Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-1-
Bioproduct Production
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-2-
Learning Objectives
• Explain the basic principles of strain design using constraint-based metabolic
reconstructions
• Explain how the limitations of the growth media can be understood using constraintbased metabolic reconstructions
• Explain the role Method of Minimization of Metabolic Adjustment (MOMA) can play in
bioproduct production
• Explain adaptive laboratory evolution
• Explain the new strain design tools that have been developed for constraint-based
metabolic reconstructions
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-3-
Lesson Outline
• Overview
• Strain Design for Bioproduct Production
• Media Optimization for Bioproduct Production
• Method of Minimization of Metabolic Adjustment (MOMA)
• Adaptive Laboratory Evolution
• New Potential Design Tools
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-4-
iAF1260
Model
(GlucoseEthanol_iaf1260.pdf)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-5-
iAF1260
Metabolic
Core
(GlucoseEthanol_iaf1260.pdf)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-6-
iAF1260
Amino Acid
Metabolism
(GlucoseEthanol_iaf1260.pdf)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-7-
iAF1260
Necleotide
Metabolism
(GlucoseEthanol_iaf1260.pdf)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-8-
iAF1260
Alternate
Carbon Sources
(GlucoseEthanol_iaf1260.pdf)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-9-
iAF1260
Cofactor
Biosynthesis
(GlucoseEthanol_iaf1260.pdf)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-10-
iAF1260
Inorganic Ion
Transport
(GlucoseEthanol_iaf1260.pdf)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-11-
Fatty acid Biosynthesis
(GlucoseEthanol_iaf1260.pdf)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-12-
tRNA Charging
(GlucoseEthanol_iaf1260.pdf)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-13-
Enterobacterial
Common
Antigen
(GlucoseEthanol_iaf1260.pdf)
Enterobacterial common antigen (ECA), also referred to as an
endotoxin, is a family-specific surface antigen shared by all members
of the Enterobacteriaceae and is restricted to this family. It is found
in freshly isolated wild-type strains as well as in laboratory strains like
Escherichia coli K-12. ECA is located in the outer leaflet of the outer
membrane. It is a glycophospholipid built up by an aminosugar
heteropolymer linked to an L-glycerophosphatidyl residue.
Kuhn, H. M., U. Meier-Dieter, et al. (1988). "ECA, the enterobacterial
common antigen." FEMS microbiology reviews 4(3): 195-222.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-14-
E.coli Genome-scale Reconstructions
•
Escherichia coli 042
•
Escherichia coli ETEC H10407
•
Escherichia coli O55:H7 str. CB9615
•
Escherichia coli 536
•
Escherichia coli HS
•
Escherichia coli O83:H1 str. NRG 857C
•
Escherichia coli 55989
•
Escherichia coli IAI1
•
Escherichia coli S88
•
Escherichia coli ABU 83972
•
Escherichia coli IAI39
•
Escherichia coli SE11
•
Escherichia coli APEC O1
•
Escherichia coli IHE3034
•
Escherichia coli SE15
•
Escherichia coli ATCC 8739
•
Escherichia coli KO11FL
•
Escherichia coli SMS-3-5
•
Escherichia coli B str. REL606
•
Escherichia coli LF82
•
Escherichia coli str. K-12 substr. DH10B
•
Escherichia coli BL21(DE3) AM946981
•
Escherichia coli NA114
•
Escherichia coli str. K-12 substr. MG1655
•
Escherichia coli BL21(DE3) BL21-Gold(DE3)pLysS AG
•
Escherichia coli O103:H2 str. 12009
•
Escherichia coli str. K-12 substr. W3110
•
Escherichia coli BL21(DE3) CP001509
•
Escherichia coli O111:H- str. 11128
•
Escherichia coli UM146
•
Escherichia coli BW2952
•
Escherichia coli O127:H6 str. E2348/69
•
Escherichia coli UMN026
•
Escherichia coli CFT073
•
Escherichia coli O157:H7 EDL933
•
Escherichia coli UMNK88
•
Escherichia coli DH1
•
Escherichia coli O157:H7 str. EC4115
•
Escherichia coli UTI89
•
Escherichia coli DH1 ME8569
•
Escherichia coli O157:H7 str. Sakai
•
Escherichia coli W
•
Escherichia coli E24377A
•
Escherichia coli O157:H7 str. TW14359
•
Escherichia coli W CP002185
•
Escherichia coli ED1a
•
Escherichia coli O26:H11 str. 11368
•
Escherichia coli K-12 MG1655
Strain used
at USU
Monk, J. M., P. Charusanti, et al. (2013). Proceedings of the National Academy of Sciences of the United States of America 110(50): 20338-20343.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-15-
Core and Pan Metabolic Capabilities of the E. coli Species.
Monk, J. M., P. Charusanti, et al. (2013). Proceedings of the National Academy of Sciences of the United States of America 110(50): 20338-20343.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-16-
Clustering of Species by Unique Growth-supporting Conditions
655 combinations
Monk, J. M., P. Charusanti, et al. (2013). Proceedings of the National Academy of Sciences of the United States of America 110(50): 20338-20343.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-17-
Predicting Microbial Growth
Single Knockout
Double Knockouts
Monk, J. and B. O. Palsson (2014). "Genetics. Predicting microbial growth." Science 344(6191): 1448-1449.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-18-
Lesson Outline
• Overview
• Strain Design for Bioproduct Production
• Media Optimization for Bioproduct Production
• Method of Minimization of Metabolic Adjustment (MOMA)
• Adaptive Laboratory Evolution
• New Potential Design Tools
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-19-
Strain Design for Bioproduct Production
• Strain design could include:
 Gene/Reaction knockouts to emphasize existing bioproduct pathways,
 Adding genes/reactions to create a new pathway,
 Media modifications to maximize bioproduct production.
• Strain design goals and objectives
 Understand the design space available in the host cell,
 Understand the maximum theoretical growth rate and bioproduct production,
 Try to couple the growth of the host cell to bioproduct production,
 Understand the metabolic burden imposed on the host cell with added plasmids.
 Match the nutrient requirements of the new engineered host cell to the supporting media.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-20-
Reaction Knockouts: Ethanol Production
% AnaerobicEthanolRA.m
clear; clc;
This curve represents
the maximum possible
growth rate
% Input the E.coli core model
model=readCbModel('ecoli_textbook');
% Set uptake rates
model = changeRxnBounds(model,'EX_glc(e)',-10,'b');
model = changeRxnBounds(model,'EX_o2(e)',-0,'b');
model = changeRxnBounds(model,'EX_etoh(e)',0,'l');
% Set optimization objective to Biomass_Ecoli_core_N(w/GAM)_Nmet2
model = changeObjective(model,'Biomass_Ecoli_core_N(w/GAM)_Nmet2');
% Using robustnessAnalysis, plot the objective function as a function
Larger production,
lower growth rate
Maximum ethanol production
with no growth
% of the ethanol secretion rate
[controlFlux, objFlux] = robustnessAnalysis(model,'EX_etoh(e)',100);
Robustness Analysis
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-21-
OptKnock Example: Maximizing Ethanol Production
(EthanolProduction_OptKnock_Gurobi.m)
% EthanolProduction_OptKnock_Gurobi_Revised.m
clear; clc;
% Input the E.coli core model
model=readCbModel('ecoli_textbook');
solverOK = changeCobraSolver('gurobi5','all');
% Set carbon source and oxygen uptake rates
model = changeRxnBounds(model,'EX_glc(e)',-10,'l');
model = changeRxnBounds(model,'EX_o2(e)',-0,'l');
model = changeObjective(model,'Biomass_Ecoli_core_N(w/GAM)_Nmet2');
(0.0767, 18.83)
% Select reactions to be explored by the optKnock algorithm
[transRxns,nonTransRxns] = findTransRxns(model,true);
[tmp,ATPMnumber] = ismember('ATPM',nonTransRxns); % Identify ATPM reaction number
[tmp,BioMassnumber] = ismember('Biomass_Ecoli_core_N(w/GAM)_Nmet2',nonTransRxns); % Identify biomass reaction number
nonTransRxnsLength = length(nonTransRxns); % Find number of non-transport reactions
selectedRxns = {nonTransRxns{[1:ATPMnumber-1, ATPMnumber+1:BioMassnumber-1, BioMassnumber+1:nonTransRxnsLength]}};
% Optknock analysis for Ethanol secretion
options.targetRxn = 'EX_etoh(e)';
options.vMax = 1000;
options.numDel = 5;
options.numDelSense = 'L';
constrOpt.rxnList = {'Biomass_Ecoli_core_N(w/GAM)_Nmet2','ATPM'};
constrOpt.values = [0.05, 8.39];
constrOpt.sense = 'GE';
deletions = 'ACKr'
'GLUDy'
'ME2'
'PGL'
'PYK'
growthRate = 0.0767
minProd = 18.5452; maxProd = 18.8342
optKnockSol = OptKnock(model, selectedRxns, options, constrOpt);
deletions = optKnockSol.rxnList'
% Print out growth rate and minimum & maximum secretion rate
[growthRate,minProd,maxProd] = testOptKnockSol(model,'EX_etoh(e)',optKnockSol.rxnList)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-22-
Multiproduction Envelopes for Different Knockouts
('ACKr‘, 'GLUDy‘, 'PYK‘)
('PFL‘)
('ACKr‘, 'GLUDy‘, 'ME2‘, 'PGL‘, 'PYK‘)
(0.0852, 18.71)
(0.1800, 16.59)
(0.0767, 18.83)
('GND‘, 'ME2‘, 'PTAr‘, 'PYK‘)
('PTAr‘, 'PYK‘)
(0.0816, 18.76)
(0.0913, 18.61)
Note that only one
case includes
secondary secretion.
EthanolProduction_OptKnock_Gurobi_Revised.m
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-23-
Adding Reactions
addReaction - Add a reaction to the model or modify an existing reaction
model = addReaction(model,rxnName,metaboliteList,stoichCoeffList,revFlag,lowerBound,upperBound,objCoeff,subSystem,grRule,geneNameList,systNameList,checkDuplicate)
model = addReaction(model,rxnName,rxnFormula)
INPUTS
model
rxnName
metaboliteList
stoichCoeffList
COBRA model structure
Reaction name abbreviation (i.e. 'ACALD')
(Note: can also be a cell array {'abbr','name'}
Cell array of metabolite names or alternatively the
reaction formula for the reaction
List of stoichiometric coefficients (reactants -ve,
products +ve), empty if reaction formula is provided
OUTPUTS
model
rxnIDexists
COBRA model structure with new reaction
Empty if the reaction did not exist previously, or if
checkDuplicate is false. Otherwise it contains the ID
of an identical reaction already present in the model.
EXAMPLES
OPTIONAL INPUTS
revFlag
Reversibility flag (Default = true)
lowerBound
Lower bound (Default = 0 or -vMax)
upperBound
Upper bound (Default = vMax)
objCoeff
Objective coefficient (Default = 0)
subsystem
Subsystem (Default = '')
grRule
Gene-reaction rule in boolean format (and/or allowed)
(Default = '');
geneNameList
List of gene names (used only for translation from
common gene names to systematic gene names)
systNameList
List of systematic names
checkDuplicate
Check S matrix too see if a duplicate reaction is
already in the model (Default true)
1) Add a new irreversible reaction using the formula approach
model = addReaction(model,'newRxn1','A -> B + 2 C')
2) Add a the same reaction using the list approach
model = addReaction(model,'newRxn1',{'A','B','C'},[-1 1 2],false);
http://opencobra.sourceforge.net/openCOBRA/opencobra_documentation/cobra_toolbox_2/index.html
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
Example #1: Biliverdin
heme
biliverdin
a
heme
oxygenase
+ Fe + CO
+ O2
+ e-
H. Scott Hinton, 2015
-24-
Potential Biliverdin protective effects:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
endotoxic shock
brain infarction
vascular intimal hyperplasia
vascular endothelial dysfunction
lung injury
airway inflammation
ischemia reperfusion injury
tissue graft rejection
corneal epithelial injury
hepatitis C infection
artheriosclerosis
colitis
cancer cell proliferation
type 2 diabetes
type 1 diabetes (pancreatic Islet cell
protection)
Jon Takemoto, USU SBI Bioproducts Summit, February 2012
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-25-
Biliverdin
Gene
Dong Chen, USU SBI Bioproducts Summit, February 2012
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-26-
Heme Oxygenase (HO1) Protein Expression
• The vector with HO1 gene
was transformed to E. coli
BL21(DE3)
• LB medium with 100 µg/ml
Kanamycin was used
• HO1 protein expression
was induced by IPTG or
lactose
• 37 ˚C, 225 rpm overnight
Biliverdin Production
Dong Chen, USU SBI Bioproducts Summit, February 2012
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-27-
Location of Heme
Pathway in iAF1260
Model
BIGG Models @ http://bigg.ucsd.edu/
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-28-
Biliverdin Pathway in iAF1260
nadph h o2
nadp fe2 co h2o
BILIVERDINtpp BILIVERDINtex
EX_biliverdin(e)
HEMEOX
biliverdin[c]
biliverdin[p]
biliverdin[e]
Biliverdin Pathway
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-29-
Biliverdin Complete Pathway
L-Glutamate
α-ketoglutarate
Secreted Biliverdin
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-30-
Key Components of the Biliverdin Pathway in iAF1260
O2
ATP
Fe2
ATP
NADPH
L-Glutamate
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-31-
What are the Limits of Biliverdin Production?
