lesson: bioproduct productionbie 5500/6500utah state university h. scott hinton, 2015...
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Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -1-
Bioproduct Production
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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
constraint-based 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
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -4-
iAF1260Model
(GlucoseEthanol_iaf1260.pdf)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -5-
iAF1260Metabolic
Core(GlucoseEthanol_iaf1260.pdf)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -6-
iAF1260Amino Acid Metabolism(GlucoseEthanol_iaf1260.pdf)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -7-
iAF1260NecleotideMetabolism(GlucoseEthanol_iaf1260.pdf)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -8-
iAF1260Alternate
Carbon Sources(GlucoseEthanol_iaf1260.pdf)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -9-
iAF1260Cofactor
Biosynthesis(GlucoseEthanol_iaf1260.pdf)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -10-
iAF1260Inorganic Ion
Transport(GlucoseEthanol_iaf1260.pdf)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -11-
Fatty acid Biosynthesis(GlucoseEthanol_iaf1260.pdf)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -12-
tRNA Charging(GlucoseEthanol_iaf1260.pdf)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -13-
EnterobacterialCommonAntigen
(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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -14-
E.coli Genome-scale Reconstructions• Escherichia coli 042
• Escherichia coli 536
• Escherichia coli 55989
• Escherichia coli ABU 83972
• Escherichia coli APEC O1
• Escherichia coli ATCC 8739
• Escherichia coli B str. REL606
• Escherichia coli BL21(DE3) AM946981
• Escherichia coli BL21(DE3) BL21-Gold(DE3)pLysS AG
• Escherichia coli BL21(DE3) CP001509
• Escherichia coli BW2952
• Escherichia coli CFT073
• Escherichia coli DH1
• Escherichia coli DH1 ME8569
• Escherichia coli E24377A
• Escherichia coli ED1a
• Escherichia coli ETEC H10407
• Escherichia coli HS
• Escherichia coli IAI1
• Escherichia coli IAI39
• Escherichia coli IHE3034
• Escherichia coli KO11FL
• Escherichia coli LF82
• Escherichia coli NA114
• Escherichia coli O103:H2 str. 12009
• Escherichia coli O111:H- str. 11128
• Escherichia coli O127:H6 str. E2348/69
• Escherichia coli O157:H7 EDL933
• Escherichia coli O157:H7 str. EC4115
• Escherichia coli O157:H7 str. Sakai
• Escherichia coli O157:H7 str. TW14359
• Escherichia coli O26:H11 str. 11368
• Escherichia coli O55:H7 str. CB9615
• Escherichia coli O83:H1 str. NRG 857C
• Escherichia coli S88
• Escherichia coli SE11
• Escherichia coli SE15
• Escherichia coli SMS-3-5
• Escherichia coli str. K-12 substr. DH10B
• Escherichia coli str. K-12 substr. MG1655
• Escherichia coli str. K-12 substr. W3110
• Escherichia coli UM146
• Escherichia coli UMN026
• Escherichia coli UMNK88
• Escherichia coli UTI89
• Escherichia coli W
• Escherichia coli W CP002185
• Escherichia coli K-12 MG1655
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.
Strain used at
USU
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -16-
Clustering of Species by Unique Growth-supporting Conditions
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.
655 combinations
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -17-
Predicting Microbial Growth
Monk, J. and B. O. Palsson (2014). "Genetics. Predicting microbial growth." Science 344(6191): 1448-1449.
Single KnockoutDouble Knockouts
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -20-
Reaction Knockouts: Ethanol Production
% AnaerobicEthanolRA.m
clear; clc;
% 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
% of the ethanol secretion rate
[controlFlux, objFlux] = robustnessAnalysis(model,'EX_etoh(e)',100);
Maximum ethanol productionwith no growth
Robustness Analysis
This curve represents the
maximum possible growth rate
Larger production,lower growth rate
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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');
% 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';
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)
deletions = 'ACKr' 'GLUDy' 'ME2' 'PGL' 'PYK'
growthRate = 0.0767
minProd = 18.5452; maxProd = 18.8342
(0.0767, 18.83)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -22-
Multiproduction Envelopes for Different Knockouts('ACKr‘, 'GLUDy‘, 'ME2‘, 'PGL‘, 'PYK‘)('PFL‘)
('PTAr‘, 'PYK‘)
('ACKr‘, 'GLUDy‘, 'PYK‘)
('GND‘, 'ME2‘, 'PTAr‘, 'PYK‘)
(0.0816, 18.76)
(0.0767, 18.83)(0.0852, 18.71)
(0.0913, 18.61)
(0.1800, 16.59)
EthanolProduction_OptKnock_Gurobi_Revised.m
Note that only one case includes secondary secretion.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -23-
Adding ReactionsaddReaction - 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 COBRA model structure rxnName Reaction name abbreviation (i.e. 'ACALD') (Note: can also be a cell array {'abbr','name'} metaboliteList Cell array of metabolite names or alternatively the reaction formula for the reaction stoichCoeffList List of stoichiometric coefficients (reactants -ve, products +ve), empty if reaction formula is provided
OPTIONAL INPUTS revFlag Reversibility flag (Default = true) lowerBound Lower bound (Default = 0 or -vMax) upperBound Upper bound (Default = vMax) objCoeffObjective 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)
OUTPUTS model COBRA model structure with new reaction rxnIDexists 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
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
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -24-
Example #1: BiliverdinPotential 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)
biliverdinheme
heme oxygenase
+ O2
+ Fe + CO
+ e-
a
Jon Takemoto, USU SBI Bioproducts Summit, February 2012
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -25-
BiliverdinGene
Dong Chen, USU SBI Bioproducts Summit, February 2012
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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
Dong Chen, USU SBI Bioproducts Summit, February 2012
Biliverdin Production
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -27-
Location of Heme Pathway in iAF1260
Model
BIGG Models @ http://bigg.ucsd.edu/
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -28-
Biliverdin Pathway in iAF1260
BILIVERDINtexBILIVERDINtppEX_biliverdin(e)
HEMEOX
nadph h o2
biliverdin[c]
h2ocofe2nadp
biliverdin[p] biliverdin[e]
Biliverdin Pathway
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -29-
Biliverdin Complete Pathway
Secreted Biliverdin
α-ketoglutarateL-Glutamate
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -30-
Key Components of the Biliverdin Pathway in iAF1260
ATP
L-Glutamate
NADPH
O2 Fe2 ATP
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -31-
What are the Limits of Biliverdin Production?
Biliverdin_Robustness_iJO1366.m
Desired Operating
Point
Minimum Production
Rate
Maximum
Growth and Production
OpportunitySpace
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -32-
% Input the E.coli core model
model=readCbModel('ecoli_iJO1366');
% 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 biliverdin secretion reactions
model = addReaction(model,'BILIVERDINtex','biliverdin[p] <=> biliverdin[e]');
model = addReaction(model,'BILIVERDINtpp','biliverdin[c] <=> biliverdin[p]');
model = 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;
model.metCharge(metID(3))= -2;
% Add carbon monoxide secretion reactions
model = addReaction(model,'COtpp','co[c] <=> co[p]');
model = addReaction(model,'COtex','co[p] <=> co[e]');
model = addReaction(model,'EX_co(e)','co[e] <=>');
Adding Biliverdin Pathway to the Model
Desired Reaction Model Information
BILIVERDINtexBILIVERDINtppEX_biliverdin(e)
HEMEOX
nadph h o2
biliverdin[c]
h2ocofe2nadp
biliverdin[p] biliverdin[e]
pheme[c] Biliverdin Pathway
Rxn Name Rxn Description Formula
Gene-Reaction
AssociationGenes Proteins Subsystem Reversible LB UB Objective Confidence
Score EC Number Notes References
Default Reaction Settings: UB = 1000
LB = -1000
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -33-
% Biliverdin_optKnock_iJO1366_Precalculated_Reactions.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');
model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M');
% 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'}));
[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('Biliverdin_SelectedRxns_iJO1366.mat','selectedRxns'); % Save the selected Reactions is a MAT file
Creating and Saving Selected Reactions in
iJO1366
Removing Undesired Subsystems
Save ‘selectedRxns’to a “mat” file
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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');
target = 'EX_biliverdin(e)';
options.targetRxn = target;
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)
Engineering E.coli to Produce Biliverdin
with OptKnockLoad ‘selectedRxns’
from“mat” file
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -35-
Biliverdin OptKnock Results
1 Knockoutans = 'PPKr'growthRate = 0.2325minProd = 0maxProd = 0
5 Knockoutsans = 'G6PDA'growthRate = 0.9824minProd = 0maxProd = 1.7997e-08
2 Knockoutsans = Empty cell arraygrowthRate = 0.2325minProd = 0maxProd = 0
10 Knockoutsans = 'CITL‘,'FUM‘,'G6PDA‘,'I4FE4ST‘, 'ICL‘,'TKT2‘,'XAND''growthRate = 0.2325minProd = 0maxProd = 0
WHY?
