Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015...

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University

H. Scott Hinton, 2015Constraint-based Metabolic Reconstructions & Analysis -1-

Bioproduct Production



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 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



iAF1260Model

(GlucoseEthanol_iaf1260.pdf)



iAF1260Metabolic

Core(GlucoseEthanol_iaf1260.pdf)



iAF1260Amino Acid Metabolism(GlucoseEthanol_iaf1260.pdf)



iAF1260NecleotideMetabolism(GlucoseEthanol_iaf1260.pdf)



iAF1260Alternate

Carbon Sources(GlucoseEthanol_iaf1260.pdf)



iAF1260Cofactor

Biosynthesis(GlucoseEthanol_iaf1260.pdf)



iAF1260Inorganic Ion

Transport(GlucoseEthanol_iaf1260.pdf)



Fatty acid Biosynthesis(GlucoseEthanol_iaf1260.pdf)



tRNA Charging(GlucoseEthanol_iaf1260.pdf)



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.



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



Core and Pan Metabolic Capabilities of the E. coli Species.




Clustering of Species by Unique Growth-supporting Conditions


655 combinations



Predicting Microbial Growth

Monk, J. and B. O. Palsson (2014). "Genetics. Predicting microbial growth." Science 344(6191): 1448-1449.

Single KnockoutDouble Knockouts



Lesson Outline

• Overview








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.



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



OptKnock Example: Maximizing Ethanol Production

(EthanolProduction_OptKnock_Gurobi.m)% EthanolProduction_OptKnock_Gurobi_Revised.m

clear; clc;



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');


% 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)



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.



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



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



BiliverdinGene

Dong Chen, USU SBI Bioproducts Summit, February 2012



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



Location of Heme Pathway in iAF1260

Model

BIGG Models @ http://bigg.ucsd.edu/



Biliverdin Pathway in iAF1260

BILIVERDINtexBILIVERDINtppEX_biliverdin(e)

HEMEOX

nadph h o2

biliverdin[c]

h2ocofe2nadp

biliverdin[p] biliverdin[e]

Biliverdin Pathway



Biliverdin Complete Pathway

Secreted Biliverdin

α-ketoglutarateL-Glutamate



Key Components of the Biliverdin Pathway in iAF1260

ATP

L-Glutamate

NADPH

O2 Fe2 ATP



What are the Limits of Biliverdin Production?

Biliverdin_Robustness_iJO1366.m

Desired Operating

Point

Minimum Production

Rate

Maximum

Growth and Production

OpportunitySpace




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;



% 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



% Biliverdin_optKnock_iJO1366_Precalculated_Reactions.m

clear; clc;



Add biliverdin reactions

% Set key variables



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



% Biliverdin_OptKnock_iJO1366_WithPrecalculatedReactions.m

clear; clc;



Add Biliverdin Reactions

% Set key variables



% Set optimization objective


% 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



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?



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.



Biliverdin Production


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



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



Metabolic Genome-Scale Metabolic

Modelingin E.coli

BIGG Models @ http://bigg.ucsd.edu/



PHBPathway

PHB Pathway

Acetyl-CoA



What are the Limits of PHB Production?

PHB_Robustness_iJO1366.m

Minimum Production

Rate



Adding PHB Reactions

% PHB_OptKnock_iJO1366_Precalculated_Reactions.m

clear; clc;


% 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



PHB Production


Desired Operating

Point

Knockouts = 'MDH','PSP_L','PTAr','TPI'

Production of unregulated PHB pathway

PHB_ProductionEnvelope_iJO1366.m



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



Spider Silk: Mechanical Properties


Spider Silk Protein



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




Spider Silk Production

in E.coli



What are the Limits of Spider Silk Production?

Spider_Silk_Robustness_iJO1366.m

Desired Operating

Point

Minimum Production

Rate



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


% Setting carbon source and oxygen





Spider Silk Production

Spider_Silk_ProductionEnvelope_iJO1366.mSpider_Silk_Robustness_iJO1366.m

Desired Operating

Point

Minimum Production

Rate


3.3Allred Production Rate

OpportunitySpace



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 Outline

• Overview








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.