Desired
Operating
Point
Opportunity
Space
Minimum
Production
Rate
Biliverdin_Robustness_iJO1366.m
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
% Input the E.coli core model
model=readCbModel('ecoli_iJO1366');
H. Scott Hinton, 2015
-32-
Adding Biliverdin Pathway to the Model
% Add heme oxygenase enzyme
model=addReaction(model,'HEMEOX','pheme[c] + 3 nadph[c] + 5 h[c] + 3 o2[c] -> biliverdin[c] + fe2[c] + co[c] + 3 nadp[c] + 3 h2o[c]');
% Add
model
model
model
biliverdin secretion reactions
= addReaction(model,'BILIVERDINtex','biliverdin[p] <=> biliverdin[e]');
= addReaction(model,'BILIVERDINtpp','biliverdin[c] <=> biliverdin[p]');
= addReaction(model,'EX_biliverdin(e)','biliverdin[e] <=>');
metID=findMetIDs(model,{'biliverdin[c]', 'biliverdin[p]', 'biliverdin[e]'});
model.metCharge(metID(1))= -2;
model.metCharge(metID(2))= -2;
pheme[c]
model.metCharge(metID(3))= -2;
% Add
model
model
model
carbon monoxide secretion reactions
= addReaction(model,'COtpp','co[c] <=> co[p]');
= addReaction(model,'COtex','co[p] <=> co[e]');
= addReaction(model,'EX_co(e)','co[e] <=>');
Biliverdin Pathway
nadph h o2
nadp fe2 co h2o
BILIVERDINtpp BILIVERDINtex
EX_biliverdin(e)
HEMEOX
biliverdin[c]
Default Reaction Settings:
UB = 1000
LB = -1000
Rxn
Rxn Name
Description
Utah State University
Formula
biliverdin[p]
biliverdin[e]
Desired Reaction Model Information
GeneReaction
Association
Genes
Proteins
Subsystem Reversible
LB
BIE 5500/6500
UB
Objective
Confidence
EC Number
Score
Notes
References
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-33-
% Biliverdin_optKnock_iJO1366_Precalculated_Reactions.m
clear; clc;
% Input the E.coli core model
model=readCbModel('ecoli_iJO1366');
Add biliverdin reactions
% Set
model
model
model
key variables
= changeRxnBounds(model,'EX_glc(e)',-10,'l');
= changeRxnBounds(model,'EX_o2(e)',-20,'l');
= changeObjective(model,'Ec_biomass_iJO1366_core_53p95M');
Creating and Saving Selected
Reactions in iJO1366
% Select potential knockout reaction
[GeneClasses RxnClasses modelIrrevFM] = pFBA(model, 'geneoption',0);
undesiredReactions = model.rxns(ismember(model.subSystems,{'Cell Envelope Biosynthesis','Glycerophospholipid Metabolism', ...
'Inorganic Ion Transport and Metabolism','Lipopolysaccharide Biosynthesis / Recycling','Membrane Lipid Metabolism',...
'Murein Recycling','Transport, Inner Membrane','Transport, Outer Membrane Porin','Transport, Outer Membrane','tRNA Charging'}));
Removing
Undesired
Subsystems
[transRxns,nonTransRxns] = findTransRxns(model,true);
[hiCarbonRxns,nCarbon] = findHiCarbonRxns(model,7);
[a,ids] = ismember([RxnClasses.Essential_Rxns; RxnClasses.ZeroFlux_Rxns; RxnClasses.Blocked_Rxns;...
undesiredReactions;hiCarbonRxns; {'ATPM'};{'Ec_biomass_iJO1366_core_53p95M'}], nonTransRxns);
id1=ids(a);
nonTransRxns(id1)=[];
selectedRxns=nonTransRxns;
Save ‘selectedRxns’
to a “mat” file
save('Biliverdin_SelectedRxns_iJO1366.mat','selectedRxns'); % Save the selected Reactions is a MAT file
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-34-
% Biliverdin_OptKnock_iJO1366_WithPrecalculatedReactions.m
clear; clc;
% Input the E.coli core model
model=readCbModel('ecoli_iJO1366');
Add Biliverdin Reactions
% Set key variables
model = changeRxnBounds(model,'EX_glc(e)',-10,'l');
model = changeRxnBounds(model,'EX_o2(e)',-20,'l');
% Set optimization objective
model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M');
% Load list of selected reactions
load('Biliverdin_SelectedRxns_iJO1366');
Engineering E.coli to
Produce Biliverdin
with OptKnock
Load ‘selectedRxns’
target = 'EX_biliverdin(e)';
options.targetRxn = target;
from“mat” file
options.vMax = 1000;
options.numDel = 2;
options.numDelSense = 'L';
constrOpt.rxnList = {'Ec_biomass_iJO1366_core_53p95M', 'ATPM'};
constrOpt.values = [0.1, 3.15];
constrOpt.sense = 'GE';
[optKnockSol,bilevelMILPproblem] = OptKnock(model, selectedRxns, options, constrOpt,{},true);
% [optKnockSol,bilevelMILPproblem] = OptKnock(model, selectedRxns, options, constrOpt,{},false);
[optKnockSol.rxnList']
[growthRate,minProd,maxProd] = testOptKnockSol(model,target,optKnockSol.rxnList)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-35-
Biliverdin OptKnock Results
1 Knockout
ans = 'PPKr'
growthRate = 0.2325
minProd = 0
maxProd = 0
5 Knockouts
ans = 'G6PDA'
growthRate = 0.9824
minProd = 0
maxProd = 1.7997e-08
Utah State University
2 Knockouts
ans = Empty cell array
growthRate = 0.2325
minProd = 0
maxProd = 0
10 Knockouts
WHY?
ans = 'CITL‘,'FUM‘,'G6PDA‘,'I4FE4ST‘,
'ICL‘,'TKT2‘,'XAND''
growthRate = 0.2325
minProd = 0
maxProd = 0
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-36-
Biliverdin Complete Pathway
1. Can’t find reactions to couple the
output production to the biomass
growth.
2. But, the HEMEOX reaction was
added to the cell with no
regulatory constraints so it will
automatically produce biliverdin
as a function of the HEMEOX
enzymes created by the plasmid.
3. To use the model, set a probable
lower limit for the HEMOX
reaction.
Secreted Biliverdin
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-37-
Biliverdin Production
Assumes the unregulated HEMEOX
Enzyme can produce 1.2 mmol/gDW-hr
These values equal
the production rate
of the HEMEOX
enzyme
Minimum
Production
Rate
Biliverdin_Robustness_iJO1366.m
Utah State University
Biliverdin_ProductionEnvelope_iJO1366.m
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-38-
Example #2: Bioplastic (PHB) Pathway in E.coli
PHA: Polyhydroxyalkanoate
Present in E.coli
coa
nadp
nadph h
coa
- family of bioplastics
DM_phb
accoa
ACACT1r
aacoa
AACOAr
PHBpoly
r3hbcoa
phb[c]
PHB: Polyhydroxybutyrate
PHB Pathway
- subset of PHAs
PHB
Demand Reaction
Also known as:
poly-3-hydroxybutyrate,
poly(3-hydroxybutyrate), and
poly(β-hydroxy butyric acid)
TEM image of PHB inside bacteria
Bioplastic
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-39-
Metabolic Genome-Scale
Metabolic Modeling
in E.coli
BIGG Models @ http://bigg.ucsd.edu/
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-40-
PHB
Pathway
Acetyl-CoA
PHB Pathway
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-41-
What are the Limits of PHB Production?
Minimum
Production
Rate
PHB_Robustness_iJO1366.m
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-42-
Adding PHB Reactions
% PHB_OptKnock_iJO1366_Precalculated_Reactions.m
clear; clc;
model=readCbModel('ecoli_iJO1366');
% Add Reaction (beta-ketothiolase) - Already included in the iaf1260 model
%model=addReaction(model,'ACACT1r', '2 accoa[c] <=> aacoa[c] + coa[c]');
%metID=findMetIDs(model,'aacoa[c]');
%model.metCharge(metID)= -4;
% Add Reaction (acetoacetyl-CoA reductase)
model=addReaction(model,'AACOAr', 'aacoa[c] + nadph[c] + h[c]
metID=findMetIDs(model,'r3hbcoa[c]');
model.metCharge(metID)= -4;
Already in E.coli
<=> r3hbcoa[c] + nadp[c]');
% Add Reaction (PHB polymerase)
model=addReaction(model,'PHBpoly', 'r3hbcoa[c] -> phb[c] + coa[c]');
%model=addReaction(model,'PHBpoly', '1860.0 r3hbcoa[c] -> phb[c] + 1860.0 coa[c]');
metID=findMetIDs(model,'phb[c]');
model.metCharge(metID)= -4;
% Add demand reaction
model = addDemandReaction(model,'phb[c]');
Utah State University
Adding a demand reaction
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-43-
PHB Production
Knockouts = 'MDH','PSP_L','PTAr','TPI'
Production of unregulated PHB pathway
Desired
Operating
Point
PHB_ProductionEnvelope_iJO1366.m
PHB_Robustness_iJO1366.m
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-44-
Example #3: Spider Silk
Potential Applications
• Wound dressings
• Tissue and bone scaffolds
• Sutures
• Artificial nerves
• Vessels for drug delivery
• Coatings for biomedical implants
• Ligament and tendon replacements
• Parachute cords
• Composite materials in aircrafts
• Construction materials
Sarah Allred, “Metabolic Modeling of Spider Silk Production in E. coli,” MS Thesis, USU, 2014
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-45-
Spider Silk: Mechanical Properties
Spider Silk Protein
Sarah Allred, “Metabolic Modeling of Spider Silk Production in E. coli,” MS Thesis, USU, 2014
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-46-
Spider Silk: Flagelliform Composition
Amino Acid Composition of Silks from A. diadematus
Amino acid
Major ampullate
Minor ampullate
Flagelliform
Aciniform
Tubuliform
Asp
1.04
1.91
2.68
8.04
6.26
Thr
0.91
1.35
2.48
8.66
3.44
Ser
7.41
5.08
3.08
15.03
27.61
Glu
11.49
1.59
2.89
7.22
8.22
Pro
15.77
trace amounts
20.54
2.99
0.59
Gly
37.24
42.77
44.16
13.93
8.63
Ala
17.60
36.75
8.29
11.30
24.44
Val
1.15
1.73
6.68
7.37
5.97
Ile
0.63
0.67
1.01
4.27
1.69
Leu
1.27
0.96
1.40
10.10
5.73
Tyr
3.92
4.71
2.56
1.99
0.95
Phe
0.45
0.41
1.08
2.79
3.22
Lys
0.54
0.39
1.35
1.90
1.76
His
trace amounts
trace amounts
0.68
0.31
trace amounts
Arg
0.57
1.69
1.13
4.09
1.49
Sarah Allred, “Metabolic Modeling of Spider Silk Production in E. coli,” MS Thesis, USU, 2014
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-47-
Spider Silk
Production
in E.coli
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-48-
What are the Limits of Spider Silk Production?
Desired
Operating
Point
Minimum
Production
Rate
Spider_Silk_Robustness_iJO1366.m
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-49-
Spider Silk Protein
% Spider_Silk_OptKnock_iJO1366_WithPrecalculatedReactions
% Read ecoli_iJO1366 model
model = readCbModel('ecoli_iJO1366');
% Add FlYS3 reaction, change lower bound
model = addReaction(model,'FlYS3','120 ala-L[c] + 4 asp-L[c] + 522 gly[c] + 12 his-L[c] +
ile-L[c] + lys-L[c] + 2 met-L[c] + 189 pro-L[c] + 111 ser-L[c] + thr-L[c] + 78 tyr-L[c] +
4476.3 atp[c] + 4476.3 h2o[c] -> flys3[c] + 4476.3 adp[c] + 4476.3 h[c] + 4476.3 pi[c]');
% Add demand reaction
model = addDemandReaction(model,'flys3[c]'); %'DM_flys3[c]’
% model = changeRxnBounds(model,'FlYS3',0.00336705,'l');
% Set objective
model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M');
% Setting carbon source and oxygen
model = changeRxnBounds(model,'EX_glc(e)',-11,'l');
model = changeRxnBounds(model,'EX_o2(e)',-20,'l');
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-50-
Spider Silk Production
Desired
Operating
Point
Opportunity
Space
Minimum
Production
Rate
3.3
Allred Production Rate
Sarah Allred, “Metabolic Modeling of Spider Silk
Production in E. coli,” MS Thesis, USU, 2014
Spider_Silk_Robustness_iJO1366.m
Utah State University
Spider_Silk_ProductionEnvelope_iJO1366.m
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-51-
Review Questions
1.