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -36-
Biliverdin Complete Pathway
Secreted Biliverdin
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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -37-
Biliverdin Production
Biliverdin_Robustness_iJO1366.m
Minimum Production
Rate
Assumes the unregulated HEMEOX Enzyme can produce 1.2 mmol/gDW-hr
These values equal the production rate
of the HEMEOX enzyme
Biliverdin_ProductionEnvelope_iJO1366.m
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -38-
Example #2: Bioplastic (PHB) Pathway in E.coli
PHA: Polyhydroxyalkanoate
- family of bioplastics
PHB: Polyhydroxybutyrate
- subset of PHAs
Also known as:
poly-3-hydroxybutyrate,
poly(3-hydroxybutyrate),
and
poly(β-hydroxy butyric
acid)
PHB Pathway
aacoaaccoa
nadph h
r3hbcoa
nadpcoa coa
phb[c]ACACT1r AACOAr PHBpoly
DM_phb
PHB Demand Reaction
Present in E.coli
TEM image of PHB inside bacteria
Bioplastic
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -39-
Metabolic Genome-Scale Metabolic
Modelingin E.coli
BIGG Models @ http://bigg.ucsd.edu/
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -40-
PHBPathway
PHB Pathway
Acetyl-CoA
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -41-
What are the Limits of PHB Production?
PHB_Robustness_iJO1366.m
Minimum Production
Rate
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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] <=> r3hbcoa[c] + nadp[c]');
metID=findMetIDs(model,'r3hbcoa[c]');
model.metCharge(metID)= -4;
% 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]');
Already in E.coli
Adding a demand reaction
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -43-
PHB Production
PHB_Robustness_iJO1366.m
Desired Operating
Point
Knockouts = 'MDH','PSP_L','PTAr','TPI'
Production of unregulated PHB pathway
PHB_ProductionEnvelope_iJO1366.m
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -44-
Example #3: Spider Silk
Sarah Allred, “Metabolic Modeling of Spider Silk Production in E. coli,” MS Thesis, USU, 2014
• 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
Potential Applications
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -45-
Spider Silk: Mechanical Properties
Sarah Allred, “Metabolic Modeling of Spider Silk Production in E. coli,” MS Thesis, USU, 2014
Spider Silk Protein
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -46-
Spider Silk: Flagelliform Composition
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
Amino Acid Composition of Silks from A. diadematus
Sarah Allred, “Metabolic Modeling of Spider Silk Production in E. coli,” MS Thesis, USU, 2014
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -47-
Spider Silk Production
in E.coli
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -48-
What are the Limits of Spider Silk Production?
Spider_Silk_Robustness_iJO1366.m
Desired Operating
Point
Minimum Production
Rate
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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');
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -50-
Spider Silk Production
Spider_Silk_ProductionEnvelope_iJO1366.mSpider_Silk_Robustness_iJO1366.m
Desired Operating
Point
Minimum Production
Rate
Sarah Allred, “Metabolic Modeling of Spider Silk Production in E. coli,” MS Thesis, USU, 2014
3.3Allred Production Rate
OpportunitySpace
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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?
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -54-
Rxn name Rxn description Formula LB UBEX_ala_L(e) L-Alanine exchange ala-L[e] <=> 0 1000EX_arg_L(e) L-Arginine exchange arg-L[e] <=> 0 1000EX_asn_L(e) L-Asparagine exchange asn-L[e] <=> 0 1000EX_asp_L(e) L-Aspartate exchange asp-L[e] <=> 0 1000EX_cys_L(e) L-Cysteine exchange cys-L[e] <=> 0 1000EX_gln_L(e) L-Glutamine exchange gln-L[e] <=> 0 1000EX_glu_L(e) L-Glutamate exchange glu-L[e] <=> 0 1000EX_gly(e) Glycine exchange gly[e] <=> 0 1000EX_his_L(e) L-Histidine exchange his-L[e] <=> 0 1000EX_ile_L(e) L-Isoleucine exchange ile-L[e] <=> 0 1000EX_leu_L(e) L-Leucine exchange leu-L[e] <=> 0 1000EX_lys_L(e) L-Lysine exchange lys-L[e] <=> 0 1000EX_met_L(e) L-Methionine exchange met-L[e] <=> 0 1000EX_phe_L(e) L-Phenylalanine exchange phe-L[e] <=> 0 1000EX_pro_L(e) L-Proline exchange pro-L[e] <=> 0 1000EX_ser_L(e) L-Serine exchange ser-L[e] <=> 0 1000EX_thr_L(e) L-Threonine exchange thr-L[e] <=> 0 1000EX_trp_L(e) L-Tryptophan exchange trp-L[e] <=> 0 1000EX_tyr_L(e) L-Tyrosine exchange tyr-L[e] <=> 0 1000EX_val_L(e) L-Valine exchange val-L[e] <=> 0 1000
iAF1260Amino AcidExchangeReactions
No essential amino acids for E.coli
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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -55-
iaf1260 Biomass Objective FunctionAmino 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]
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -56-
iAF1260 Model
Mineral Exchanges
Rxn name Rxn description Formula LB UBEX_ca2(e) Calcium exchange ca2[e] <=> -1000 1000EX_cbl1(e) Cob(I)alamin exchange cbl1[e] <=> -0.01 1000EX_cl(e) Chloride exchange cl[e] <=> -1000 1000EX_co2(e) CO2 exchange co2[e] <=> -1000 1000EX_cobalt2(e) Co2+ exchange cobalt2[e] <=> -1000 1000EX_cu2(e) Cu2+ exchange cu2[e] <=> -1000 1000EX_fe2(e) Fe2+ exchange fe2[e] <=> -1000 1000EX_fe3(e) Fe3+ exchange fe3[e] <=> -1000 1000EX_h2o(e) H2O exchange h2o[e] <=> -1000 1000EX_h(e) H+ exchange h[e] <=> -1000 1000EX_k(e) K+ exchange k[e] <=> -1000 1000EX_mg2(e) Mg exchange mg2[e] <=> -1000 1000EX_mn2(e) Mn2+ exchange mn2[e] <=> -1000 1000EX_mobd(e) Molybdate exchange mobd[e] <=> -1000 1000EX_na1(e) Sodium exchange na1[e] <=> -1000 1000EX_nh4(e) Ammonia exchange nh4[e] <=> -1000 1000EX_pi(e) Phosphate exchange pi[e] <=> -1000 1000EX_so4(e) Sulfate exchange so4[e] <=> -1000 1000EX_tungs(e) tungstate exchange tungs[e] <=> -1000 1000EX_zn2(e) Zinc exchange zn2[e] <=> -1000 1000
Note that all the minerals except cob(I)alamin allow unlimited uptake.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -57-
iaf1260 Biomass Objective FunctionMineral 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]
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -58-
in silico M9 Minimal
Media
Reaction Abbreviation Reaction Name Formula Lower Bound Upper BoundEX_ca2(e) Calcium exchange ca2[e] <=> -1000 1000EX_cl(e) Chloride exchange cl[e] <=> -1000 1000EX_co2(e) CO2 exchange co2[e] <=> -1000 1000EX_cobalt2(e) Co2+ exchange cobalt2[e] <=> -1000 1000EX_cu2(e) Cu2+ exchange cu2[e] <=> -1000 1000EX_fe2(e) Fe2+ exchange fe2[e] <=> -1000 1000EX_fe3(e) Fe3+ exchange fe3[e] <=> -1000 1000EX_h(e) H+ exchange h[e] <=> -1000 1000EX_h2o(e) H2O exchange h2o[e] <=> -1000 1000EX_k(e) K+ exchange k[e] <=> -1000 1000EX_mg2(e) Mg exchange mg2[e] <=> -1000 1000EX_mn2(e) Mn2+ exchange mn2[e] <=> -1000 1000EX_mobd(e) Molybdate exchange mobd[e] <=> -1000 1000EX_na1(e) Sodium exchange na1[e] <=> -1000 1000EX_tungs(e) tungstate exchange tungs[e] <=> -1000 1000EX_zn2(e) Zinc exchange zn2[e] <=> -1000 1000EX_ni2(e) Ni2+ exchange ni2[e] <=> -1000 1000EX_sel(e) Selenate exchange sel[e] <=> -1000 1000EX_slnt(e) selenite exchange slnt[e] <=> -1000 1000EX_so4(e) Sulfate exchange so4[e] <=> -1000 1000EX_nh4(e) Ammonia exchange nh4[e] <=> -1000 1000EX_pi(e) Phosphate exchange pi[e] <=> -1000 1000EX_cbl1(e) Cob(I)alamin exchange cbl1[e] <=> -0.01 1000
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.
