Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -1- Bioproduct.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -1- Bioproduct Production

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-based Metabolic Reconstructions & Analysis -4- iAF1260 Model (GlucoseEthanol_iaf1260.pdf)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -5- iAF1260 Metabolic Core (GlucoseEthanol_iaf1260.pdf)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -6- iAF1260 Amino Acid Metabolism (GlucoseEthanol_iaf1260.pdf)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -7- iAF1260 Necleotide Metabolism (GlucoseEthanol_iaf1260.pdf)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -8- iAF1260 Alternate Carbon Sources (GlucoseEthanol_iaf1260.pdf)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -9- iAF1260 Cofactor Biosynthesis (GlucoseEthanol_iaf1260.pdf)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -10- iAF1260 Inorganic Ion Transport (GlucoseEthanol_iaf1260.pdf)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -11- Fatty acid Biosynthesis (GlucoseEthanol_iaf1260.pdf)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -12- tRNA Charging (GlucoseEthanol_iaf1260.pdf)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -13- Enterobacterial Common Antigen (GlucoseEthanol_iaf1260.pdf) Enterobacterial common antigen (ECA), also referred to as an endotoxin, is a family-specific surface antigen shared by all members of the Enterobacteriaceae and is restricted to this family. It is found in freshly isolated wild-type strains as well as in laboratory strains like Escherichia coli K-12. ECA is located in the outer leaflet of the outer membrane. It is a glycophospholipid built up by an aminosugar heteropolymer linked to an L-glycerophosphatidyl residue. Kuhn, H. M., U. Meier-Dieter, et al. (1988). "ECA, the enterobacterial common antigen." FEMS microbiology reviews 4(3): 195-222.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-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 Knockout Double Knockouts

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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 production with 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, 2015 Constraint-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 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, 2015 Constraint-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, 2015 Constraint-based Metabolic Reconstructions & Analysis -23- Adding Reactions addReaction - Add a reaction to the model or modify an existing reaction model = addReaction(model,rxnName,metaboliteList,stoichCoeffList,revFlag,lowerBound,upperBound,objCoeff,subSystem,grRule,geneNameList,systNameList,checkDuplicate) model = addReaction(model,rxnName,rxnFormula) INPUTS modelCOBRA model structure rxnNameReaction 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) lowerBoundLower bound (Default = 0 or -vMax) upperBoundUpper bound (Default = vMax) objCoeffObjective coefficient (Default = 0) subsystemSubsystem (Default = '') grRuleGene-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, 2015 Constraint-based Metabolic Reconstructions & Analysis -24- Example #1: Biliverdin Potential Biliverdin protective effects: endotoxic shock brain infarction vascular intimal hyperplasia vascular endothelial dysfunction lung injury airway inflammation ischemia reperfusion injury tissue graft rejection corneal epithelial injury hepatitis C infection artheriosclerosis colitis cancer cell proliferation type 2 diabetes type 1 diabetes (pancreatic Islet cell protection) biliverdin heme heme oxygenase + O 2 + Fe + CO + e - a Jon Takemoto, USU SBI Bioproducts Summit, February 2012

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -25- Biliverdin Gene Dong Chen, USU SBI Bioproducts Summit, February 2012

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-based Metabolic Reconstructions & Analysis -28- Biliverdin Pathway in iAF1260 BILIVERDINtex BILIVERDINtpp EX_biliverdin(e) HEMEOX nadphho2 biliverdin[c] h2o co fe2nadp biliverdin[p]biliverdin[e] Biliverdin Pathway

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -29- Biliverdin Complete Pathway Secreted Biliverdin α-ketoglutarate L-Glutamate

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -30- Key Components of the Biliverdin Pathway in iAF1260 ATP L-Glutamate NADPH O2O2 Fe 2 ATP

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

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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 BILIVERDINtex BILIVERDINtpp EX_biliverdin(e) HEMEOX nadphho2 biliverdin[c] h2o co fe2nadp biliverdin[p]biliverdin[e] pheme[c] Biliverdin Pathway Default Reaction Settings: UB = 1000 LB = -1000

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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 OptKnock Load ‘selectedRxns’ from“mat” file

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -35- Biliverdin OptKnock Results 1 Knockout ans = 'PPKr' growthRate = 0.2325 minProd = 0 maxProd = 0 5 Knockouts ans = 'G6PDA' growthRate = 0.9824 minProd = 0 maxProd = 1.7997e-08 2 Knockouts ans = Empty cell array growthRate = 0.2325 minProd = 0 maxProd = 0 10 Knockouts ans = 'CITL‘,'FUM‘,'G6PDA‘,'I4FE4ST‘, 'ICL‘,'TKT2‘,'XAND'' growthRate = 0.2325 minProd = 0 maxProd = 0 WHY?

