metabolites: 541
Metabolic Modeling Results
• General metabolic capabilities
• Impact of extracellular electron acceptors
– Differences in energy generation
attributed to proton balancing
• Prospective studies
– Analysis of the energetics of
menaquinone secretion
– Explanation for dominance of G.
sulfurreducens in the environment
2
Amino Acid Yields of E. coli (mol AA/mol
Acetate)
– Significance of pyruvate ferredoxin
oxidoreductase for enhanced biomass yields
during growth with acetate
Comparison of Amino Acid Synthesis for Growth on Acetate:
G. sulfurreducens vs. E. coli
1.8
Predicted Yields
Line of Equal Yields
1.6
1.4
A
1.2
G
D
S
1
E
0.8
P
0.6
R
0.4
C
M
F
0.2
Q
T
V
L
K
I
N
H
W
0
0
0.5
1
1.5
2
Amino Acid Yields of G. sulfurreducens (mol AA/mol Acetate)
Energetics of Menaquinone Secretion
(10 mmol/gdw hr Acetate Uptake)
0.9-1
0.8-0.9
1
0.7-0.8
0.6-0.7
0.9
0.5-0.6
0.8
0.4-0.5
0.7
0.3-0.4
0.2-0.3
0.6
0.5
0.1-0.2
0-0.1
0.4
0.3
0.2
0.1
MQN9
MQN8
MQN7
MQN5
MQN4
MQN6
0
0.1
MQN3
Day 1, Section 3
0
0.02
0.04
Menaquinone
0.06
Secretion Rate
0.08
(mmol/gdw hr)
Number of Units
Normalized
Growth Rate
Comparison with E. coli : Amino Acid
Synthesis
• Ability to synthesize amino
acids
during
acetate
oxidation with Fe(III) as
electron acceptor, analyzed
• G. sulfurreducens network
more efficient than E. coli for
synthesizing amino acids
2
Amino Acid Yields of E. coli (mol AA/mol
Acetate)
• Genome-scale metabolic
models used to compare the
capabilities of the networks
of E. coli & G. sulfurreducens
(Reed et al., 2003)
Comparison of Amino Acid Synthesis for Growth on Acetate:
G. sulfurreducens vs. E. coli
1.8
Predicted Yields
Line of Equal Yields
1.6
1.4
A
1.2
G
D
S
1
E
0.8
P
0.6
R
0.4
C
M
F
0.2
Q
T
V
L
K
I
N
H
W
0
0
Day 1, Section 3
0.5
1
1.5
2
Amino Acid Yields of G. sulfurreducens (mol AA/mol Acetate)
One letter code used to represent AAs on
the plot (A:alanine, etc…)
Simulation Studies: Analysis of Proton
Translocation Stoichiometry
• Electron transport chain: Key energy generating step
– Transfer of electrons from a donor to an acceptor
– Accompanied by translocation of protons across the membrane
ATP synthesis driven by
H+ gradient
Maintaining the H+
gradient is critical
Day 1, Section 3
Image from “Biology: A guide to the natural world: David Krogh, 2002”
Proton Translocation
Stoichiometry: Issues
• Number of protons translocated per electron (H+/e- ratio) is a
critical parameter
• H+/e- ratio depends on available energy between the
donor/acceptor (e.g, NADH/O2 ,NADH/Fumarate, NADH/Fe(III)
)
• Thermodynamic consideration leads to H+/e- ratio of 1 for
NADH/Fumarate (Kroger et al., 2002)
• Experimental evidence indicates that biomass yield per mole of
acetate with fumarate is three times the yield on Fe(III)
CO2
Acetate
eFumarate Succinate
Day 1, Section 3
CO2
Acetate
eBiomass yield
(gdw/mmol Ac)
Fe(III)
Fe(II)
Proton Translocation under Fe(III),
Fumarate Reduction
Scheme 1
2H
Acetate
Fumarate
Outside
Inside
NADH
MQH2
MQH2
MQ
H
MQ
2H
Fumarate + 2H
NAD
NADH dehydrogenase
H
H
NADH
NAD
MQ
H
Acetate
Fe(III)
MQH2
MQH2
Inside
Succinate
Fumarate reductase
Succinate dehydrogenase
2H
Outside
Net Rxn: Ac- + H+ +Fum CO2 +Succ
MQ
2H
2H
2H
Cytochrome menaquinol oxidoreductase Fumarate + 2H