This in silico media assumes the cell can uptake all the minerals wanted/needed from the media. It does not allow amino acid uptake.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -59-
Ethanol Production(GlucoseEthanol_Mutant_iJO1366.m & GlucoseEthanol_multiProductionEnvelope_iJO1366.m)
• Picture of ethanol producing map in 1260
Biomass 0.246761EX_ca2(e) -0.00128439EX_cl(e) -0.00128439EX_co2(e) 6.90492EX_cobalt2(e) -6.16902e-06EX_cu2(e) -0.000174953EX_etoh(e) 6.41879EX_fe2(e) -0.00203652EX_fe3(e) -0.00192671EX_glc(e) -20EX_glyclt(e)0.000165083EX_h(e) 32.3664EX_h2o(e) 7.08385EX_k(e) -0.048166EX_lac-D(e) 29.9334EX_meoh(e) 4.93522e-07EX_mg2(e) -0.00214065EX_mn2(e) -0.000170512EX_mobd(e) -3.18322e-05EX_nh4(e) -2.66522EX_ni2(e) -7.97038e-05EX_pi(e) -0.238033EX_so4(e) -0.0622356EX_succ(e) 0.0817924EX_zn2(e) -8.41455e-05
Flux ThroughExchange Reactions
Assumptions:AerobicGlucose = -20
Knockouts (OptKnock):'PFL','PPC','PPKr'
Barely Coupled
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -60-
Amino Acid Flux Values &Reduced Costs
for Ethanol Productionin M9 Minimal Media
(GlucoseEthonal_ReducedCosts_iJO1366)
Flux ThroughExchange Reactions
EX_ala-L(e) -1EX_arg-L(e) -1.83333EX_asn-L(e) -1EX_asp-L(e) -1EX_cys-L(e) -0.833333EX_gln-L(e) -1.5EX_glu-L(e) -1.5EX_gly(e) -0.5EX_his-L(e) 0EX_ile-L(e) 0EX_leu-L(e) 0EX_lys-L(e) 0EX_met-L(e) 0EX_phe-L(e) 0EX_pro-L(e) -0.5EX_ser-L(e) -0.833333EX_thr-L(e) -1.33333EX_trp-L(e) -0.833333EX_tyr-L(e) 0EX_val-L(e) 0
Amino Acid Reduced Costs
Objective Function = EX_etoh(e)
Biomass 0.246761EX_ca2(e) -0.00128439EX_cl(e) -0.00128439EX_co2(e) 6.90492EX_cobalt2(e) -6.16902e-06EX_cu2(e) -0.000174953EX_etoh(e) 6.41879EX_fe2(e) -0.00203652EX_fe3(e) -0.00192671EX_glc(e) -20EX_glyclt(e)0.000165083EX_h(e) 32.3664EX_h2o(e) 7.08385EX_k(e) -0.048166EX_lac-D(e) 29.9334EX_meoh(e) 4.93522e-07EX_mg2(e) -0.00214065EX_mn2(e) -0.000170512EX_mobd(e) -3.18322e-05EX_nh4(e) -2.66522EX_ni2(e) -7.97038e-05EX_pi(e) -0.238033EX_so4(e) -0.0622356EX_succ(e) 0.0817924EX_zn2(e) -8.41455e-05
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -61-
Impact of Additional L-Arginine on Biliverdin Production(Biliverdin_Robustness_iJO1366.m & Biliverdin_Mutants_iJO1366.m)
EX_arg-L(e) Lower Bound = 0 EX_arg-L(e) Lower Bound = -1 EX_arg-L(e) Lower Bound = -20
Biomass 0.246761EX_etoh(e) 6.41879EX_glc(e) -20EX_lac-D(e) 29.9334EX_nh4(e) -2.66522EX_succ(e) 0.0817924
Biomass 0.148636EX_arg-L(e) -1EX_etoh(e) 0EX_glc(e) -20EX_lac-D(e) 28.0093EX_nh4(e) 2.39461EX_succ(e) 0.889455
Biomass 0.412812EX_ac(e) 2.70698EX_arg-L(e) -2.77733EX_etoh(e) 0EX_glc(e) -20EX_lac-D(e) 37.1847EX_nh4(e) 6.65062EX_succ(e) 0.136832
'ARGDC‘, 'PFL‘, 'PSP_L''PFL‘, 'PPC‘, 'PSERT''PFL‘, 'PPC‘, 'PPKr'
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -62-
Production Envelopes of Additional L-Arginine for Ethanol Production(Biliverdin_Robustness_iJO1366.m & Biliverdin_Mutants_iJO1366.m)
EX_arg-L(e) Lower Bound = 0 EX_arg-L(e) Lower Bound = -1 EX_arg-L(e) Lower Bound = -20
Biomass 0.246761EX_etoh(e) 6.41879EX_glc(e) -20EX_lac-D(e) 29.9334EX_nh4(e) -2.66522EX_succ(e) 0.0817924
Biomass 0.148636EX_arg-L(e) -1EX_etoh(e) 0EX_glc(e) -20EX_lac-D(e) 28.0093EX_nh4(e) 2.39461EX_succ(e) 0.889455
Biomass 0.412812EX_ac(e) 2.70698EX_arg-L(e) -2.77733EX_etoh(e) 0EX_glc(e) -20EX_lac-D(e) 37.1847EX_nh4(e) 6.65062EX_succ(e) 0.136832
'ARGDC‘, 'PFL‘, 'PSP_L''PFL‘, 'PPC‘, 'PSERT''PFL‘, 'PPC‘, 'PPKr'
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -63-
Biliverdin Production(Biliverdin_Mutants_iJO1366)
Biomass 0.103363EX_ca2(e) -0.000538004EX_cl(e) -0.000538004EX_co2(e) 14.9571EX_cobalt2(e) -2.58407e-06EX_cu2(e) -7.32843e-05EX_fe2(e) -0.00166011EX_glc(e) -10EX_h(e) 8.14972EX_h2o(e) 45.2869EX_k(e) -0.0201757EX_meoh(e) 2.06726e-07EX_mg2(e) -0.000896673EX_mn2(e) -7.14238e-05EX_mobd(e) -1.33338e-05EX_nh4(e) -5.9164EX_ni2(e) -3.33862e-05EX_o2(e) -12.3366EX_pi(e) -0.0997071EX_so4(e) -0.0260692EX_zn2(e) -3.52468e-05EX_biliverdin(e) 1.2EX_co(e) 1.2
Flux ThroughExchange Reactions
EX_biliverdin(e) LB = 1.2
Assume a non-regulated enzyme produced by a plasmid
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -64-
L-Glutamate Metabolite Connectivity
L-Glutamate can feed up to 49 different
reactions
VisualizeGlutamate_iJO1366.m
ecoli_iJO1366 Model
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -65-
Biliverdin Production(Biliverdin_AminoAcid_ReducedCosts_iJO1366)
EX_co2(e) 14.7976EX_glc(e) -10EX_h(e) 7.97689EX_h2o(e) 45.3757EX_nh4(e) -5.31792EX_o2(e) -12.1387EX_biliverdin(e) 1.32948EX_co(e) 1.32948
Flux ThroughExchange Reactions
EX_ala-L(e) -0.0646425EX_arg-L(e) -0.139079EX_asn-L(e)-0.0705191EX_asp-L(e)-0.0705191EX_cys-L(e) -0.0528893EX_gln-L(e) -0.111655EX_glu-L(e) -0.110676EX_gly(e) -0.0381978EX_his-L(e) 0EX_ile-L(e) 0EX_leu-L(e) 0EX_lys-L(e) 0EX_met-L(e) 0EX_phe-L(e) 0EX_pro-L(e) -0.109696EX_ser-L(e) -0.0558276EX_thr-L(e) -0.0950049EX_trp-L(e) -0.0568071EX_tyr-L(e) 0EX_val-L(e) 0
Amino Acid Reduced Costs
Objective Function = EX_biliverdin(e)
Note: Can’t produce more biliverdin than the induced enzymes from the plasmids can process!