What are the main Cobra tools for determining the knockouts that can improve bioproduct production?
2.
What is the Cobra function for adding a reaction?
3.
Can a host cell be growth-coupled to all new pathways created by adding reactions?
4.
Why can a cell produce a bioproduct when a gene is added to a host cell even though the host is not
growth-coupled to the bioproduct?
5.
What governs the maximum production of a bioproduct in a given host cell?
6.
What role does the media play in the maximum production of a bioproduct?
7.
What is the difference between the bioproduction of a modified cell precursor (biliverdin, PHB) and a
protein-based bioproduct (spider silk)?
8.
What is a demand reaction?
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-52-
Lesson Outline
• Overview
• Strain Design for Bioproduct Production
• Media Optimization for Bioproduct Production
• Method of Minimization of Metabolic Adjustment (MOMA)
• Adaptive Laboratory Evolution
• New Potential Design Tools
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-53-
Media Optimization
• The media can significantly alter the growth rate and bioproduct production
• Some amino acids and other metabolites can act as carbon sources
• Some produced bioproducts could require increased minerals uptake
• Adapting the composition of media to meet the needs of the engineered cells
can significantly improve both the growth rate and bioproduct production.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
iAF1260
Amino Acid
Exchange
Reactions
Note: Amino acids are only allowed to be
secreted in the basic model (LB = 0).
The model can be modified to allow
amino acid uptake.
No essential amino acids for E.coli
Utah State University
Rxn name
EX_ala_L(e)
EX_arg_L(e)
EX_asn_L(e)
EX_asp_L(e)
EX_cys_L(e)
EX_gln_L(e)
EX_glu_L(e)
EX_gly(e)
EX_his_L(e)
EX_ile_L(e)
EX_leu_L(e)
EX_lys_L(e)
EX_met_L(e)
EX_phe_L(e)
EX_pro_L(e)
EX_ser_L(e)
EX_thr_L(e)
EX_trp_L(e)
EX_tyr_L(e)
EX_val_L(e)
Rxn description
L-Alanine exchange
L-Arginine exchange
L-Asparagine exchange
L-Aspartate exchange
L-Cysteine exchange
L-Glutamine exchange
L-Glutamate exchange
Glycine exchange
L-Histidine exchange
L-Isoleucine exchange
L-Leucine exchange
L-Lysine exchange
L-Methionine exchange
L-Phenylalanine exchange
L-Proline exchange
L-Serine exchange
L-Threonine exchange
L-Tryptophan exchange
L-Tyrosine exchange
L-Valine exchange
BIE 5500/6500
H. Scott Hinton, 2015
Formula
ala-L[e] <=>
arg-L[e] <=>
asn-L[e] <=>
asp-L[e] <=>
cys-L[e] <=>
gln-L[e] <=>
glu-L[e] <=>
gly[e] <=>
his-L[e] <=>
ile-L[e] <=>
leu-L[e] <=>
lys-L[e] <=>
met-L[e] <=>
phe-L[e] <=>
pro-L[e] <=>
ser-L[e] <=>
thr-L[e] <=>
trp-L[e] <=>
tyr-L[e] <=>
val-L[e] <=>
-54-
LB
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
UB
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-55-
iaf1260 Biomass Objective Function
Amino Acid Components
(Ec_biomass_iAF1260_core_59p81M)
0.000223 10fthf[c] + 0.000223 2ohph[c] + 0.5137 ala-L[c] + 0.000223 amet[c] + 0.2958 arg-L[c] + 0.2411 asn-L[c] + 0.2411 asp-L[c] +
59.984 atp[c] + 0.004737 ca2[c] + 0.004737 cl[c] + 0.000576 coa[c] + 0.003158 cobalt2[c] + 0.1335 ctp[c] + 0.003158 cu2[c] + 0.09158
cys-L[c] + 0.02617 datp[c] + 0.02702 dctp[c] + 0.02702 dgtp[c] + 0.02617 dttp[c] + 0.000223 fad[c] + 0.007106 fe2[c] + 0.007106 fe3[c]
+ 0.2632 gln-L[c] + 0.2632 glu-L[c] + 0.6126 gly[c] + 0.2151 gtp[c] + 54.462 h2o[c] + 0.09474 his-L[c] + 0.2905 ile-L[c] + 0.1776 k[c] +
0.01945 kdo2lipid4[e] + 0.4505 leu-L[c] + 0.3432 lys-L[c] + 0.1537 met-L[c] + 0.007895 mg2[c] + 0.000223 mlthf[c] + 0.003158 mn2[c] +
0.003158 mobd[c] + 0.01389 murein5px4p[p] + 0.001831 nad[c] + 0.000447 nadp[c] + 0.011843 nh4[c] + 0.02233 pe160[c] + 0.04148
pe160[p] + 0.02632 pe161[c] + 0.04889 pe161[p] + 0.1759 phe-L[c] + 0.000223 pheme[c] + 0.2211 pro-L[c] + 0.000223 pydx5p[c] +
0.000223 ribflv[c] + 0.2158 ser-L[c] + 0.000223 sheme[c] + 0.003948 so4[c] + 0.000223 thf[c] + 0.000223 thmpp[c] + 0.2537 thr-L[c] +
0.05684 trp-L[c] + 0.1379 tyr-L[c] + 5.5e-005 udcpdp[c] + 0.1441 utp[c] + 0.4232 val-L[c] + 0.003158 zn2[c] -> 59.81 adp[c] + 59.81 h[c]
+ 59.806 pi[c] + 0.7739 ppi[c]
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
iAF1260
Model Mineral
Exchanges
Note that all the minerals
except cob(I)alamin allow
unlimited uptake.
Utah State University
Rxn name
EX_ca2(e)
EX_cbl1(e)
EX_cl(e)
EX_co2(e)
EX_cobalt2(e)
EX_cu2(e)
EX_fe2(e)
EX_fe3(e)
EX_h2o(e)
EX_h(e)
EX_k(e)
EX_mg2(e)
EX_mn2(e)
EX_mobd(e)
EX_na1(e)
EX_nh4(e)
EX_pi(e)
EX_so4(e)
EX_tungs(e)
EX_zn2(e)
Rxn description
Calcium exchange
Cob(I)alamin exchange
Chloride exchange
CO2 exchange
Co2+ exchange
Cu2+ exchange
Fe2+ exchange
Fe3+ exchange
H2O exchange
H+ exchange
K+ exchange
Mg exchange
Mn2+ exchange
Molybdate exchange
Sodium exchange
Ammonia exchange
Phosphate exchange
Sulfate exchange
tungstate exchange
Zinc exchange
BIE 5500/6500
H. Scott Hinton, 2015
Formula
ca2[e] <=>
cbl1[e] <=>
cl[e] <=>
co2[e] <=>
cobalt2[e] <=>
cu2[e] <=>
fe2[e] <=>
fe3[e] <=>
h2o[e] <=>
h[e] <=>
k[e] <=>
mg2[e] <=>
mn2[e] <=>
mobd[e] <=>
na1[e] <=>
nh4[e] <=>
pi[e] <=>
so4[e] <=>
tungs[e] <=>
zn2[e] <=>
LB
-1000
-0.01
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-1000
-56-
UB
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-57-
iaf1260 Biomass Objective Function
Mineral Components
(Ec_biomass_iAF1260_core_59p81M)
0.000223 10fthf[c] + 0.000223 2ohph[c] + 0.5137 ala-L[c] + 0.000223 amet[c] + 0.2958 arg-L[c] + 0.2411 asn-L[c] + 0.2411 asp-L[c] +
59.984 atp[c] + 0.004737 ca2[c] + 0.004737 cl[c] + 0.000576 coa[c] + 0.003158 cobalt2[c] + 0.1335 ctp[c] + 0.003158 cu2[c] + 0.09158
cys-L[c] + 0.02617 datp[c] + 0.02702 dctp[c] + 0.02702 dgtp[c] + 0.02617 dttp[c] + 0.000223 fad[c] + 0.007106 fe2[c] + 0.007106 fe3[c]
+ 0.2632 gln-L[c] + 0.2632 glu-L[c] + 0.6126 gly[c] + 0.2151 gtp[c] + 54.462 h2o[c] + 0.09474 his-L[c] + 0.2905 ile-L[c] + 0.1776 k[c] +
0.01945 kdo2lipid4[e] + 0.4505 leu-L[c] + 0.3432 lys-L[c] + 0.1537 met-L[c] + 0.007895 mg2[c] + 0.000223 mlthf[c] + 0.003158 mn2[c] +
0.003158 mobd[c] + 0.01389 murein5px4p[p] + 0.001831 nad[c] + 0.000447 nadp[c] + 0.011843 nh4[c] + 0.02233 pe160[c] + 0.04148
pe160[p] + 0.02632 pe161[c] + 0.04889 pe161[p] + 0.1759 phe-L[c] + 0.000223 pheme[c] + 0.2211 pro-L[c] + 0.000223 pydx5p[c] +
0.000223 ribflv[c] + 0.2158 ser-L[c] + 0.000223 sheme[c] + 0.003948 so4[c] + 0.000223 thf[c] + 0.000223 thmpp[c] + 0.2537 thr-L[c] +
0.05684 trp-L[c] + 0.1379 tyr-L[c] + 5.5e-005 udcpdp[c] + 0.1441 utp[c] + 0.4232 val-L[c] + 0.003158 zn2[c] -> 59.81 adp[c] + 59.81 h[c]
+ 59.806 pi[c] + 0.7739 ppi[c]
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
in silico
M9 Minimal
Media
This in silico media assumes the
cell can uptake all the minerals
wanted/needed from the media.
It does not allow amino acid
uptake.
Monk, J. M., P. Charusanti, et al. (2013). "Genome-scale
metabolic reconstructions of multiple Escherichia coli strains
highlight strain-specific adaptations to nutritional
environments." Proc Natl Acad Sci USA 110(50): 20338-20343.