Net Rxn: Ac-+Fe(III) CO2 +Fe(II) + H+
Succinate
Fumarate reduction: Net consumption of cytosolic protons
Fe(III) reduction: Net production of cytosolic protons
Electron transport chain results in excess cytosolic proton production
during Fe(III) reduction
Day 1, Section 3
Model-based Analysis of Yield
Differences
4
Excess cytosolic protons
formed during Fe(III)
reduction
•
Energy (ATP) required to
pump excess protons
•
Energetics of proton
pumping leads to
decreased biomass yields
•
Model-based analysis
explained observed yield
differences
•
Key physiological insight
related to proton generation
Model predictions of biomass yield/ATP yield on
acetate for different growth conditions
3.5
Biomass yield (Model)
3
Normalized Yield
•
ATP yield
(Model)
Experimental Yield
2.5
2
1.5
1
0.5
0
Fumarate
Fe(III)
Grow th Condition
Day 1, Section 3
Need for Enhanced Power Generation
Rates
• G. sulfurreducens can respire on acetate and donate electrons
to electrode (Bond & Lovley, 2003)
• Low power generation rates (~0.001 mW/cm2 ) even though
high efficiency
• Clear requirement for enhancing the rate of power generation
through
– strain engineering
– electrode optimization
– protein engineering
Cell Membrane
eCytP
Cytoplasm
CytIM
Acetate
MQH2
XH2
Electrons Generated
Day 1,From
Section
3
Central
Metabolism
X
MQ
Anode
Cathode
Low-powered
devices
Engineering Geobacter for Enhanced
Electricity Generation Capability
Goal:
Increase electricity (i.e. current) generation capability of Geobacter
1) modify native metabolism to increase electron transfer rate/respiration
Cell
Membrane
Acetate
Cytoplasm
e-
ATP Drain
Glycerol
Electrons (e-)
Anode
Glucose
Day 1, Section 3
Cathode
Model Predictions: Creating Futile
Cycles
• Simulations indicate that increased ATP
Fe(III) Reduction vs. ATP drain: 10 mmol/gdw hr
Acetate
drain/maintenance leads to increased
electron transport flux
1
Normalized Rates
 ATPdrain ~  e- transport,  
1.2
0.8
0.6
0.4
Normalized Fe(III) Uptake Rate
0.2
Normalized Growth Rate
0
0
0.5
1
1.5
2
2.5
ATP drain (mmol/gdw hr)
• Koebmann et al. (2002) overexpressed F1
subunit to increase flux through glycolysis
• Ingram et al. (2003) deleted F0 subunit to
increase acetate production
• Establish a futile cycle by overexpressing a
soluble ATP synthase to  ATPdrain
(confirm initially for growth on acetate)
Day 1, Section 3
Cytosol
Membrane
Engineering Futile Cycle: Results
With futile cycle
Without futile cycle (control)
•Geobacter (pCD::atpAGD)
• acetate & fumarate (e- acceptor)
•Geobacter (pCD)
• acetate & fumarate (e- acceptor)
1.2
1.2
1
1
0.8
OD600
OD. 600 nm
0.8
0.6
0 uM
1 uM
10 uM
100 uM
1000 uM
0.4
0.2
0
0 uM
1 uM
10 uM
100 uM
1000uM
0.6
0.4
0.2
0
0
12
24
36
48
60
0
12
24
36
48
Hours
Hours
Engineering Respiration Rate
• Specific Level of Soluble Fe(III) reduction:
No induction:
1.19x10-7 mM FeII/cell
1mM IPTG:
2.4x10-7 mM FeII/cell
~2x  Respiration Rate on Fe(III) due to Futile Cycle
Day 1, Section 3
Mounir Izallalen from Lovley group at UMass
2.5
Normalized Respiration Rates
• Rate of fumarate reduction:
No induction:
0.88 mM Succ/OD/h
1mM IPTG:
1.13 mM Succ/OD/h
2
Without futile cycle
With futile cycle
1.5
1
0.5
0
Growth on Fe(III)
60