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -66-
Impact of Additional L-Glutamate on Biliverdin Production(Biliverdin_Robustness_iJO1366.m & Biliverdin_Mutants_iJO1366.m)
EX_glu-L(e) Lower Bound = 0 EX_glu-L(e) Lower Bound = -0.1859 EX_glu-L(e) Lower Bound = -20
Biomass 0.103363EX_glc(e) -10EX_nh4(e) -5.9164EX_o2(e) -12.3366EX_biliverdin(e) 1.2
EX_co(e) 1.2
Biomass 0.119785EX_glc(e) -10EX_glu-L(e) -0.185878EX_nh4(e) -5.9079EX_o2(e) -12.4638EX_biliverdin(e) 1.2
EX_co(e) 1.2
Biomass 0.952339EX_ac(e) 22.8365EX_glc(e) -10EX_glu-L(e) -20EX_nh4(e) 4.91396EX_o2(e) -20EX_biliverdin(e) 1.2
EX_co(e) 1.2
EX_biliverdin(e) ≥ 1.2 EX_biliverdin(e) ≥ 1.2EX_biliverdin(e) ≥ 1.2
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -67-
Biomass 0.206854EX_ca2(e) -0.00107667EX_cl(e) -0.00107667EX_co2(e) 11.509EX_cobalt2(e) -5.17134e-06EX_cu2(e) -0.000146659EX_fe2(e) -0.00332228EX_glc(e) -10EX_h(e) 1.90062EX_h2o(e) 26.9719EX_k(e) -0.0403764EX_meoh(e) 4.13707e-07EX_mg2(e) -0.00179445EX_mn2(e) -0.000142936EX_mobd(e) -2.66841e-05EX_nh4(e) -2.23419EX_ni2(e) -6.68137e-05EX_o2(e) -6.06759EX_pi(e) -0.199537EX_so4(e) -0.0521705EX_zn2(e) -7.05371e-05DM_phb[c] 10
Flux ThroughExchange Reactions
PHB Production(PHB_Mutants_iJO1366)
PHB Pathway
Acetyl-CoA
PHB_Robustness_iJO1366.m
Assume a non-regulated enzyme produced by a plasmid
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -68-
EX_co2(e) 10.3513EX_glc(e) -10EX_h2o(e) 22.7635EX_o2(e) -4.14522DM_phb[c] 12.4122
Flux ThroughExchange Reactions
EX_ala-L(e) -0.608696EX_arg-L(e) -1.14783EX_asn-L(e)-0.652174EX_asp-L(e)-0.652174EX_cys-L(e) -0.504348EX_gln-L(e) -1.0087EX_glu-L(e) -1EX_gly(e) -0.347826EX_his-L(e) 0EX_ile-L(e) 0EX_leu-L(e) 0EX_lys-L(e) 0EX_met-L(e) 0EX_phe-L(e) 0EX_pro-L(e) -0.991304EX_ser-L(e) -0.530435EX_thr-L(e) -0.869565EX_trp-L(e) -0.53913EX_tyr-L(e) 0EX_val-L(e) 0
Amino Acid Reduced Costs
PHB Production(PHB_AminoAcid_ReducedCosts_iJO1366)
PHB Pathway
Acetyl-CoA
Objective Function = DM_phb[c]
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -69-
Impact of Additional L-Arginine on PHB Production(Biliverdin_Robustness_iJO1366.m & Biliverdin_Mutants_iJO1366.m)
EX_arg-L(e) Lower Bound = 0 EX_arg-L(e) Lower Bound = -1 EX_arg-L(e) Lower Bound = -20
Biomass 0.206854EX_glc(e) -10EX_nh4(e) -2.23419EX_o2(e) -6.06759DM_phb[c] 10
Biomass 0.30366EX_arg-L(e) -1EX_glc(e) -10EX_nh4(e) 0.720224EX_o2(e) -7.38727DM_phb[c] 10
Biomass 1.06729EX_ac(e) 9.52367EX_arg-L(e) -20EX_for(e) 0.444941EX_glc(e) -10EX_nh4(e) 39.6441EX_o2(e) -20DM_phb[c] 10
DM_phb[c] ≥ 10 DM_phb[c] ≥ 10DM_phb[c] ≥ 10
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -70-
EX_co2(e) 16.6918EX_glc(e) -10EX_h(e) 13.1856EX_h2o(e) 36.6126EX_nh4(e) -13.198EX_o2(e) -19.0627EX_so4(e) -0.0247617DM_flys3[c] 0.0123808
Flux ThroughExchange Reactions
EX_ala-L(e) -0.000584658
EX_arg-L(e) -0.00110235
EX_asn-L(e)-0.000661926
EX_asp-L(e)-0.000661926
EX_cys-L(e) -0.00118735
EX_gln-L(e) -0.000958118
EX_glu-L(e) -0.00094524
EX_gly(e) -0.000388914
EX_his-L(e) -0.00134446
EX_ile-L(e) -0.00165353
EX_leu-L(e) 0EX_lys-L(e) -0.00154793
EX_met-L(e) -0.00188018EX_phe-L(e) 0
EX_pro-L(e) -0.00119507
EX_ser-L(e) -0.000558902
EX_thr-L(e) -0.00096327
EX_trp-L(e) -0.000491937
EX_tyr-L(e) -0.00215577
EX_val-L(e) 0
Amino Acid Reduced Costs
Spider Silk Production(Spider_Silk_AminoAcid_ReducedCosts_iJO1366)
Objective Function = DM_flys3[c]
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -71-
Impact of Three Amino Acids to Spider Silk Production(Spider_Silk_Robustness_iJO1366.m & Spider_Silk_Mutants_iJO1366.m)
EX_ala-L(e) Lower Bound = 0EX_pro-L(e) Lower Bound = 0EX_ser-L(e) Lower Bound = 0
Biomass 0.716261EX_glc(e) -10EX_nh4(e) -11.3255EX_o2(e) -17.9371DM_flys3[c] 0.00336705
Biomass 0.871302EX_ac(e) 0.716336EX_ala-L(e) -1EX_for(e) 1.61531EX_glc(e) -10EX_nh4(e) -10.0001EX_o2(e) -20EX_pro-L(e) -1EX_ser-L(e) -1DM_flys3[c]0.00336705
Biomass 1.3575EX_ac(e) 42.9806EX_ala-L(e) -20EX_for(e) 33.8487EX_glc(e) -10EX_nh4(e) 26.4335EX_o2(e) -20EX_pro-L(e) -4.6849EX_ser-L(e) -20DM_flys3[c] 0.00336705
DM_flys3 [c] ≥ 0.0034 DM_flys3 [c] ≥ 0.0034DM_flys3 [c] ≥ 0.0034
EX_ala-L(e) Lower Bound = -1EX_pro-L(e) Lower Bound = -1EX_ser-L(e) Lower Bound = -1
EX_ala-L(e) Lower Bound = -20EX_pro-L(e) Lower Bound = -20EX_ser-L(e) Lower Bound = -20
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -72-
K12 Media
• Growth in K12 media was simulated by adjusting lower bounds of exchange reactions to correspond to media conditions
Chemical ConcentrationK12 Medium: KH2PO4 2 g/L K2HPO4.3H2O 4 g/L (NH4)2HPO4 5 g/L Yeast Extract 5 g/L Glucose 25 g/L MgSO4.7H2O 0.5 g/L Thiamine 2.5 mg/L K12 trace metal 5 ml/L K12 trace metal solution: NaCl 5 g/L ZnSO4.7H2O 1 g/L MnCl2.4H2O 4 g/L FeCl3.6H2O 4.75 g/L CuSO4.5H2O 0.4 g/L H3BO3 0.575 g/L NaMoO4.2H2O 0.5 g/L 6N H2SO4 12.5 ml/L
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -73-
Calculated Metabolite Uptake Rates for K-12 Media Metabolite MW (g/mol) g/L in media mmol/L in
mediaLower bound (mmol gDW-1h-1)
Glucose 180.16 25 138.7655417 -11Ammonium 18.03851 1.365829484 75.71742258 -6.002150375Phosphate 94.9714 6.655736264 70.08147994 -5.555386948Potassium 39.0983 1.945099309 49.74894838 -3.943619038Sulfate 96.07 0.203323529 2.11641021 -0.167768684Chloride 35.453 0.062050364 1.750214773 -0.138740225Copper 63.546 0.000509009 0.008010093 -0.000634963Iron (III) 55.845 0.00490684 0.087865335 -0.00696512Magnesium 24.305 0.049304203 2.028562155 -0.160804933Manganese 54.938044 0.005551956 0.101058487 -0.008010947Molybdate 95.95 0.001826052 0.019031286 -0.001508618Sodium 22.98976928 0.029775544 1.295164976 -0.102668246Thiamine 265.35 0.0025 0.009421519 -0.000746848Zinc 65.38 0.001136847 0.017388304 -0.001378378Alanine 89.09 0.225 2.525535975 -0.200200247Arginine 174.2 0.145 0.832376579 -0.065982824Asparagine 132.12 0.16 1.211020285 -0.095998062