Utah State University
Reaction Abbreviation
EX_ca2(e)
EX_cl(e)
EX_co2(e)
EX_cobalt2(e)
EX_cu2(e)
EX_fe2(e)
EX_fe3(e)
EX_h(e)
EX_h2o(e)
EX_k(e)
EX_mg2(e)
EX_mn2(e)
EX_mobd(e)
EX_na1(e)
EX_tungs(e)
EX_zn2(e)
EX_ni2(e)
EX_sel(e)
EX_slnt(e)
EX_so4(e)
EX_nh4(e)
EX_pi(e)
EX_cbl1(e)
Reaction Name
Calcium exchange
Chloride exchange
CO2 exchange
Co2+ exchange
Cu2+ exchange
Fe2+ exchange
Fe3+ exchange
H+ exchange
H2O exchange
K+ exchange
Mg exchange
Mn2+ exchange
Molybdate exchange
Sodium exchange
tungstate exchange
Zinc exchange
Ni2+ exchange
Selenate exchange
selenite exchange
Sulfate exchange
Ammonia exchange
Phosphate exchange
Cob(I)alamin exchange
BIE 5500/6500
H. Scott Hinton, 2015
Formula
Lower Bound
ca2[e] <=>
-1000
cl[e] <=>
-1000
co2[e] <=>
-1000
cobalt2[e] <=>
-1000
cu2[e] <=>
-1000
fe2[e] <=>
-1000
fe3[e] <=>
-1000
h[e] <=>
-1000
h2o[e] <=>
-1000
k[e] <=>
-1000
mg2[e] <=>
-1000
mn2[e] <=>
-1000
mobd[e] <=>
-1000
na1[e] <=>
-1000
tungs[e] <=>
-1000
zn2[e] <=>
-1000
ni2[e] <=>
-1000
sel[e] <=>
-1000
slnt[e] <=>
-1000
so4[e] <=>
-1000
nh4[e] <=>
-1000
pi[e] <=>
-1000
cbl1[e] <=>
-0.01
-58-
Upper Bound
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-59-
Ethanol Production
Flux Through
Exchange Reactions
Biomass
EX_ca2(e)
EX_cl(e)
EX_co2(e)
EX_cobalt2(e)
EX_cu2(e)
EX_etoh(e)
EX_fe2(e)
EX_fe3(e)
EX_glc(e)
EX_glyclt(e)
EX_h(e)
EX_h2o(e)
EX_k(e)
EX_lac-D(e)
EX_meoh(e)
EX_mg2(e)
EX_mn2(e)
EX_mobd(e)
EX_nh4(e)
EX_ni2(e)
EX_pi(e)
EX_so4(e)
EX_succ(e)
EX_zn2(e)
0.246761
-0.00128439
-0.00128439
6.90492
-6.16902e-06
-0.000174953
6.41879
-0.00203652
-0.00192671
-20
0.000165083
32.3664
7.08385
-0.048166
29.9334
4.93522e-07
-0.00214065
-0.000170512
-3.18322e-05
-2.66522
-7.97038e-05
-0.238033
-0.0622356
0.0817924
-8.41455e-05
Utah State University
(GlucoseEthanol_Mutant_iJO1366.m & GlucoseEthanol_multiProductionEnvelope_iJO1366.m)
Barely Coupled
• Picture of ethanol
producing map in 1260
Assumptions:
Aerobic
Glucose = -20
Knockouts (OptKnock):
'PFL','PPC','PPKr'
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
Flux Through
Exchange Reactions
Amino Acid
Reduced Costs
Amino Acid
Flux Values &
Reduced Costs
for Ethanol Production
in M9 Minimal Media
(GlucoseEthonal_ReducedCosts_iJO1366)
Biomass
EX_ca2(e)
EX_cl(e)
EX_co2(e)
EX_cobalt2(e)
EX_cu2(e)
EX_etoh(e)
EX_fe2(e)
EX_fe3(e)
EX_glc(e)
EX_glyclt(e)
EX_h(e)
EX_h2o(e)
EX_k(e)
EX_lac-D(e)
EX_meoh(e)
EX_mg2(e)
EX_mn2(e)
EX_mobd(e)
EX_nh4(e)
EX_ni2(e)
EX_pi(e)
EX_so4(e)
EX_succ(e)
EX_zn2(e)
0.246761
-0.00128439
-0.00128439
6.90492
-6.16902e-06
-0.000174953
6.41879
-0.00203652
-0.00192671
-20
0.000165083
32.3664
7.08385
-0.048166
29.9334
4.93522e-07
-0.00214065
-0.000170512
-3.18322e-05
-2.66522
-7.97038e-05
-0.238033
-0.0622356
0.0817924
-8.41455e-05
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-60-
-1
-1.83333
-1
-1
-0.833333
-1.5
-1.5
-0.5
0
0
0
0
0
0
-0.5
-0.833333
-1.33333
-0.833333
0
0
Objective Function = EX_etoh(e)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-61-
Impact of Additional L-Arginine on Biliverdin Production
(Biliverdin_Robustness_iJO1366.m & Biliverdin_Mutants_iJO1366.m)
'PFL‘, 'PPC‘, 'PPKr'
EX_arg-L(e) Lower Bound = 0
Biomass
EX_etoh(e)
EX_glc(e)
EX_lac-D(e)
EX_nh4(e)
EX_succ(e)
Utah State University
0.246761
6.41879
-20
29.9334
-2.66522
0.0817924
'PFL‘, 'PPC‘, 'PSERT'
EX_arg-L(e) Lower Bound = -1
Biomass
EX_arg-L(e)
EX_etoh(e)
EX_glc(e)
EX_lac-D(e)
EX_nh4(e)
EX_succ(e)
0.148636
-1
0
-20
28.0093
2.39461
0.889455
BIE 5500/6500
'ARGDC‘, 'PFL‘, 'PSP_L'
EX_arg-L(e) Lower Bound = -20
Biomass
EX_ac(e)
EX_arg-L(e)
EX_etoh(e)
EX_glc(e)
EX_lac-D(e)
EX_nh4(e)
EX_succ(e)
0.412812
2.70698
-2.77733
0
-20
37.1847
6.65062
0.136832
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-62-
Production Envelopes of Additional L-Arginine for Ethanol Production
(Biliverdin_Robustness_iJO1366.m & Biliverdin_Mutants_iJO1366.m)
'ARGDC‘, 'PFL‘, 'PSP_L'
'PFL‘, 'PPC‘, 'PPKr'
'PFL‘, 'PPC‘, 'PSERT'
EX_arg-L(e) Lower Bound = 0
EX_arg-L(e) Lower Bound = -1
Biomass
EX_etoh(e)
EX_glc(e)
EX_lac-D(e)
EX_nh4(e)
EX_succ(e)
Utah State University
0.246761
6.41879
-20
29.9334
-2.66522
0.0817924
Biomass
EX_arg-L(e)
EX_etoh(e)
EX_glc(e)
EX_lac-D(e)
EX_nh4(e)
EX_succ(e)
0.148636
-1
0
-20
28.0093
2.39461
0.889455
BIE 5500/6500
EX_arg-L(e) Lower Bound = -20
Biomass
EX_ac(e)
EX_arg-L(e)
EX_etoh(e)
EX_glc(e)
EX_lac-D(e)
EX_nh4(e)
EX_succ(e)
0.412812
2.70698
-2.77733
0
-20
37.1847
6.65062
0.136832
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-63-
Biliverdin Production
(Biliverdin_Mutants_iJO1366)
Flux Through
Exchange Reactions
Biomass
EX_ca2(e)
EX_cl(e)
EX_co2(e)
EX_cobalt2(e)
EX_cu2(e)
EX_fe2(e)
EX_glc(e)
EX_h(e)
EX_h2o(e)
EX_k(e)
EX_meoh(e)
EX_mg2(e)
EX_mn2(e)
EX_mobd(e)
EX_nh4(e)
EX_ni2(e)
EX_o2(e)
EX_pi(e)
EX_so4(e)
EX_zn2(e)
EX_biliverdin(e)
EX_co(e)
0.103363
-0.000538004
-0.000538004
14.9571
-2.58407e-06
-7.32843e-05
-0.00166011
-10
8.14972
45.2869
-0.0201757
2.06726e-07
-0.000896673
-7.14238e-05
-1.33338e-05
-5.9164
-3.33862e-05
-12.3366
-0.0997071
-0.0260692
-3.52468e-05
1.2
1.2
Utah State University
Assume a non-regulated
enzyme produced by a plasmid
EX_biliverdin(e) LB = 1.2
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-64-
L-Glutamate Metabolite Connectivity
ecoli_iJO1366 Model
L-Glutamate can feed up
to 49 different reactions
VisualizeGlutamate_iJO1366.m
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
Biliverdin Production
(Biliverdin_AminoAcid_ReducedCosts_iJO1366)
Flux Through
Exchange Reactions
EX_co2(e)
EX_glc(e)
EX_h(e)
EX_h2o(e)
EX_nh4(e)
EX_o2(e)
EX_biliverdin(e)
EX_co(e)
14.7976
-10
7.97689
45.3757
-5.31792
-12.1387
1.32948
1.32948
H. Scott Hinton, 2015
-65-
Note: Can’t produce more
biliverdin than the
induced enzymes from the
plasmids can process!
Amino Acid
Reduced Costs
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-0.0646425
-0.139079
-0.0705191
-0.0705191
-0.0528893
-0.111655
-0.110676
-0.0381978
0
0
0
0
0
0
-0.109696
-0.0558276
-0.0950049
-0.0568071
0
0
Objective Function = EX_biliverdin(e)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-66-
Impact of Additional L-Glutamate on Biliverdin Production
(Biliverdin_Robustness_iJO1366.m & Biliverdin_Mutants_iJO1366.m)
EX_biliverdin(e) ≥ 1.2
EX_glu-L(e) Lower Bound = 0
Biomass
EX_glc(e)
EX_nh4(e)
EX_o2(e)
EX_biliverdin(e)
EX_co(e)
Utah State University
0.103363
-10
-5.9164
-12.3366
1.2
1.2
EX_biliverdin(e) ≥ 1.2
EX_glu-L(e) Lower Bound = -0.1859
Biomass
EX_glc(e)
EX_glu-L(e)
EX_nh4(e)
EX_o2(e)
EX_biliverdin(e)
EX_co(e)
0.119785
-10
-0.185878
-5.9079
-12.4638
1.2
1.2
BIE 5500/6500
EX_biliverdin(e) ≥ 1.2
EX_glu-L(e) Lower Bound = -20
Biomass
EX_ac(e)
EX_glc(e)
EX_glu-L(e)
EX_nh4(e)
EX_o2(e)
EX_biliverdin(e)
EX_co(e)
0.952339
22.8365
-10
-20
4.91396
-20
1.2
1.2
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-67-
PHB Production
(PHB_Mutants_iJO1366)
Flux Through
Exchange Reactions
Biomass
EX_ca2(e)
EX_cl(e)
EX_co2(e)
EX_cobalt2(e)
EX_cu2(e)
EX_fe2(e)
EX_glc(e)
EX_h(e)
EX_h2o(e)
EX_k(e)
EX_meoh(e)
EX_mg2(e)
EX_mn2(e)
EX_mobd(e)
EX_nh4(e)
EX_ni2(e)
EX_o2(e)
EX_pi(e)
EX_so4(e)
EX_zn2(e)
DM_phb[c]
0.206854
-0.00107667
-0.00107667
11.509
-5.17134e-06
-0.000146659
-0.00332228
-10
1.90062
26.9719
-0.0403764
4.13707e-07
-0.00179445
-0.000142936
-2.66841e-05
-2.23419
-6.68137e-05
-6.06759
-0.199537
-0.0521705
-7.05371e-05
10
Utah State University
Acetyl-CoA
PHB_Robustness_iJO1366.m
Assume a non-regulated
enzyme produced by a plasmid
BIE 5500/6500
PHB Pathway
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-68-
PHB Production
(PHB_AminoAcid_ReducedCosts_iJO1366)
Flux Through
Exchange Reactions
EX_co2(e)
EX_glc(e)
EX_h2o(e)
EX_o2(e)
DM_phb[c]
Acetyl-CoA
PHB Pathway
Utah State University
10.3513
-10
22.7635
-4.14522
12.4122
Amino Acid
Reduced Costs
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-0.608696
-1.14783
-0.652174
-0.652174
-0.504348
-1.0087
-1
-0.347826
0
0
0
0
0
0
-0.991304
-0.530435
-0.869565
-0.53913
0
0
Objective Function = DM_phb[c]
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-69-
Impact of Additional L-Arginine on PHB Production
(Biliverdin_Robustness_iJO1366.m & Biliverdin_Mutants_iJO1366.m)
DM_phb[c] ≥ 10
EX_arg-L(e) Lower Bound = 0
Biomass
EX_glc(e)
EX_nh4(e)
EX_o2(e)
DM_phb[c]
Utah State University
0.206854
-10
-2.23419
-6.06759
10
DM_phb[c] ≥ 10
EX_arg-L(e) Lower Bound = -1
Biomass
EX_arg-L(e)
EX_glc(e)
EX_nh4(e)
EX_o2(e)
DM_phb[c]
0.30366
-1
-10
0.720224
-7.38727
10
BIE 5500/6500
DM_phb[c] ≥ 10
EX_arg-L(e) Lower Bound = -20
Biomass
EX_ac(e)
EX_arg-L(e)
EX_for(e)
EX_glc(e)
EX_nh4(e)
EX_o2(e)
DM_phb[c]
1.06729
9.52367
-20
0.444941
-10
39.6441
-20
10
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-70-
Spider Silk Production
(Spider_Silk_AminoAcid_ReducedCosts_iJO1366)
Flux Through
Exchange Reactions
EX_co2(e)
EX_glc(e)
EX_h(e)
EX_h2o(e)
EX_nh4(e)
EX_o2(e)
EX_so4(e)
DM_flys3[c]
16.6918
-10
13.1856
36.6126
-13.198
-19.0627
-0.0247617
0.0123808
Amino Acid
Reduced Costs
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-0.000584658
-0.00110235
-0.000661926
-0.000661926
-0.00118735
-0.000958118
-0.00094524
-0.000388914
-0.00134446
-0.00165353
0
-0.00154793
-0.00188018
0
-0.00119507
-0.000558902
-0.00096327
-0.000491937
-0.00215577