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.



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]



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.



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]



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.



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



Amino Acid Flux Values &Reduced Costs

for Ethanol Productionin M9 Minimal Media

(GlucoseEthonal_ReducedCosts_iJO1366)


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)




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'



Production Envelopes of Additional L-Arginine for Ethanol Production(Biliverdin_Robustness_iJO1366.m & Biliverdin_Mutants_iJO1366.m)


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'



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


EX_biliverdin(e) LB = 1.2

Assume a non-regulated enzyme produced by a plasmid



L-Glutamate Metabolite Connectivity

L-Glutamate can feed up to 49 different

reactions

VisualizeGlutamate_iJO1366.m

ecoli_iJO1366 Model



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


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


Objective Function = EX_biliverdin(e)

Note: Can’t produce more biliverdin than the induced enzymes from the plasmids can process!



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



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


PHB Production(PHB_Mutants_iJO1366)

PHB Pathway

Acetyl-CoA


Assume a non-regulated enzyme produced by a plasmid



EX_co2(e) 10.3513EX_glc(e) -10EX_h2o(e) 22.7635EX_o2(e) -4.14522DM_phb[c] 12.4122


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


PHB Production(PHB_AminoAcid_ReducedCosts_iJO1366)

PHB Pathway

Acetyl-CoA

Objective Function = DM_phb[c]



Impact of Additional L-Arginine on PHB Production(Biliverdin_Robustness_iJO1366.m & Biliverdin_Mutants_iJO1366.m)


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



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


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


Spider Silk Production(Spider_Silk_AminoAcid_ReducedCosts_iJO1366)

Objective Function = DM_flys3[c]



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



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



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




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))



Amino Acid Flux Values &Reduced Costs

for Ethanol Productionin M9 Minimal Media

(GlucoseEthonal_ReducedCosts_iJO1366)


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 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


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






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









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








EX_co2(e) 10.3513EX_glc(e) -10EX_h2o(e) 22.7635EX_o2(e) -4.14522DM_phb[c] 12.4122


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



PHB Pathway

Acetyl-CoA




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


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



PHB Pathway

Acetyl-CoA




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


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


Spider Silk Production(Spider_Silk_AminoAcid_ReducedCosts_iJO1366)




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


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)




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))



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


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



Ethanol Production with Limited Nutrients: Robustness Analysis

(GlucoseEthanol_Limited_Media_Mutant_iJO1366.m)

Unconstrained Media Constrained Media



Biliverdin Production Limitations


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



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



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



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



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.



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 Outline

• Overview








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



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.



MOMA Example

% EthanolProduction_GDLS_Mutants_MOMA.m

clear;



% Set carbon source and oxygen uptake rates




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)



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



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



Production Envelope – Ethanol Production Mutant

(0.050473, 7.3865)

(0.009745, 5.38586)

MOMAOperating

Point

FBAOperating

Point



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 Outline

• Overview








Desired Cell Evolution

(0.050473, 7.3865)

(0.009745, 5.38586)



Adaptive Laboratory Evolution

B. Palsson (2010). “Adaptive Laboratory Evolution.” Microbe, 6(2):69-74



Adaptive Laboratory Evolution Methods

Dragosits, M. and D. Mattanovich (2013). "Adaptive laboratory evolution -- principles and applications for biotechnology." Microbial cell factories 12: 64.



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.



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.



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



Growth of E.coli K-12 on Malate


Blue dots are starting and ending points



Growth of E.coli K-12 on Glucose




Growth of E.coli K-12 on Glycerol

(Ibarra, 2002)

30°C

37°C



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?




Lesson Outline

• Overview








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



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.



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.



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



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



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.



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.



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.




Strain Design




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

()


pFBA()

identifyCorrelSets()

findExcRxns()findTransRxns()

findHiCarbobRxns()



Substrate Conditions




Theoretical maximum production Yp/s (%) analysis.




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.



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.



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.



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 Outline

• Overview








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.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015...

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