Metabolite MW (g/mol) g/L in media mmol/L in media
Lower bound (mmol gDW-1h-1)
Aspartic acid 133.1 0.16 1.202103681 -0.09529124Cysteine 121.16 0.02 0.165070981 -0.013085243Glutamine 146.14 0.345 2.360749966 -0.187137594Glutamic Acid 147.13 0.345 2.344865085 -0.185878393Glycine 75.07 0.135 1.798321567 -0.14255367Histidine 155.15 0.06 0.386722527 -0.030655649Isoleucine 131.17 0.145 1.105435694 -0.08762833Leucine 131.17 0.21 1.600975833 -0.126909995Lysine 146.19 0.22 1.504890895 -0.119293303Methionine 149.21 0.045 0.301588365 -0.02390703Phenylalanine 165.19 0.12 0.726436225 -0.05758489Proline 115.13 0.115 0.998870842 -0.079180891Serine 105.09 0.13 1.237034922 -0.098060253Threonine 119.12 0.135 1.133310947 -0.089838012Tyrosine 181.19 0.09 0.496716154 -0.039374888Valine 117.15 0.165 1.408450704 -0.111648451
Sarah Allred, “Metabolic Modeling of Spider Silk Production in E. coli,” MS Thesis, USU, 2014
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -74-
Impact of Limiting Amino Acids Uptake for Ethanol Production
% GlucoseEthanol_AminoAcid_ReducedCosts_iaf1260e.mclear; changeCobraSolver('glpk','all'); % Set constraintsmodel=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 uptake values for amino acids;model = changeRxnBounds(model,'EX_ala_L(e)',-0.200200247,'l');model = changeRxnBounds(model,'EX_arg_L(e)',-0.065982824,'l');model = changeRxnBounds(model,'EX_asn_L(e)',-0.095998062,'l');model = changeRxnBounds(model,'EX_asp_L(e)',-0.09529124,'l');model = changeRxnBounds(model,'EX_cys_L(e)',-0.013085243,'l');model = changeRxnBounds(model,'EX_gln_L(e)',-0.187137594,'l');model = changeRxnBounds(model,'EX_glu_L(e)',-0.185878393,'l');model = changeRxnBounds(model,'EX_gly(e)',-0.14255367,'l');model = changeRxnBounds(model,'EX_his_L(e)',-0.030655649,'l');model = changeRxnBounds(model,'EX_ile_L(e)',-0.08762833,'l');model = changeRxnBounds(model,'EX_leu_L(e)',-0.126909995,'l');model = changeRxnBounds(model,'EX_lys_L(e)',-0.119293303,'l');model = changeRxnBounds(model,'EX_met_L(e)',-0.02390703,'l');model = changeRxnBounds(model,'EX_phe_L(e)',-0.05758489,'l');model = changeRxnBounds(model,'EX_pro_L(e)',-0.079180891,'l');model = changeRxnBounds(model,'EX_ser_L(e)',-0.098060253,'l');model = changeRxnBounds(model,'EX_thr_L(e)',-0.089838012,'l');model = changeRxnBounds(model,'EX_tyr_L(e)',-0.039374888,'l');model = changeRxnBounds(model,'EX_val_L(e)',-0.111648451,'l');
% Set uptake values for minerals;model = changeRxnBounds(model,'EX_ca2(e)',-0.00237348,'l');model = changeRxnBounds(model,'EX_cobalt2(e)',-1.14e-05,'l');model = changeRxnBounds(model,'EX_ni2(e)',-0.000147288,'l');model = changeRxnBounds(model,'EX_nh4(e)',-6.002150375,'l');model = changeRxnBounds(model,'EX_pi(e)',-5.555386948,'l');model = changeRxnBounds(model,'EX_k(e)',-3.943619038,'l');model = changeRxnBounds(model,'EX_so4(e)',-0.167768684,'l');model = changeRxnBounds(model,'EX_cl(e)',-0.138740225,'l');model = changeRxnBounds(model,'EX_cu2(e)',-0.000634963,'l');model = changeRxnBounds(model,'EX_fe3(e)',-0.00696512,'l');model = changeRxnBounds(model,'EX_mg2(e)',-0.160804933,'l');model = changeRxnBounds(model,'EX_mn2(e)',-0.008010947,'l');model = changeRxnBounds(model,'EX_mobd(e)',-0.001508618,'l');model = changeRxnBounds(model,'EX_na1(e)',-0.102668246,'l');model = changeRxnBounds(model,'EX_thm(e)',-0.000746848,'l');model = 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))
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -75-
Amino Acid Flux Values &Reduced Costs
for Ethanol Productionin M9 Minimal Media
(GlucoseEthonal_ReducedCosts_iJO1366)
Flux ThroughExchange Reactions
EX_ala-L(e) -1EX_arg-L(e) -1.83333EX_asn-L(e) -1EX_asp-L(e) -1EX_cys-L(e) -0.833333EX_gln-L(e) -1.5EX_glu-L(e) -1.5EX_gly(e) -0.5EX_his-L(e) 0EX_ile-L(e) 0EX_leu-L(e) 0EX_lys-L(e) 0EX_met-L(e) 0EX_phe-L(e) 0EX_pro-L(e) -0.5EX_ser-L(e) -0.833333EX_thr-L(e) -1.33333EX_trp-L(e) -0.833333EX_tyr-L(e) 0EX_val-L(e) 0
Amino Acid Reduced Costs
Objective Function = EX_etoh(e)
Biomass 0.246761EX_ca2(e) -0.00128439EX_cl(e) -0.00128439EX_co2(e) 6.90492EX_cobalt2(e) -6.16902e-06EX_cu2(e) -0.000174953EX_etoh(e) 6.41879EX_fe2(e) -0.00203652EX_fe3(e) -0.00192671EX_glc(e) -20EX_glyclt(e)0.000165083EX_h(e) 32.3664EX_h2o(e) 7.08385EX_k(e) -0.048166EX_lac-D(e) 29.9334EX_meoh(e) 4.93522e-07EX_mg2(e) -0.00214065EX_mn2(e) -0.000170512EX_mobd(e) -3.18322e-05EX_nh4(e) -2.66522EX_ni2(e) -7.97038e-05EX_pi(e) -0.238033EX_so4(e) -0.0622356EX_succ(e) 0.0817924EX_zn2(e) -8.41455e-05
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -76-
Amino Acid Flux Values &
Reduced Costs for Ethanol Production in Calculated K-12 Media
(GlucoseEthonal_AminoAcid_ReducedCosts_iaf1260)
EX_15dap(e) 0.119293EX_ala_L(e) -0.2002EX_arg_L(e) -0.0659828EX_asn_L(e) -0.0959981EX_asp_L(e) -0.0952912EX_co2(e)42.0184EX_cys_L(e) -0.0130852EX_etoh(e) 41.3615EX_fe2(e) 0.00696512EX_fe3(e) -0.00696512EX_glc(e) -20EX_gln_L(e) -0.187138EX_glu_L(e) -0.185878EX_gly(e) -0.142554EX_h2o(e)-1.77129EX_h2s(e)0.0130852EX_h(e) -1.98611EX_lys_L(e) -0.119293EX_nh4(e)1.65859EX_pro_L(e) -0.00348256EX_ser_L(e) -0.0980603EX_thr_L(e) -0.089838
Flux ThroughExchange Reactions
EX_ala_L(e) -1EX_arg_L(e) -1.83333EX_asn_L(e) -1EX_asp_L(e) -1EX_cys_L(e) -0.833333EX_gln_L(e) -1.5EX_glu_L(e) -1.5EX_gly(e) -0.5EX_his_L(e) 0EX_ile_L(e) 0EX_leu_L(e) 0EX_lys_L(e) 0EX_met_L(e) 0EX_phe_L(e) 0EX_pro_L(e) 0EX_ser_L(e) -0.833333EX_thr_L(e) -1.33333EX_trp_L(e) -0.833333EX_tyr_L(e) 0EX_val_L(e) 0