0
Objective Function = DM_flys3[c]
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-71-
Impact of Three Amino Acids to Spider Silk Production
(Spider_Silk_Robustness_iJO1366.m & Spider_Silk_Mutants_iJO1366.m)
DM_flys3 [c] ≥ 0.0034
EX_ala-L(e) Lower Bound = 0
EX_pro-L(e) Lower Bound = 0
EX_ser-L(e) Lower Bound = 0
Biomass
EX_glc(e)
EX_nh4(e)
EX_o2(e)
DM_flys3[c]
Utah State University
0.716261
-10
-11.3255
-17.9371
0.00336705
DM_flys3 [c] ≥ 0.0034
DM_flys3 [c] ≥ 0.0034
EX_ala-L(e) Lower Bound = -1
EX_pro-L(e) Lower Bound = -1
EX_ser-L(e) Lower Bound = -1
Biomass
EX_ac(e)
EX_ala-L(e)
EX_for(e)
EX_glc(e)
EX_nh4(e)
EX_o2(e)
EX_pro-L(e)
EX_ser-L(e)
DM_flys3[c]
0.871302
0.716336
-1
1.61531
-10
-10.0001
-20
-1
-1
0.00336705
BIE 5500/6500
EX_ala-L(e) Lower Bound = -20
EX_pro-L(e) Lower Bound = -20
EX_ser-L(e) Lower Bound = -20
Biomass
EX_ac(e)
EX_ala-L(e)
EX_for(e)
EX_glc(e)
EX_nh4(e)
EX_o2(e)
EX_pro-L(e)
EX_ser-L(e)
DM_flys3[c]
1.3575
42.9806
-20
33.8487
-10
26.4335
-20
-4.6849
-20
0.00336705
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-72-
K12 Media
K12 Medium:
• Growth in K12 media was simulated by
adjusting lower bounds of exchange
reactions to correspond to media
conditions
K12 trace metal solution:
Utah State University
BIE 5500/6500
Chemical
KH2PO4
K2HPO4.3H2O
(NH4)2HPO4
Yeast Extract
Glucose
MgSO4.7H2O
Thiamine
K12 trace metal
Concentration
2 g/L
4 g/L
5 g/L
5 g/L
25 g/L
0.5 g/L
2.5 mg/L
5 ml/L
NaCl
ZnSO4.7H2O
MnCl2.4H2O
FeCl3.6H2O
CuSO4.5H2O
H3BO3
NaMoO4.2H2O
6N H2SO4
5 g/L
1 g/L
4 g/L
4.75 g/L
0.4 g/L
0.575 g/L
0.5 g/L
12.5 ml/L
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-73-
Calculated Metabolite Uptake Rates for K-12 Media
Metabolite
Glucose
Ammonium
Phosphate
Potassium
Sulfate
Chloride
Copper
Iron (III)
Magnesium
Manganese
Molybdate
Sodium
Thiamine
Zinc
Alanine
Arginine
Asparagine
MW (g/mol) g/L in media mmol/L in
media
180.16
25
138.7655417
18.03851
1.365829484 75.71742258
94.9714
6.655736264 70.08147994
39.0983
1.945099309 49.74894838
96.07
0.203323529 2.11641021
35.453
0.062050364 1.750214773
63.546
0.000509009 0.008010093
55.845
0.00490684 0.087865335
24.305
0.049304203 2.028562155
54.938044
0.005551956 0.101058487
95.95
0.001826052 0.019031286
22.98976928 0.029775544 1.295164976
265.35
0.0025
0.009421519
65.38
0.001136847 0.017388304
89.09
0.225
2.525535975
174.2
0.145
0.832376579
132.12
0.16
1.211020285
Utah State University
Lower bound
(mmol gDW-1h-1)
-11
-6.002150375
-5.555386948
-3.943619038
-0.167768684
-0.138740225
-0.000634963
-0.00696512
-0.160804933
-0.008010947
-0.001508618
-0.102668246
-0.000746848
-0.001378378
-0.200200247
-0.065982824
-0.095998062
Metabolite
MW (g/mol) g/L in media mmol/L in
media
Aspartic acid 133.1
0.16
1.202103681
Cysteine
121.16
0.02
0.165070981
Glutamine
146.14
0.345
2.360749966
Glutamic Acid 147.13
0.345
2.344865085
Glycine
75.07
0.135
1.798321567
Histidine
155.15
0.06
0.386722527
Isoleucine
131.17
0.145
1.105435694
Leucine
131.17
0.21
1.600975833
Lysine
146.19
0.22
1.504890895
Methionine
149.21
0.045
0.301588365
Phenylalanine 165.19
0.12
0.726436225
Proline
115.13
0.115
0.998870842
Serine
105.09
0.13
1.237034922
Threonine
119.12
0.135
1.133310947
Tyrosine
181.19
0.09
0.496716154
Valine
117.15
0.165
1.408450704
Lower bound
(mmol gDW-1h-1)
-0.09529124
-0.013085243
-0.187137594
-0.185878393
-0.14255367
-0.030655649
-0.08762833
-0.126909995
-0.119293303
-0.02390703
-0.05758489
-0.079180891
-0.098060253
-0.089838012
-0.039374888
-0.111648451
Sarah Allred, “Metabolic Modeling of Spider Silk Production in E. coli,” MS Thesis, USU, 2014
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-74-
Impact of Limiting Amino Acids Uptake for Ethanol Production
% GlucoseEthanol_AminoAcid_ReducedCosts_iaf1260e.m
clear;
changeCobraSolver('glpk','all');
% Set constraints
model=readCbModel('ecoli_iaf1260');
model = changeRxnBounds(model, {'EX_o2(e)', 'EX_glc(e)'}, [-0 -20],
'l');
% model = changeObjective(model,'Ec_biomass_iAF1260_core_59p81M');
model = changeObjective(model,'EX_etoh(e)');
% Set
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
uptake values for amino acids;
= changeRxnBounds(model,'EX_ala_L(e)',-0.200200247,'l');
= changeRxnBounds(model,'EX_arg_L(e)',-0.065982824,'l');
= changeRxnBounds(model,'EX_asn_L(e)',-0.095998062,'l');
= changeRxnBounds(model,'EX_asp_L(e)',-0.09529124,'l');
= changeRxnBounds(model,'EX_cys_L(e)',-0.013085243,'l');
= changeRxnBounds(model,'EX_gln_L(e)',-0.187137594,'l');
= changeRxnBounds(model,'EX_glu_L(e)',-0.185878393,'l');
= changeRxnBounds(model,'EX_gly(e)',-0.14255367,'l');
= changeRxnBounds(model,'EX_his_L(e)',-0.030655649,'l');
= changeRxnBounds(model,'EX_ile_L(e)',-0.08762833,'l');
= changeRxnBounds(model,'EX_leu_L(e)',-0.126909995,'l');
= changeRxnBounds(model,'EX_lys_L(e)',-0.119293303,'l');
= changeRxnBounds(model,'EX_met_L(e)',-0.02390703,'l');
= changeRxnBounds(model,'EX_phe_L(e)',-0.05758489,'l');
= changeRxnBounds(model,'EX_pro_L(e)',-0.079180891,'l');
= changeRxnBounds(model,'EX_ser_L(e)',-0.098060253,'l');
= changeRxnBounds(model,'EX_thr_L(e)',-0.089838012,'l');
= changeRxnBounds(model,'EX_tyr_L(e)',-0.039374888,'l');
= changeRxnBounds(model,'EX_val_L(e)',-0.111648451,'l');
Utah State University
% Set
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
uptake values for minerals;
= changeRxnBounds(model,'EX_ca2(e)',-0.00237348,'l');
= changeRxnBounds(model,'EX_cobalt2(e)',-1.14e-05,'l');
= changeRxnBounds(model,'EX_ni2(e)',-0.000147288,'l');
= changeRxnBounds(model,'EX_nh4(e)',-6.002150375,'l');
= changeRxnBounds(model,'EX_pi(e)',-5.555386948,'l');
= changeRxnBounds(model,'EX_k(e)',-3.943619038,'l');
= changeRxnBounds(model,'EX_so4(e)',-0.167768684,'l');
= changeRxnBounds(model,'EX_cl(e)',-0.138740225,'l');
= changeRxnBounds(model,'EX_cu2(e)',-0.000634963,'l');
= changeRxnBounds(model,'EX_fe3(e)',-0.00696512,'l');
= changeRxnBounds(model,'EX_mg2(e)',-0.160804933,'l');
= changeRxnBounds(model,'EX_mn2(e)',-0.008010947,'l');
= changeRxnBounds(model,'EX_mobd(e)',-0.001508618,'l');
= changeRxnBounds(model,'EX_na1(e)',-0.102668246,'l');
= changeRxnBounds(model,'EX_thm(e)',-0.000746848,'l');
= changeRxnBounds(model,'EX_zn2(e)',-0.001378378,'l');
aminoAcids = {'EX_ala_L(e)','EX_arg_L(e)', 'EX_asn_L(e)', 'EX_asp_L(e)',...
'EX_cys_L(e)', 'EX_gln_L(e)', 'EX_glu_L(e)', 'EX_gly(e)', 'EX_his_L(e)', ...
'EX_ile_L(e)', 'EX_leu_L(e)', 'EX_lys_L(e)', 'EX_met_L(e)', 'EX_phe_L(e)', ...
'EX_pro_L(e)', 'EX_ser_L(e)', 'EX_thr_L(e)', 'EX_trp_L(e)', 'EX_tyr_L(e)', ...
'EX_val_L(e)'};
% Perform FBA
FBAsolution = optimizeCbModel(model,'max')
printFluxVector(model,FBAsolution.x, true, true)
'Amino acid reduced costs'
rxnID = findRxnIDs(model, aminoAcids);
printLabeledData(aminoAcids',FBAsolution.w(rxnID))
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
Flux Through
Exchange Reactions
Amino Acid
Reduced Costs
Amino Acid
Flux Values &
Reduced Costs
for Ethanol Production
in M9 Minimal Media
(GlucoseEthonal_ReducedCosts_iJO1366)
Biomass
EX_ca2(e)
EX_cl(e)
EX_co2(e)
EX_cobalt2(e)
EX_cu2(e)
EX_etoh(e)
EX_fe2(e)
EX_fe3(e)
EX_glc(e)
EX_glyclt(e)
EX_h(e)
EX_h2o(e)
EX_k(e)
EX_lac-D(e)
EX_meoh(e)
EX_mg2(e)
EX_mn2(e)
EX_mobd(e)
EX_nh4(e)
EX_ni2(e)
EX_pi(e)
EX_so4(e)
EX_succ(e)
EX_zn2(e)
0.246761
-0.00128439
-0.00128439
6.90492
-6.16902e-06
-0.000174953
6.41879
-0.00203652
-0.00192671
-20
0.000165083
32.3664
7.08385
-0.048166
29.9334
4.93522e-07
-0.00214065
-0.000170512
-3.18322e-05
-2.66522
-7.97038e-05
-0.238033
-0.0622356
0.0817924
-8.41455e-05
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-75-
-1
-1.83333
-1
-1
-0.833333
-1.5
-1.5
-0.5
0
0
0
0
0
0
-0.5
-0.833333
-1.33333
-0.833333
0
0
Objective Function = EX_etoh(e)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
Flux Through
Exchange Reactions
Amino Acid
Reduced Costs
Amino Acid
Flux Values &
Reduced Costs for
Ethanol Production in
Calculated K-12 Media
(GlucoseEthonal_AminoAcid_ReducedCosts_iaf1260)
Objective Function = EX_etoh(e)
Utah State University
EX_15dap(e)
EX_ala_L(e)
EX_arg_L(e)
EX_asn_L(e)
EX_asp_L(e)
EX_co2(e)
EX_cys_L(e)
EX_etoh(e)
EX_fe2(e)
EX_fe3(e)
EX_glc(e)
EX_gln_L(e)
EX_glu_L(e)
EX_gly(e)
EX_h2o(e)
EX_h2s(e)
EX_h(e)
EX_lys_L(e)
EX_nh4(e)
EX_pro_L(e)
EX_ser_L(e)
EX_thr_L(e)
BIE 5500/6500
0.119293
-0.2002
-0.0659828
-0.0959981
-0.0952912
42.0184
-0.0130852
41.3615
0.00696512
-0.00696512
-20
-0.187138
-0.185878
-0.142554
-1.77129
0.0130852
-1.98611
-0.119293
1.65859
-0.00348256
-0.0980603
-0.089838
EX_ala_L(e)
EX_arg_L(e)
EX_asn_L(e)
EX_asp_L(e)
EX_cys_L(e)
EX_gln_L(e)
EX_glu_L(e)
EX_gly(e)
EX_his_L(e)
EX_ile_L(e)
EX_leu_L(e)
EX_lys_L(e)
EX_met_L(e)
EX_phe_L(e)
EX_pro_L(e)
EX_ser_L(e)
EX_thr_L(e)
EX_trp_L(e)
EX_tyr_L(e)
EX_val_L(e)
-76-
-1
-1.83333
-1
-1
-0.833333
-1.5
-1.5
-0.5
0
0
0
0
0
0
0
-0.833333
-1.33333
-0.833333
0
0
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
Biliverdin Production
(Biliverdin_AminoAcid_ReducedCosts_iJO1366)
Flux Through
Exchange Reactions
EX_co2(e)
EX_glc(e)
EX_h(e)
EX_h2o(e)
EX_nh4(e)
EX_o2(e)
EX_biliverdin(e)
EX_co(e)
14.7976
-10
7.97689
45.3757
-5.31792
-12.1387
1.32948
1.32948
H. Scott Hinton, 2015
-77-
Note: Can’t produce more
biliverdin than the
induced enzymes from the
plasmids can process!