Amino Acid Reduced Costs
Objective Function = EX_etoh(e)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -77-
Biliverdin Production(Biliverdin_AminoAcid_ReducedCosts_iJO1366)
EX_co2(e) 14.7976EX_glc(e) -10EX_h(e) 7.97689EX_h2o(e) 45.3757EX_nh4(e) -5.31792EX_o2(e) -12.1387EX_biliverdin(e) 1.32948EX_co(e) 1.32948
Flux ThroughExchange Reactions
EX_ala-L(e) -0.0646425EX_arg-L(e) -0.139079EX_asn-L(e)-0.0705191EX_asp-L(e)-0.0705191EX_cys-L(e) -0.0528893EX_gln-L(e) -0.111655EX_glu-L(e) -0.110676EX_gly(e) -0.0381978EX_his-L(e) 0EX_ile-L(e) 0EX_leu-L(e) 0EX_lys-L(e) 0EX_met-L(e) 0EX_phe-L(e) 0EX_pro-L(e) -0.109696EX_ser-L(e) -0.0558276EX_thr-L(e) -0.0950049EX_trp-L(e) -0.0568071EX_tyr-L(e) 0EX_val-L(e) 0
Amino Acid Reduced Costs
Objective Function = EX_biliverdin(e)
Note: Can’t produce more biliverdin than the induced enzymes from the plasmids can process!
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -78-
Biliverdin Production(Biliverdin_AminoAcid_ReducedCosts_iJO1366)
EX_15dap(e) 0.119293EX_ala-L(e) -0.2002EX_arg-L(e) -0.0659828EX_asn-L(e)-0.0959981EX_asp-L(e)-0.0952912EX_co2(e) 16.3765EX_cys-L(e) -0.0130852EX_glc(e) -10EX_gln-L(e) -0.187138EX_glu-L(e) -0.185878EX_gly(e) -0.142554EX_h(e) 6.533EX_h2o(e) 46.7738EX_h2s(e) 0.0130852EX_lys-L(e) -0.119293EX_nh4(e) -4.00022EX_o2(e) -12.8919EX_pro-L(e) -0.0791809EX_ser-L(e) -0.0980603EX_thr-L(e) -0.089838EX_biliverdin(e) 1.43363EX_co(e) 1.43363
Flux ThroughExchange Reactions
EX_ala-L(e) -0.0646425EX_arg-L(e) -0.123408EX_asn-L(e)-0.0705191EX_asp-L(e)-0.0705191EX_cys-L(e) -0.0528893EX_gln-L(e) -0.110676EX_glu-L(e) -0.110676EX_gly(e) -0.036239EX_his-L(e) 0EX_ile-L(e) 0EX_leu-L(e) 0EX_lys-L(e) 0EX_met-L(e) 0EX_phe-L(e) 0EX_pro-L(e) -0.109696EX_ser-L(e) -0.0558276EX_thr-L(e) -0.0920666EX_trp-L(e) -0.0568071EX_tyr-L(e) 0EX_val-L(e) 0
Amino Acid Reduced Costs
Objective Function = EX_biliverdin(e)
Note: Can’t produce more biliverdin than the induced enzymes from the plasmids can process!
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -79-
EX_co2(e) 10.3513EX_glc(e) -10EX_h2o(e) 22.7635EX_o2(e) -4.14522DM_phb[c] 12.4122
Flux ThroughExchange Reactions
EX_ala-L(e) -0.608696EX_arg-L(e) -1.14783EX_asn-L(e)-0.652174EX_asp-L(e)-0.652174EX_cys-L(e) -0.504348EX_gln-L(e) -1.0087EX_glu-L(e) -1EX_gly(e) -0.347826EX_his-L(e) 0EX_ile-L(e) 0EX_leu-L(e) 0EX_lys-L(e) 0EX_met-L(e) 0EX_phe-L(e) 0EX_pro-L(e) -0.991304EX_ser-L(e) -0.530435EX_thr-L(e) -0.869565EX_trp-L(e) -0.53913EX_tyr-L(e) 0EX_val-L(e) 0
Amino Acid Reduced Costs
PHB Production(PHB_AminoAcid_ReducedCosts_iJO1366)
PHB Pathway
Acetyl-CoA
Objective Function = DM_phb[c]
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -80-
EX_15dap(e) 0.119293EX_ala-L(e) -0.2002EX_arg-L(e) -0.0659828EX_asn-L(e)-0.0959981EX_asp-L(e)-0.0952912EX_co2(e) 11.6396EX_cys-L(e) -0.0130852EX_glc(e) -10EX_gln-L(e) -0.187138EX_glu-L(e) -0.185878EX_gly(e) -0.142554EX_h(e) -2.06877EX_h2o(e) 22.4335EX_h2s(e) 0.0130852EX_lys-L(e) -0.119293EX_nh4(e) 1.73429EX_o2(e) -4.33723EX_pro-L(e) -0.0791809EX_ser-L(e) -0.0980603EX_thr-L(e) -0.089838DM_phb[c] 13.3701
Flux ThroughExchange Reactions
EX_ala-L(e) -0.608696EX_arg-L(e) -1.11304EX_asn-L(e)-0.652174EX_asp-L(e)-0.652174EX_cys-L(e) -0.504348EX_gln-L(e) -1EX_glu-L(e) -1EX_gly(e) -0.347826EX_his-L(e) 0EX_ile-L(e) 0EX_leu-L(e) 0EX_lys-L(e) 0EX_met-L(e) 0EX_phe-L(e) 0EX_pro-L(e) -0.991304EX_ser-L(e) -0.530435EX_thr-L(e) -0.869565EX_trp-L(e) -0.53913EX_tyr-L(e) 0EX_val-L(e) 0
Amino Acid Reduced Costs
PHB Production(PHB_AminoAcid_ReducedCosts_iJO1366)
PHB Pathway
Acetyl-CoA
Objective Function = DM_phb[c]
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H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -81-
EX_co2(e) 16.6918EX_glc(e) -10EX_h(e) 13.1856EX_h2o(e) 36.6126EX_nh4(e) -13.198EX_o2(e) -19.0627EX_so4(e) -0.0247617DM_flys3[c] 0.0123808
Flux ThroughExchange Reactions
EX_ala-L(e) -0.000584658
EX_arg-L(e) -0.00110235
EX_asn-L(e)-0.000661926
EX_asp-L(e)-0.000661926
EX_cys-L(e) -0.00118735
EX_gln-L(e) -0.000958118
EX_glu-L(e) -0.00094524
EX_gly(e) -0.000388914
EX_his-L(e) -0.00134446
EX_ile-L(e) -0.00165353
EX_leu-L(e) 0EX_lys-L(e) -0.00154793
EX_met-L(e) -0.00188018EX_phe-L(e) 0
EX_pro-L(e) -0.00119507
EX_ser-L(e) -0.000558902
EX_thr-L(e) -0.00096327
EX_trp-L(e) -0.000491937
EX_tyr-L(e) -0.00215577
EX_val-L(e) 0
Amino Acid Reduced Costs
Spider Silk Production(Spider_Silk_AminoAcid_ReducedCosts_iJO1366)
Objective Function = DM_flys3[c]
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H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -82-
EX_15dap(e) 0.111878EX_ac(e) 6.38362EX_ala-L(e) -0.2002EX_arg-L(e) -0.0659828EX_asn-L(e) -0.0959981EX_asp-L(e) -0.0952912EX_co2(e) 11.183EX_cys-L(e) -0.0130852EX_for(e) 15.9244EX_glc(e) -10EX_gln-L(e) -0.187138EX_glu-L(e) -0.185878EX_gly(e) -0.142554EX_h(e) 28.0127EX_h2o(e) 20.113EX_h2s(e) 0.0130852EX_his-L(e) -0.0306556EX_ile-L(e) -0.00741544EX_lys-L(e) -0.119293EX_met-L(e) -0.0148309EX_nh4(e) -6.00215EX_o2(e) -20EX_pro-L(e) -0.0791809EX_ser-L(e) -0.0980603EX_thr-L(e) -0.089838EX_tyr-L(e) -0.0393749DM_flys3[c] 0.00741544
Flux ThroughExchange Reactions
EX_ala-L(e) -0.000942507 EX_arg-L(e) -0.00377003 EX_asn-L(e) -0.00188501 EX_asp-L(e) -0.000942507 EX_cys-L(e) -0.000942507 EX_gln-L(e) -0.00188501 EX_glu-L(e) -0.000942507 EX_gly(e) -0.000942507 EX_his-L(e) -0.00282752 EX_ile-L(e) 0 EX_leu-L(e) 0 EX_lys-L(e) 0EX_met-L(e) 0 EX_phe-L(e) 0 EX_pro-L(e) -0.000942507 EX_ser-L(e) -0.000942507 EX_thr-L(e) -0.000942507 EX_trp-L(e) -0.000942507 EX_tyr-L(e) -0.000942507 EX_val-L(e) 0
Amino Acid Reduced CostsSpider Silk Production
(Spider_Silk_AminoAcid_ReducedCosts_iJO1366)
Objective Function = DM_flys3[c]
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -83-
Impact of Uptaking Minerals% GlucoseEthanol_AminoAcid_ReducedCosts_iaf1260e.mclear; changeCobraSolver('glpk','all'); % Set constraintsmodel=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 uptake values for amino acids;model = changeRxnBounds(model,'EX_ala_L(e)',-0.200200247,'l');model = changeRxnBounds(model,'EX_arg_L(e)',-0.065982824,'l');model = changeRxnBounds(model,'EX_asn_L(e)',-0.095998062,'l');model = changeRxnBounds(model,'EX_asp_L(e)',-0.09529124,'l');model = changeRxnBounds(model,'EX_cys_L(e)',-0.013085243,'l');model = changeRxnBounds(model,'EX_gln_L(e)',-0.187137594,'l');model = changeRxnBounds(model,'EX_glu_L(e)',-0.185878393,'l');model = changeRxnBounds(model,'EX_gly(e)',-0.14255367,'l');model = changeRxnBounds(model,'EX_his_L(e)',-0.030655649,'l');model = changeRxnBounds(model,'EX_ile_L(e)',-0.08762833,'l');model = changeRxnBounds(model,'EX_leu_L(e)',-0.126909995,'l');model = changeRxnBounds(model,'EX_lys_L(e)',-0.119293303,'l');model = changeRxnBounds(model,'EX_met_L(e)',-0.02390703,'l');model = changeRxnBounds(model,'EX_phe_L(e)',-0.05758489,'l');model = changeRxnBounds(model,'EX_pro_L(e)',-0.079180891,'l');model = changeRxnBounds(model,'EX_ser_L(e)',-0.098060253,'l');model = changeRxnBounds(model,'EX_thr_L(e)',-0.089838012,'l');model = changeRxnBounds(model,'EX_tyr_L(e)',-0.039374888,'l');model = changeRxnBounds(model,'EX_val_L(e)',-0.111648451,'l');
% Set uptake values for minerals;model = changeRxnBounds(model,'EX_ca2(e)',-0.00237348,'l');model = changeRxnBounds(model,'EX_cobalt2(e)',-1.14e-05,'l');model = changeRxnBounds(model,'EX_ni2(e)',-0.000147288,'l');model = changeRxnBounds(model,'EX_nh4(e)',-6.002150375,'l');model = changeRxnBounds(model,'EX_pi(e)',-5.555386948,'l');model = changeRxnBounds(model,'EX_k(e)',-3.943619038,'l');model = changeRxnBounds(model,'EX_so4(e)',-0.167768684,'l');model = changeRxnBounds(model,'EX_cl(e)',-0.138740225,'l');model = changeRxnBounds(model,'EX_cu2(e)',-0.000634963,'l');model = changeRxnBounds(model,'EX_fe3(e)',-0.00696512,'l');model = changeRxnBounds(model,'EX_mg2(e)',-0.160804933,'l');model = changeRxnBounds(model,'EX_mn2(e)',-0.008010947,'l');model = changeRxnBounds(model,'EX_mobd(e)',-0.001508618,'l');model = changeRxnBounds(model,'EX_na1(e)',-0.102668246,'l');model = changeRxnBounds(model,'EX_thm(e)',-0.000746848,'l');model = 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))
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -84-
MineralFlux Values &
Reduced Costsfor Ethanol Production in Calculated K-12 Media
(GlucoseEthonal_AminoAcid_ReducedCosts_iaf1260)
EX_15dap(e) 0.119293EX_ala_L(e) -0.2002EX_arg_L(e) -0.0659828EX_asn_L(e) -0.0959981EX_asp_L(e) -0.0952912EX_co2(e)42.0184EX_cys_L(e) -0.0130852EX_etoh(e) 41.3615EX_fe2(e) 0.00696512EX_fe3(e) -0.00696512EX_glc(e) -20EX_gln_L(e) -0.187138EX_glu_L(e) -0.185878EX_gly(e) -0.142554EX_h2o(e)-1.77129EX_h2s(e)0.0130852EX_h(e) -1.98611EX_lys_L(e) -0.119293EX_nh4(e)1.65859EX_pro_L(e) -0.00348256EX_ser_L(e) -0.0980603EX_thr_L(e) -0.089838
Flux ThroughExchange Reactions
EX_ca2(e)0EX_cobalt2(e) 0EX_ni2(e) 0EX_nh4(e)0EX_pi(e) 0EX_k(e) 0EX_so4(e)0EX_cl(e) 0EX_cu2(e)0EX_fe3(e) -0.833333EX_mg2(e) 0EX_mn2(e) 0EX_mobd(e) 0EX_na1(e)0EX_thm(e) 0EX_zn2(e)0
MineralReduced Costs
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -85-
Ethanol Production with Limited Nutrients: Robustness Analysis
(GlucoseEthanol_Limited_Media_Mutant_iJO1366.m)
Unconstrained Media Constrained Media
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -86-
Biliverdin Production Limitations
Biliverdin_Robustness_iJO1366.m
Desired Operating
Point
Uptake rate for some amino acids and minerals not sufficient for both cell growth and bioproduct
production
Biliverdin_Media_Robustness_iJO1366.m
Unconstrained Media Constrained Media
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H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -87-
PHB Production Limitations
Uptake rate for some amino acids and minerals are not
sufficient for both cell growth and bioproduct
production
PHB_Media_Robustness_iJO1366.mPHB_Robustness_iJO1366.m
Desired Operating
PointUnconstrained Media Constrained Media
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -88-
Spider Silk Production LimitationsUptake rate for some amino
acids and minerals is not sufficient for both cell growth and bioproduct
production
Spider_Silk_Media_Robustness_iJO1366.mSpider_Silk_Robustness_iJO1366.m
Desired Operating
Point
Unconstrained MediaConstrained Media
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H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -89-
Regulatory Issues
Regulatory constraints are not included in the constraint-based reconstructions.
http://biocyc.org/ECOLI/NEW-IMAGE?type=PATHWAY&object=PROSYN-PWY&detail-level=2
Proline is self-limiting
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -90-
Plasmid Impact• It is widely acknowledged that the metabolism of wild-
type 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 PlasmidBearing
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 plasmidproduction rate on the basis of the experimentally determined production rates of plasmid DNA and β-lactamaseprotein, which is estimated as 3% of total cellular proteinfrom 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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -91-
Review Questions1. 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?