Amino Acid
Reduced Costs
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-0.0646425
-0.139079
-0.0705191
-0.0705191
-0.0528893
-0.111655
-0.110676
-0.0381978
0
0
0
0
0
0
-0.109696
-0.0558276
-0.0950049
-0.0568071
0
0
Objective Function = EX_biliverdin(e)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
Biliverdin Production
(Biliverdin_AminoAcid_ReducedCosts_iJO1366)
Flux Through
Exchange Reactions
EX_15dap(e)
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_co2(e)
EX_cys-L(e)
EX_glc(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_h(e)
EX_h2o(e)
EX_h2s(e)
EX_lys-L(e)
EX_nh4(e)
EX_o2(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_biliverdin(e)
EX_co(e)
0.119293
-0.2002
-0.0659828
-0.0959981
-0.0952912
16.3765
-0.0130852
-10
-0.187138
-0.185878
-0.142554
6.533
46.7738
0.0130852
-0.119293
-4.00022
-12.8919
-0.0791809
-0.0980603
-0.089838
1.43363
1.43363
Utah State University
H. Scott Hinton, 2015
-78-
Note: Can’t produce more
biliverdin than the
induced enzymes from the
plasmids can process!
Amino Acid
Reduced Costs
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-0.0646425
-0.123408
-0.0705191
-0.0705191
-0.0528893
-0.110676
-0.110676
-0.036239
0
0
0
0
0
0
-0.109696
-0.0558276
-0.0920666
-0.0568071
0
0
Objective Function = EX_biliverdin(e)
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-79-
PHB Production
(PHB_AminoAcid_ReducedCosts_iJO1366)
Flux Through
Exchange Reactions
EX_co2(e)
EX_glc(e)
EX_h2o(e)
EX_o2(e)
DM_phb[c]
Acetyl-CoA
PHB Pathway
Utah State University
10.3513
-10
22.7635
-4.14522
12.4122
Amino Acid
Reduced Costs
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-0.608696
-1.14783
-0.652174
-0.652174
-0.504348
-1.0087
-1
-0.347826
0
0
0
0
0
0
-0.991304
-0.530435
-0.869565
-0.53913
0
0
Objective Function = DM_phb[c]
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-80-
PHB Production
(PHB_AminoAcid_ReducedCosts_iJO1366)
Flux Through
Exchange Reactions
Acetyl-CoA
PHB Pathway
Utah State University
EX_15dap(e)
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_co2(e)
EX_cys-L(e)
EX_glc(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_h(e)
EX_h2o(e)
EX_h2s(e)
EX_lys-L(e)
EX_nh4(e)
EX_o2(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
DM_phb[c]
0.119293
-0.2002
-0.0659828
-0.0959981
-0.0952912
11.6396
-0.0130852
-10
-0.187138
-0.185878
-0.142554
-2.06877
22.4335
0.0130852
-0.119293
1.73429
-4.33723
-0.0791809
-0.0980603
-0.089838
13.3701
Amino Acid
Reduced Costs
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-0.608696
-1.11304
-0.652174
-0.652174
-0.504348
-1
-1
-0.347826
0
0
0
0
0
0
-0.991304
-0.530435
-0.869565
-0.53913
0
0
Objective Function = DM_phb[c]
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-81-
Spider Silk Production
(Spider_Silk_AminoAcid_ReducedCosts_iJO1366)
Flux Through
Exchange Reactions
EX_co2(e)
EX_glc(e)
EX_h(e)
EX_h2o(e)
EX_nh4(e)
EX_o2(e)
EX_so4(e)
DM_flys3[c]
16.6918
-10
13.1856
36.6126
-13.198
-19.0627
-0.0247617
0.0123808
Amino Acid
Reduced Costs
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-0.000584658
-0.00110235
-0.000661926
-0.000661926
-0.00118735
-0.000958118
-0.00094524
-0.000388914
-0.00134446
-0.00165353
0
-0.00154793
-0.00188018
0
-0.00119507
-0.000558902
-0.00096327
-0.000491937
-0.00215577
0
Objective Function = DM_flys3[c]
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
Flux Through
Exchange Reactions
Spider Silk Production
(Spider_Silk_AminoAcid_ReducedCosts_iJO1366)
Utah State University
H. Scott Hinton, 2015
EX_15dap(e)
EX_ac(e)
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_co2(e)
EX_cys-L(e)
EX_for(e)
EX_glc(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_h(e)
EX_h2o(e)
EX_h2s(e)
EX_his-L(e)
EX_ile-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_nh4(e)
EX_o2(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_tyr-L(e)
DM_flys3[c]
BIE 5500/6500
0.111878
6.38362
-0.2002
-0.0659828
-0.0959981
-0.0952912
11.183
-0.0130852
15.9244
-10
-0.187138
-0.185878
-0.142554
28.0127
20.113
0.0130852
-0.0306556
-0.00741544
-0.119293
-0.0148309
-6.00215
-20
-0.0791809
-0.0980603
-0.089838
-0.0393749
0.00741544
-82-
Amino Acid
Reduced Costs
EX_ala-L(e)
EX_arg-L(e)
EX_asn-L(e)
EX_asp-L(e)
EX_cys-L(e)
EX_gln-L(e)
EX_glu-L(e)
EX_gly(e)
EX_his-L(e)
EX_ile-L(e)
EX_leu-L(e)
EX_lys-L(e)
EX_met-L(e)
EX_phe-L(e)
EX_pro-L(e)
EX_ser-L(e)
EX_thr-L(e)
EX_trp-L(e)
EX_tyr-L(e)
EX_val-L(e)
-0.000942507
-0.00377003
-0.00188501
-0.000942507
-0.000942507
-0.00188501
-0.000942507
-0.000942507
-0.00282752
0
0
0
0
0
-0.000942507
-0.000942507
-0.000942507
-0.000942507
-0.000942507
0
Objective Function = DM_flys3[c]
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-83-
Impact of Uptaking Minerals
% GlucoseEthanol_AminoAcid_ReducedCosts_iaf1260e.m
clear;
changeCobraSolver('glpk','all');
% Set constraints
model=readCbModel('ecoli_iaf1260');
model = changeRxnBounds(model, {'EX_o2(e)', 'EX_glc(e)'}, [-0 -20],
'l');
% model = changeObjective(model,'Ec_biomass_iAF1260_core_59p81M');
model = changeObjective(model,'EX_etoh(e)');
% Set
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
uptake values for amino acids;
= changeRxnBounds(model,'EX_ala_L(e)',-0.200200247,'l');
= changeRxnBounds(model,'EX_arg_L(e)',-0.065982824,'l');
= changeRxnBounds(model,'EX_asn_L(e)',-0.095998062,'l');
= changeRxnBounds(model,'EX_asp_L(e)',-0.09529124,'l');
= changeRxnBounds(model,'EX_cys_L(e)',-0.013085243,'l');
= changeRxnBounds(model,'EX_gln_L(e)',-0.187137594,'l');
= changeRxnBounds(model,'EX_glu_L(e)',-0.185878393,'l');
= changeRxnBounds(model,'EX_gly(e)',-0.14255367,'l');
= changeRxnBounds(model,'EX_his_L(e)',-0.030655649,'l');
= changeRxnBounds(model,'EX_ile_L(e)',-0.08762833,'l');
= changeRxnBounds(model,'EX_leu_L(e)',-0.126909995,'l');
= changeRxnBounds(model,'EX_lys_L(e)',-0.119293303,'l');
= changeRxnBounds(model,'EX_met_L(e)',-0.02390703,'l');
= changeRxnBounds(model,'EX_phe_L(e)',-0.05758489,'l');
= changeRxnBounds(model,'EX_pro_L(e)',-0.079180891,'l');
= changeRxnBounds(model,'EX_ser_L(e)',-0.098060253,'l');
= changeRxnBounds(model,'EX_thr_L(e)',-0.089838012,'l');
= changeRxnBounds(model,'EX_tyr_L(e)',-0.039374888,'l');
= changeRxnBounds(model,'EX_val_L(e)',-0.111648451,'l');
Utah State University
% Set
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
model
uptake values for minerals;
= changeRxnBounds(model,'EX_ca2(e)',-0.00237348,'l');
= changeRxnBounds(model,'EX_cobalt2(e)',-1.14e-05,'l');
= changeRxnBounds(model,'EX_ni2(e)',-0.000147288,'l');
= changeRxnBounds(model,'EX_nh4(e)',-6.002150375,'l');
= changeRxnBounds(model,'EX_pi(e)',-5.555386948,'l');
= changeRxnBounds(model,'EX_k(e)',-3.943619038,'l');
= changeRxnBounds(model,'EX_so4(e)',-0.167768684,'l');
= changeRxnBounds(model,'EX_cl(e)',-0.138740225,'l');
= changeRxnBounds(model,'EX_cu2(e)',-0.000634963,'l');
= changeRxnBounds(model,'EX_fe3(e)',-0.00696512,'l');
= changeRxnBounds(model,'EX_mg2(e)',-0.160804933,'l');
= changeRxnBounds(model,'EX_mn2(e)',-0.008010947,'l');
= changeRxnBounds(model,'EX_mobd(e)',-0.001508618,'l');
= changeRxnBounds(model,'EX_na1(e)',-0.102668246,'l');
= changeRxnBounds(model,'EX_thm(e)',-0.000746848,'l');
= changeRxnBounds(model,'EX_zn2(e)',-0.001378378,'l');
minerals = {'EX_ca2(e)','EX_cobalt2(e)','EX_ni2(e)','EX_nh4(e)','EX_pi(e)',...
'EX_k(e)','EX_so4(e)', 'EX_cl(e)','EX_cu2(e)','EX_fe3(e)','EX_mg2(e)',...
'EX_mn2(e)','EX_mobd(e)','EX_na1(e)','EX_thm(e)','EX_zn2(e)'};
% Perform FBA
FBAsolution = optimizeCbModel(model,'max')
printFluxVector(model,FBAsolution.x, true, true)
'Mineral reduced costs'
rxnID = findRxnIDs(model, minerals);
printLabeledData(minerals',FBAsolution.w(rxnID))
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
Flux Through
Exchange Reactions
Mineral
Reduced Costs
Mineral
Flux Values &
Reduced Costsfor
Ethanol Production in
Calculated K-12 Media
(GlucoseEthonal_AminoAcid_ReducedCosts_iaf1260)
Utah State University
EX_15dap(e)
EX_ala_L(e)
EX_arg_L(e)
EX_asn_L(e)
EX_asp_L(e)
EX_co2(e)
EX_cys_L(e)
EX_etoh(e)
EX_fe2(e)
EX_fe3(e)
EX_glc(e)
EX_gln_L(e)
EX_glu_L(e)
EX_gly(e)
EX_h2o(e)
EX_h2s(e)
EX_h(e)
EX_lys_L(e)
EX_nh4(e)
EX_pro_L(e)
EX_ser_L(e)
EX_thr_L(e)
BIE 5500/6500
0.119293
-0.2002
-0.0659828
-0.0959981
-0.0952912
42.0184
-0.0130852
41.3615
0.00696512
-0.00696512
-20
-0.187138
-0.185878
-0.142554
-1.77129
0.0130852
-1.98611
-0.119293
1.65859
-0.00348256
-0.0980603
-0.089838
EX_ca2(e)
EX_cobalt2(e)
EX_ni2(e)
EX_nh4(e)
EX_pi(e)
EX_k(e)
EX_so4(e)
EX_cl(e)
EX_cu2(e)
EX_fe3(e)
EX_mg2(e)
EX_mn2(e)
EX_mobd(e)
EX_na1(e)
EX_thm(e)
EX_zn2(e)
-84-
0
0
0
0
0
0
0
0
0
-0.833333
0
0
0
0
0
0
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-85-
Ethanol Production with Limited Nutrients: Robustness Analysis
(GlucoseEthanol_Limited_Media_Mutant_iJO1366.m)
Unconstrained Media
Utah State University
Constrained Media
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-86-
Biliverdin Production Limitations
Uptake rate for some amino
acids and minerals not
sufficient for both cell
growth and bioproduct
production
Unconstrained Media
Desired
Operating
Point
Constrained Media
Biliverdin_Robustness_iJO1366.m
Utah State University
Biliverdin_Media_Robustness_iJO1366.m
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-87-
PHB Production Limitations
Uptake rate for some amino
acids and minerals are not
sufficient for both cell
growth and bioproduct
production
Unconstrained Media
Desired
Operating
Point
Constrained Media
PHB_Robustness_iJO1366.m
Utah State University
PHB_Media_Robustness_iJO1366.m
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-88-
Spider Silk Production Limitations
Uptake rate for some amino
acids and minerals is not
sufficient for both cell
growth and bioproduct
production
Desired
Operating
Point
Constrained Media
Unconstrained Media
Spider_Silk_Robustness_iJO1366.m
Utah State University
Spider_Silk_Media_Robustness_iJO1366.m
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-89-
Regulatory Issues
Regulatory constraints are not included in the constraint-based reconstructions.
Proline is self-limiting
http://biocyc.org/ECOLI/NEW-IMAGE?type=PATHWAY&object=PROSYN-PWY&detail-level=2
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
Plasmid Impact
• It is widely acknowledged that the metabolism of wildtype E. coli has evolved toward the maximum rate of
growth.
• There are evidences suggesting that the recombinant
E. coli cells bearing multicopy plasmids could be under an
altered physiological state. The introduction of plasmids
to the cell creates a metabolic burden effect leading to
retarded growth and changes in gene regulation, enzyme
activities, and metabolic flux.