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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.
• 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.
Production Envelope
Optimal Production State
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H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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
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.
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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -95-
MOMA Example
% 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
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)
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -96-
Flux Differences: Wild Type, GDLS, & MOMAReaction WT GDLS MOMA Reaction WT GDLS MOMA Reaction WT GDLS MOMA
ACALD 2.27E-13 -7.38652 -5.38586 EX_lac_D(e) 0 0 2.39504 NADTRHD 0 1.1172 0ACONTa 3.44346 1.87357 0.010514 EX_nh4(e) -2.26617 -0.27522 -0.05314 NH4t 2.26617 0.275221 0.05314ACONTb 3.44346 1.87357 0.010514 EX_o2(e) -11.8336 -0.90956 0 O2t 11.8336 0.909557 0AKGDH 2.99507 1.81911 0 EX_pi(e) -1.52886 -0.18568 -0.03585 PDH 5.00103 0 5.36461ALCD2x 0 -7.38652 -5.38586 FBA 3.89814 4.92254 3.95219 PFK 3.89814 4.92254 3.95219ATPM 8.39 8.39 8.39 FORti 0 9.44925 0.068288 PFL 0 9.44925 0.068288ATPS4r 24.8712 0.094332 1.27156 FRD7 0 0 1.49752 PGI 2.8494 4.9079 3.94936Biomass 0.415598 0.050473 0.009745 FUM 2.99507 1.81911 0 PGK -8.20675 -9.83858 -7.90311CO2t -12.314 -3.6298 -5.36298 G6PDH2r 2.06541 0.081752 0.015785 PGL 2.06541 0.081752 0.015785CS 3.44346 1.87357 0.010514 GAPD 8.20675 9.83858 7.90311 PGM -7.58501 -9.76307 -7.88854CYTBD 23.6671 1.81911 0 GLCpts 5 5 3.96714 PIt2r 1.52886 0.185676 0.035851D_LACt2 0 0 -2.39504 GLNS 0.106268 0.012906 0.002492 PPC 1.19094 0.144636 0.163454ENO 7.58501 9.76307 7.88854 GLUDy -2.1599 -0.26232 -0.05065 PYK 1.17834 4.59223 3.75288ETOHt2r 0 -7.38652 -5.38586 GND 2.06541 0.081752 0.015785 RPE 1.07821 0.018221 0.003518EX_co2(e) 12.314 3.6298 5.36298 H2Ot -15.3414 3.38905 -0.04811 RPI -0.9872 -0.06353 -0.01227EX_etoh(e) 0 7.38652 5.38586 ICDHyr 3.44346 1.87357 0.010514 SUCDi 2.99507 1.81911 1.49752EX_for(e) 0 9.44925 0.068288 LDH_D 0 0 -2.39504 SUCOAS -2.99507 -1.81911 0EX_glc(e) -5 -5 -3.96714 MDH 2.99507 1.81911 -0.13553 TALA 0.614118 0.018221 0.003518EX_h2o(e) 15.3414 -3.38905 0.048112 ME2 0 0 0.135527 TKT1 0.614118 0.018221 0.003518EX_h(e) 8.33689 10.4617 2.65882 NADH16 20.672 0 0 TKT2 0.464088 0 0
TPI 3.89814 4.92254 3.95219
EthanolProduction_GDLS_Mutants_MOMA.m
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -97-
Flux Maps: Wild Type, GDLS, & MOMA
EthanolProduction_GDLS_Mutant.mEthanolProduction_WT.m EthanolProduction_GDLS_Mutants_MOMA.m
Wild Type GDLS-Mutant MOMA Result
X
X
X
X
X
X
Loopq8, q8h2
nadh for TCA
Lactate
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -98-
Production Envelope – Ethanol Production Mutant
(0.050473, 7.3865)
(0.009745, 5.38586)
MOMAOperating
Point
FBAOperating
Point
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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?
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
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Desired Cell Evolution
(0.050473, 7.3865)
(0.009745, 5.38586)
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H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -102-
Adaptive Laboratory Evolution
B. Palsson (2010). “Adaptive Laboratory Evolution.” Microbe, 6(2):69-74
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H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -103-
Adaptive Laboratory Evolution Methods
Dragosits, M. and D. Mattanovich (2013). "Adaptive laboratory evolution -- principles and applications for biotechnology." Microbial cell factories 12: 64.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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 networksConrad, T. M., N. E. Lewis, et al. (2011). "Microbial laboratory evolution in the era of genome-scale science." Molecular Systems Biology 7: 509.
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H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -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).
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.
2 5
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Growth of E.coli K-12 on Malate
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.
Blue dots are starting and ending points
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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.
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Growth of E.coli K-12 on Glycerol
(Ibarra, 2002)
30°C
37°C
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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.
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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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Evolution of Bioproduction Tools
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
MOMA
OptKnock
OptStrain
OptGene
ROOM
OptReg GDLS
OptOrf
OptForce
EMILiO
SimOptStrain
Fiest
Available in Cobra Toolbox GAMS
OptCom K-OptForce
Matlab CPLEX ILOG Solver
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OptStrainOptStrain 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.
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.
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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.
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.
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OptOrf
Kim, J. and J. L. Reed (2010). "OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains." BMC systems biology 4: 53.
• 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
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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). 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
• 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
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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, substrate-specific 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.
• Substrate-specific productivity (SSP): Product yield per unit substrate multiplied by the growth rate
• Strength of growth coupling (SGC): Product yield per unit substrate divided by the slope of the lower edge of the production curve
/product
p ssubstrate
production rate mmol productY
consumption rate mmol substrate
product
substrate
production rate growth rate mmol productSSP
consumption rate mmol substrate hr
2product yield mmol productSGC
slope 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 non-linear 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 delPenalty∗ numDels
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.
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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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Problem Formulation: Reduction of Model and Selection of Targeted Reactions
Cobra Model
Remove dead-ends & minimize flux
ranges
Remove essential & nearly essential
reactions
Remove non-gene associated or spontaneous
reactions(including key subsystems)
Remove transport reactions and periphery metabolic
pathways
Remove reactions that act on high-carbon containing molecules
Determine co-sets: only consider one reaction in a correlated set
Target reactions
reduceModel()detectDeadEnds()removeDeadEnds
()
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.
pFBA()
identifyCorrelSets()
findExcRxns()findTransRxns()
findHiCarbobRxns()
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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.
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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.
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The Strain Designs Generated for Five Different Targets from Glucose and Xylose Anaerobically
glucose 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.
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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
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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 trade-offs 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.
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
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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 stoichiometry-alone 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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Review Questions
1. Describe the key features of OptStrain.
2. Describe the key features of ROOM.
3. Describe the key features of OptReg.
4. Describe the key features of OptOrf.
5. Describe the key features of OptForce.
6. Describe the key features of SimOptStrain.
7. Describe the key features of the 2010 Feist Strain Design System.
8. Describe the key features of OptCom.
9. Describe the key 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)?
Lesson: Bioproduct ProductionBIE 5500/6500Utah State University
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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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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.