Wild-Type
Plasmid
Bearing
H. Scott Hinton, 2015
-90-
The plasmid equation consists of the biosynthetic precursors
and energetic requirement for pOri2 plasmid DNA replication
and marker protein expression. The pOri2 (4,575 bp) requires
four nucleotide precursors for its replication based on an
approximated E. coli K-12 GC content of 51%:
2,315.0 dGTP + 2,315.0 dCTP + 2,260.1 dATP + 2,260.1 dTTP
-> plasmid
The biosynthetic precursor balance for the sole plasmid
encoded antibiotic marker protein (β-lactamase) combined
with energetic requirements of 4.306 mol ATP/mol amino
acids for its expression can be formulated as follows:
28 Ala + 3 Cys + 16 Asp + 20 Glu + 9 Phe + 21 Gly + 7His + 17
Ile + 11 Lys + 33 Leu + 10 Met + 8Asn + 14 Pro + 9Gln + 19 Arg
+ 17 Ser + 20 Thr + 16 Val + 4 Trp + 4 Tyr + 1,231.52 ATP
-> b-lactamase + 1,231.52 ADP + 1,231.52 PI
These equations are incorporated to formulate the plasmid
production rate on the basis of the experimentally
determined production rates of plasmid DNA and β-lactamase
protein, which is estimated as 3% of total cellular protein
from two-dimensional gel electrophoresis analysis.
Ow, D. S., D. Y. Lee, et al. (2009). "Identification of cellular objective for elucidating the physiological state of
plasmid-bearing Escherichia coli using genome-scale in silico analysis." Biotechnology progress 25(1): 61-67.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-91-
Review Questions
1.
In addition to the designated carbon source in a growth media what other components of the media
can act as carbon sources?
2.
What components in a growth media can impact the maximum bioproduct production?
3.
How is the M9 minimal media modeled?
4.
In the standard E.coli models (textbook, iAF1260, and iJO1366) what are the typical default lower
bounds for most amino acids and minerals? What are the default upper bounds?
5.
What relationship do the amino acids have with the biomass function?
6.
How can amino acid and mineral uptake rates impact growth rate and bioproduct production?
7.
How can reduced costs be used to understand the impact of an amino acid or mineral on bioproduct
production?
8.
Are regulatory constraints included in the standard E.coli constraint-based models?
9.
What impacts can be observed by adding a plasmid to a host cell?
10. How can plasmids be modeled?
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-92-
Lesson Outline
• Overview
• Strain Design for Bioproduct Production
• Media Optimization for Bioproduct Production
• Method of Minimization of Metabolic Adjustment (MOMA)
• Adaptive Laboratory Evolution
• New Potential Design Tools
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-93-
Do Cells Really Operate at the Calculated
Optimal State Of Bioproduct Production?
• It has been assumed that the mutant bacteria display an optimal
metabolic state.
• Unfortunately, mutants generated artificially in the laboratory are
generally not subjected to the same evolutionary pressure that
shaped the wild type. Thus, a mutant is likely to initially display a
suboptimal flux distribution that is somehow intermediate between
the wild-type optimum and the mutant optimum.
Optimal Production State
• A technique referred to as the method of minimization of metabolic
adjustment (MOMA), which is based on the same stoichiometric
constraints as FBA, but relaxes the assumption of optimal growth
flux for gene deletions.
• MOMA provides a mathematically tractable approximation for this
intermediate suboptimal state, based on the conjecture that the
mutant remains initially as close as possible.
Utah State University
BIE 5500/6500
Production Envelope
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-94-
Method of Minimization of Metabolic Adjustment (MOMA)
Segre, D., D. Vitkup, et al. (2002). "Analysis of optimality in natural and perturbed metabolic networks."
• Uses the same steady state flux
cone as FBA.
• Relaxes the assumption of
maximal optimal growth.
• MOMA searches the flux
distribution in the “mutant flux
space” which is closest to the
optimal flux distribution in the
“wild-type flux space.”
• Typically returns suboptimal
flux distribution between wild
type optimum and mutant
optimum
Segre, D., D. Vitkup, et al. (2002). "Analysis of
optimality in natural and perturbed metabolic networks."
Proceedings of the National Academy of Sciences of the
United States of America 99(23): 15112-15117.
Utah State University
Price, N. D., J. L. Reed, et al. (2004). "Genome-scale models of microbial cells: evaluating
the consequences of constraints." Nature reviews. Microbiology 2(11): 886-897.
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-95-
% EthanolProduction_GDLS_Mutants_MOMA.m
clear;
% Input the E.coli core model
model=readCbModel('ecoli_textbook');
% Set carbon source and oxygen uptake rates
model = changeRxnBounds(model,'EX_glc(e)',-5,'l');
model = changeRxnBounds(model,'EX_o2(e)',-20,'l');
model = changeObjective(model,'Biomass_Ecoli_core_N(w/GAM)_Nmet2');
FBAsolution = optimizeCbModel(model,'max',0,0);
modelWT = model;
% Knockout reactions
MOMA Example
model = changeRxnBounds(model,'NADH16',0,'b');
model = changeRxnBounds(model,'PTAr',0,'b');
model = changeRxnBounds(model,'TKT2',0,'b');
Mutantsolution = optimizeCbModel(model,'max',0,0);
modelMutant = model;
% MOMA calculation
[solutionDel,solutionWT,totalFluxDiff,solStatus] = MOMA(modelWT,modelMutant,'max','false')
printFluxVector(model, [FBAsolution.x,Mutantsolution.x solutionDel.x], true)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-96-
Flux Differences: Wild Type, GDLS, & MOMA
Reaction
ACALD
ACONTa
ACONTb
AKGDH
ALCD2x
ATPM
ATPS4r
Biomass
CO2t
CS
CYTBD
D_LACt2
ENO
ETOHt2r
EX_co2(e)
EX_etoh(e)
EX_for(e)
EX_glc(e)
EX_h2o(e)
EX_h(e)
WT
2.27E-13
3.44346
3.44346
2.99507
0
8.39
24.8712
0.415598
-12.314
3.44346
23.6671
0
7.58501
0
12.314
0
0
-5
15.3414
8.33689
GDLS
-7.38652
1.87357
1.87357
1.81911
-7.38652
8.39
0.094332
0.050473
-3.6298
1.87357
1.81911
0
9.76307
-7.38652
3.6298
7.38652
9.44925
-5
-3.38905
10.4617
MOMA
-5.38586
0.010514
0.010514
0
-5.38586
8.39
1.27156
0.009745
-5.36298
0.010514
0
-2.39504
7.88854
-5.38586
5.36298
5.38586
0.068288
-3.96714
0.048112
2.65882
Reaction
EX_lac_D(e)
EX_nh4(e)
EX_o2(e)
EX_pi(e)
FBA
FORti
FRD7
FUM
G6PDH2r
GAPD
GLCpts
GLNS
GLUDy
GND
H2Ot
ICDHyr
LDH_D
MDH
ME2
NADH16
WT
0
-2.26617
-11.8336
-1.52886
3.89814
0
0
2.99507
2.06541
8.20675
5
0.106268
-2.1599
2.06541
-15.3414
3.44346
0
2.99507
0
20.672
GDLS
0
-0.27522
-0.90956
-0.18568
4.92254
9.44925
0
1.81911
0.081752
9.83858
5
0.012906
-0.26232
0.081752
3.38905
1.87357
0
1.81911
0
0
MOMA
2.39504
-0.05314
0
-0.03585
3.95219
0.068288
1.49752
0
0.015785
7.90311
3.96714
0.002492
-0.05065
0.015785
-0.04811
0.010514
-2.39504
-0.13553
0.135527
0
Reaction
NADTRHD
NH4t
O2t
PDH
PFK
PFL
PGI
PGK
PGL
PGM
PIt2r
PPC
PYK
RPE
RPI
SUCDi
SUCOAS
TALA
TKT1
TKT2
TPI
WT
0
2.26617
11.8336
5.00103
3.89814
0
2.8494
-8.20675
2.06541
-7.58501
1.52886
1.19094
1.17834
1.07821
-0.9872
2.99507
-2.99507
0.614118
0.614118
0.464088
3.89814
GDLS
1.1172
0.275221
0.909557
0
4.92254
9.44925
4.9079
-9.83858
0.081752
-9.76307
0.185676
0.144636
4.59223
0.018221
-0.06353
1.81911
-1.81911
0.018221
0.018221
0
4.92254
MOMA
0
0.05314
0
5.36461
3.95219
0.068288
3.94936
-7.90311
0.015785
-7.88854
0.035851
0.163454
3.75288
0.003518
-0.01227
1.49752
0
0.003518
0.003518
0
3.95219
EthanolProduction_GDLS_Mutants_MOMA.m
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-97-
Flux Maps: Wild Type, GDLS, & MOMA
Wild Type
GDLS-Mutant
MOMA Result
nadh for TCA
X
X
q8, q8h2
Loop
X
X
EthanolProduction_WT.m
Utah State University
EthanolProduction_GDLS_Mutant.m
BIE 5500/6500
X
X
Lactate
EthanolProduction_GDLS_Mutants_MOMA.m
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-98-
Production Envelope – Ethanol Production Mutant
(0.050473, 7.3865)
FBA
Operating
Point
(0.009745, 5.38586)
MOMA
Operating
Point
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-99-
Review Questions
1. After a gene has been knockout or a new gene has been added to a
host cell does the maximum theoretical performance match the
laboratory results?
2. What Cobra function can be used to approximate the intermediate
suboptimal state of the modified host cell?
3. What is a wild-type cell/model? How does it differ from the mutant
cell/model?
4. Are the optimized flux values similar to the MOMA flux results?
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-100-
Lesson Outline
• Overview
• Strain Design for Bioproduct Production
• Media Optimization for Bioproduct Production
• Method of Minimization of Metabolic Adjustment (MOMA)
• Adaptive Laboratory Evolution
• New Potential Design Tools
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-101-
Desired Cell Evolution
(0.050473, 7.3865)
(0.009745, 5.38586)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-102-
Adaptive Laboratory Evolution
B. Palsson (2010). “Adaptive Laboratory Evolution.” Microbe, 6(2):69-74
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-103-
Adaptive Laboratory Evolution Methods
Dragosits, M. and D. Mattanovich (2013). "Adaptive laboratory evolution -- principles and applications for biotechnology." Microbial cell factories 12: 64.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-104-
Adaptive Laboratory Evolution
• Cells will modify their genotype to optimize their fitness in the given
environment.
• FBA results can be presented on phenotypic phase-plane (PhPP) plots of
model-predicted optimal biomass production (growth rate) versus carbon
source uptake rate (SUR) and oxygen uptake rate (OUR).
• The line of optimality (LO) describes the most efficient ratio of SUR and
OUR for biomass synthesis.
• Under several conditions, the experimentally measured E.coli phenotype
corresponds to the LO of the PhPP
• When E.coli growth is not consistent with the LO, populations migrate
toward the LO through adaptive evolution.
• Adaptive evolution outcomes have shown that evolved strains exhibit a
general pattern of increased expression of genes and proteins associated
with the optimal flux distribution, and decreased expression of genes and
proteins associated with unused pathways
• The frequent mutation of transcriptional regulators is consistent with
recent evidence showing that regulatory networks evolve faster than
other networks, such as genetic networks, protein interaction networks,
and metabolic networks
Conrad, T. M., N. E. Lewis, et al. (2011). "Microbial laboratory evolution in the era of genome-scale science." Molecular Systems Biology 7: 509.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-105-
Adaptive Laboratory Evolution
Conrad, T. M., N. E. Lewis, et al. (2011). "Microbial laboratory evolution in the era of genome-scale science." Molecular Systems Biology 7: 509.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-106-
Growth rate of E.coli K-12 on Glucose, Malate, Succinate, & Acetate
• Growth rate (exponential phase)
during adaptive evolution on
glucose, malate, succinate and
acetate.
• The increases in growth rate over
time were as follows:
 glucose (18%),
 malate (21%),
 succinate (17%) and
 acetate (20%).
• The number of generations for
each adaptive evolution was: glucose
(500), malate (500), succinate
(1,000) and acetate (700).
2
5
Ibarra, R. U., J. S. Edwards, et al. (2002). "Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth." Nature 420(6912): 186-189.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-107-
Growth of E.coli K-12 on Malate
Blue dots are starting and ending points
Ibarra, R. U., J. S. Edwards, et al. (2002). "Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth." Nature 420(6912): 186-189.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-108-
Growth of E.coli K-12 on Glucose
Ibarra, R. U., J. S. Edwards, et al. (2002). "Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth." Nature 420(6912): 186-189.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-109-
Growth of E.coli K-12
on Glycerol
37°C
30°C
(Ibarra, 2002)
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-110-
Review Questions
1.
What don’t the first generation of
transformed cells normally achieve the
optimal performance predicted in the FBA
models?
2.
How can cells evolve after 100’s of
generations to operate on the line of
optimality?
3.
What is the relationship between MOMA
and adaptive laboratory evolution?
4.
What are the number of generations
required for adaptive laboratory evolution?
Ibarra, R. U., J. S. Edwards, et al. (2002). "Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth." Nature 420(6912): 186-189.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-111-
Lesson Outline
• Overview
• Strain Design for Bioproduct Production
• Media Optimization for Bioproduct Production
• Method of Minimization of Metabolic Adjustment (MOMA)
• Adaptive Laboratory Evolution
• New Potential Design Tools
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-112-
Evolution of Bioproduction Tools
Fiest
SimOptStrain
ROOM
OptKnock
2000
2001
2002
2003
Available in Cobra Toolbox
Utah State University
OptGene
OptStrain
MOMA
2004
K-OptForce
OptOrf
OptReg
2005
OptCom
OptForce
2006
GAMS
EMILiO
GDLS
2007
2008
Matlab
BIE 5500/6500
2009
2010
2011
2012
2013
2014
CPLEX ILOG Solver
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
OptStrain uses a multi-step process to first
identify non-native reactions that would improve
the host organism’s maximum production
capabilities. Reaction deletions can then be
found which couple production and growth in the
modified host metabolic network.
H. Scott Hinton, 2015
-113-
OptStrain
OptStrain procedure.
•
Step 1 - Curation of database(s) of reactions
to compile the Universal database, comprising
only elementally balanced reactions.
•
Step 2 - Identifies a maximum-yield path
enabling the desired biotransformation from
a substrate to product without any
consideration for the origin of reactions.
Note that the white arrows represent native
reactions of the host and the yellow arrows
denote non-native reactions.
•
Step 3 - Minimizes the reliance on non-native
reactions,
•
Step 4 - Incorporates the non-native
functionalities into the microbial host’s
stoichiometric model and applies the
OptKnock procedure to identify and eliminate
reactions competing with the targeted
product. The red X’s pinpoint the deleted
reactions.
Pharkya, P., A. P. Burgard, et al. (2004). "OptStrain: a computational framework for redesign of microbial production systems." Genome research 14(11): 2367-2376.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-114-
OptReg
• OptReg is an extension of OptKnock (Burgard
et al., 2003) to allow for up- and/or
downregulation in addition to gene knock-outs
to meet a bioproduction goal.
• The objective is to computationally identify
which reactions should be modulated, (i.e.,
repressed or activated) or knocked-out such
that the biochemical of interest is
overproduced.
• Gene up- or downregulation can be tuned by
using widely available promoter libraries
• In many cases, a gene deletion is lethal
whereas its downregulation is not.
Utah State University
Pharkya, P. and C. D. Maranas (2006). "An optimization framework for identifying reaction activation/inhibition
or elimination candidates for overproduction in microbial systems." Metabolic engineering 8(1): 1-13.
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-115-
OptOrf
• Identifies gene deletion and
overexpression strategies (instead of
reaction deletions) by directly taking
into account gene to protein to reaction
association and transcriptional
regulation.
• Integrates transcriptional regulatory
networks and metabolic networks.
• By taking regulatory effects into
account, OptORF can propose changes
such as the overexpression of metabolic
genes or deletion of transcriptional
factors, in addition to the deletion of
metabolic genes, that may lead to faster
evolutionary trajectories
Utah State University
Kim, J. and J. L. Reed (2010). "OptORF: Optimal metabolic and regulatory perturbations for
metabolic engineering of microbial strains." BMC systems biology 4: 53.
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-116-
OptForce
• OptForce is a process that identifies all possible engineering
interventions by classifying reactions in the metabolic model
depending upon whether their flux values must increase,
decrease or become equal to zero to meet a pre-specified
overproduction target.
• By superimposing the flux ranges for a given reaction in the
wild-type vs. the overproducing network a number of possible
outcomes are revealed. If there is any degree of overlap
between the two reaction flux ranges then it may be possible
to achieve the overproduction target without changing the
value of the corresponding reaction flux in the wild-type strain.
In contrast, if the flux ranges for a reaction in the wild-type
metabolic network are completely to the left or to the right of
the corresponding ranges for the overproducing metabolic
network then the overproduction target cannot be achieved
unless the reaction flux is directly or indirectly changed.
• Note that if the reaction flux range collapses to zero then the
corresponding reaction needs to be eliminated (e.g., through a
gene knock-out).
• Applying this classification rule for pairs, triples, quadruples, etc.
of reactions. This leads to the identification of a sufficient and
non-redundant set of fluxes that must change (i.e., MUST set) to
meet a pre-specified overproduction target.
• Starting with the MUST set a minimal set of fluxes is identified
that must be actively forced through genetic manipulations (i.e.,
FORCE set) to ensure that all fluxes in the network are consistent
with the overproduction objective
Ranganathan, S., P. F. Suthers, et al. (2010). "OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions." PLoS computational biology 6(4): e1000744
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-117-
SimOptStrain
•
SimOptStrain simultaneously considers gene
deletions in a host organism and reaction
additions from a universal database such as
KEGG or MetaCyc.
•
Previously, the OptStrain framework used a
multi-step procedure to first identify a
minimal set of non-native reactions to add to
the metabolic network to achieve the
theoretical maximum production (TMP) of a
biochemical target (Step 1), and then identify
deletion strategies in the expanded metabolic
network using OptKnock (Step 2).
•
The current multi-step OptStrain procedure
may miss higher production strategies by not
evaluating additions and deletions
simultaneously.
Kim, J., J. L. Reed, et al. (2011). "Large-scale bi-level strain design approaches and mixed-integer programming solution techniques." PLoS One 6(9): e24162.
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Feist (2010)-Production Potential for Growth-Coupled Bioproducts
• An integrated approach through a systematic
model-driven evaluation of the production
potential for E. coli to produce multiple
native products from different
representative feedstocks through coupling
metabolite production to growth rate.
• Optimal strain designs were based on designs
which possess maximum yield, substratespecific productivity, and strength of
growth-coupling for up to 10 reaction
eliminations (knockouts).
• The method introduced a new concept of
objective function tilting for strain design
Feist, A. M., D. C. Zielinski, et al. (2010). "Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli." Metabolic engineering 12(3): 173-186.
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Desirable Production Metrics
•
All designs had to be growth coupled. Each equation examines a different
desirable production phenotype.
•
Product yield (Yp/s): Maximum amount of product that can be generated
per unit of substrate.
Yp/s 
•
consumption ratesubstrate
 mmol product 


 mmol substrate 
Substrate-specific productivity (SSP): Product yield per unit substrate
multiplied by the growth rate
SSP 
•
production rate product
production rate product  growth rate
consumption ratesubstrate
 mmol product 


 mmol substrate  hr 
slope
2
 mmol product 


 mmol substrate  hr 
The slope in this function is the slope of the line between the point
of minimum production rate at maximum growth and the point of
maximum growth at zero production on a production envelope plot.
When this slope is high, it is possible for a strain to grow at very
close to the maximum growth rate with only a small production rate,
which is undesirable. Therefore, optimizing for maximum production
rate is the same as optimizing for maximum product yield.
Maximizing for substrate specific productivity introduces a nonlinear objective function, which can be handled by OptGene but not
OptKnock. Similarly, the strength of coupling is also a non-linear
objective function and can only be handled by OptGene. Additionally,
a penalty can be added to the scoring function in OptGene by
multiplying the objective function with the following penalty
function:
objective_new=objective_original∗ delPenaltynumDels
Strength of growth coupling (SGC): Product yield per unit substrate
divided by the slope of the lower edge of the production curve
 product yield 
SGC 
•
where objective_new is the new score of the objective
function, objective_original is the original objective function
(e.g., product yield), delPenalty is the deletion penalty, and
numDels is the number of knockout reactions. This penalty
ensures that designs with fewer knockouts will be selected
over designs with similar phenotypes, but more knockouts.
Fewer knockouts are desirable for ease of strain construction.
Feist, A. M., D. C. Zielinski, et al. (2010). "Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli." Metabolic engineering 12(3): 173-186.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
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Strain
Design
Feist, A. M., D. C. Zielinski, et al. (2010).
"Model-driven evaluation of the production
potential for growth-coupled products of
Escherichia coli." Metabolic engineering
12(3): 173-186.
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Constraint-based Metabolic Reconstructions & Analysis
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Problem Formulation: Reduction of Model
and Selection of Targeted Reactions
Cobra Model
reduceModel()
detectDeadEnds()
removeDeadEnds()
pFBA()
findExcRxns()
findTransRxns()
Remove dead-ends
& minimize flux ranges
Remove transport reactions and
periphery metabolic pathways
Remove essential
& nearly essential reactions
Remove reactions that act on highcarbon containing molecules
Remove non-gene associated
or spontaneous reactions
(including key subsystems)
Determine co-sets: only consider
one reaction in a correlated set
findHiCarbobRxns()
identifyCorrelSets()
Target reactions
Feist, A. M., D. C. Zielinski, et al. (2010). "Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli." Metabolic engineering 12(3): 173-186.
Utah State University
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Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
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Substrate Conditions
Feist, A. M., D. C. Zielinski, et al. (2010). "Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli." Metabolic engineering 12(3): 173-186.
Utah State University
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Constraint-based Metabolic Reconstructions & Analysis
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Theoretical maximum production Yp/s (%) analysis.
Feist, A. M., D. C. Zielinski, et al. (2010). "Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli." Metabolic engineering 12(3): 173-186.
Utah State University
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Constraint-based Metabolic Reconstructions & Analysis
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The Strain Designs Generated for Five Different Targets
from Glucose and Xylose Anaerobically
The different target production rates (mmol gDW−1 hr−1)
are shown on the y-axis and the growth rate (hr−1) is given
on the x-axis.
glucose
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xylose
Feist, A. M., D. C. Zielinski, et al. (2010). "Model-driven evaluation of the production potential
for growth-coupled products of Escherichia coli." Metabolic engineering 12(3): 173-186.
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Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
H. Scott Hinton, 2015
-125-
OptCom
• OptCom, a comprehensive flux balance analysis
framework for microbial communities.
• OptCom is general enough to capture any type of
interactions (positive, negative or combinations
thereof) and is capable of accommodating any
number of microbial species (or guilds) involved.
• OptCom demonstrates the importance of tradeoffs between species- and community-level
fitness driving forces and lays the foundation
for metabolic-driven analysis of various types of
interactions in multi-species microbial systems
using genome-scale metabolic models.
Zomorrodi, A. R. and C. D. Maranas (2012). "OptCom: A Multi-Level Optimization Framework for the
Metabolic Modeling and Analysis of Microbial Communities." PLoS computational biology 8(2): e1002363.
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Constraint-based Metabolic Reconstructions & Analysis
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k-OptForce
• Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects
of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions.
• k-OptForce integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the
prediction of intervention strategies for improving the bio-production of a chemical of interest.
• In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic
expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in
other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby
requiring fewer direct interventions.
• k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key
enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometryalone analysis.
Chowdhury, A., A. R. Zomorrodi, et al. (2014). "k-OptForce: integrating kinetics with flux balance analysis for strain design." PLoS computational biology 10(2): e1003487.
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Constraint-based Metabolic Reconstructions & Analysis
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Review Questions
1.
2.
3.
4.
5.
Describe the key features of OptStrain.
Describe the key features of ROOM.
Describe the key features of OptReg.
Describe the key features of OptOrf.
Describe the key features of OptForce.
6.
7.
8.
9.
Describe the key
Describe the key
Describe the key
Describe the key
features of SimOptStrain.
features of the 2010 Feist Strain Design System.
features of OptCom.
features of k-OptForce.
10. What is the product yield as defined by Feist (2010)?
11. What is the substrate-specific productivity as defined by Feist (2010)?
12. What is the strength of growth coupling as defined by Feist (2010)?
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Lesson Outline
• Overview
• Strain Design for Bioproduct Production
• Media Optimization for Bioproduct Production
• Method of Minimization of Metabolic Adjustment (MOMA)
• Adaptive Laboratory Evolution
• New Potential Design Tools
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Lesson: Bioproduct Production
Constraint-based Metabolic Reconstructions & Analysis
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ROOM
• Regulatory on/off minimization (ROOM) is a
constraint- based algorithm for predicting the
metabolic steady state after gene knockouts.
• It aims to minimize the number of significant flux
changes (hence on/off) with respect to the wild
type.
• MOMA (unique solution) provides accurate
predictions for the initial transient growth rates
observed during the early postperturbation state,
whereas ROOM (non-unique solutions) and FBA
more successfully predict final higher steady-state
growth rates.
• FBA will always predict higher growth rates but is
better at predicting adaptive evolutionary outcomes
Shlomi, T., O. Berkman, et al. (2005). "Regulatory on/off minimization of metabolic flux changes after genetic perturbations."
Proceedings of the National Academy of Sciences of the United States of America 102(21): 7695-7700.
Utah State University
BIE 5500/6500
Lesson: Bioproduct Production