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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. 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, 2015 Constraint-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, 2015 Constraint-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 nadp 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, 2015 Constraint-based Metabolic Reconstructions & Analysis -39- Metabolic Genome-Scale Metabolic Modeling in E.coli BIGG Models @ http://bigg.ucsd.edu/

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -40- PHB Pathway Acetyl-CoA

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-based Metabolic Reconstructions & Analysis -46- Spider Silk: Flagelliform Composition 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, 2015 Constraint-based Metabolic Reconstructions & Analysis -47- Spider Silk Production in E.coli

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-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 3.3 Allred Production Rate Opportunity Space

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-based Metabolic Reconstructions & Analysis -54- iAF1260 Amino Acid Exchange Reactions 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, 2015 Constraint-based Metabolic Reconstructions & Analysis -55- iaf1260 Biomass Objective Function Amino Acid Components (Ec_biomass_iAF1260_core_59p81M) 0.000223 10fthf[c] + 0.000223 2ohph[c] + 0.5137 ala-L[c] + 0.000223 amet[c] + 0.2958 arg-L[c] + 0.2411 asn-L[c] + 0.2411 asp-L[c] + 59.984 atp[c] + 0.004737 ca2[c] + 0.004737 cl[c] + 0.000576 coa[c] + 0.003158 cobalt2[c] + 0.1335 ctp[c] + 0.003158 cu2[c] + 0.09158 cys-L[c] + 0.02617 datp[c] + 0.02702 dctp[c] + 0.02702 dgtp[c] + 0.02617 dttp[c] + 0.000223 fad[c] + 0.007106 fe2[c] + 0.007106 fe3[c] + 0.2632 gln-L[c] + 0.2632 glu-L[c] + 0.6126 gly[c] + 0.2151 gtp[c] + 54.462 h2o[c] + 0.09474 his-L[c] + 0.2905 ile-L[c] + 0.1776 k[c] + 0.01945 kdo2lipid4[e] + 0.4505 leu-L[c] + 0.3432 lys-L[c] + 0.1537 met-L[c] + 0.007895 mg2[c] + 0.000223 mlthf[c] + 0.003158 mn2[c] + 0.003158 mobd[c] + 0.01389 murein5px4p[p] + 0.001831 nad[c] + 0.000447 nadp[c] + 0.011843 nh4[c] + 0.02233 pe160[c] + 0.04148 pe160[p] + 0.02632 pe161[c] + 0.04889 pe161[p] + 0.1759 phe-L[c] + 0.000223 pheme[c] + 0.2211 pro-L[c] + 0.000223 pydx5p[c] + 0.000223 ribflv[c] + 0.2158 ser-L[c] + 0.000223 sheme[c] + 0.003948 so4[c] + 0.000223 thf[c] + 0.000223 thmpp[c] + 0.2537 thr-L[c] + 0.05684 trp-L[c] + 0.1379 tyr-L[c] + 5.5e-005 udcpdp[c] + 0.1441 utp[c] + 0.4232 val-L[c] + 0.003158 zn2[c] -> 59.81 adp[c] + 59.81 h[c] + 59.806 pi[c] + 0.7739 ppi[c]

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -56- iAF1260 Model Mineral Exchanges Note that all the minerals except cob(I)alamin allow unlimited uptake.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -57- iaf1260 Biomass Objective Function Mineral Components (Ec_biomass_iAF1260_core_59p81M) 0.000223 10fthf[c] + 0.000223 2ohph[c] + 0.5137 ala-L[c] + 0.000223 amet[c] + 0.2958 arg-L[c] + 0.2411 asn-L[c] + 0.2411 asp-L[c] + 59.984 atp[c] + 0.004737 ca2[c] + 0.004737 cl[c] + 0.000576 coa[c] + 0.003158 cobalt2[c] + 0.1335 ctp[c] + 0.003158 cu2[c] + 0.09158 cys-L[c] + 0.02617 datp[c] + 0.02702 dctp[c] + 0.02702 dgtp[c] + 0.02617 dttp[c] + 0.000223 fad[c] + 0.007106 fe2[c] + 0.007106 fe3[c] + 0.2632 gln-L[c] + 0.2632 glu-L[c] + 0.6126 gly[c] + 0.2151 gtp[c] + 54.462 h2o[c] + 0.09474 his-L[c] + 0.2905 ile-L[c] + 0.1776 k[c] + 0.01945 kdo2lipid4[e] + 0.4505 leu-L[c] + 0.3432 lys-L[c] + 0.1537 met-L[c] + 0.007895 mg2[c] + 0.000223 mlthf[c] + 0.003158 mn2[c] + 0.003158 mobd[c] + 0.01389 murein5px4p[p] + 0.001831 nad[c] + 0.000447 nadp[c] + 0.011843 nh4[c] + 0.02233 pe160[c] + 0.04148 pe160[p] + 0.02632 pe161[c] + 0.04889 pe161[p] + 0.1759 phe-L[c] + 0.000223 pheme[c] + 0.2211 pro-L[c] + 0.000223 pydx5p[c] + 0.000223 ribflv[c] + 0.2158 ser-L[c] + 0.000223 sheme[c] + 0.003948 so4[c] + 0.000223 thf[c] + 0.000223 thmpp[c] + 0.2537 thr-L[c] + 0.05684 trp-L[c] + 0.1379 tyr-L[c] + 5.5e-005 udcpdp[c] + 0.1441 utp[c] + 0.4232 val-L[c] + 0.003158 zn2[c] -> 59.81 adp[c] + 59.81 h[c] + 59.806 pi[c] + 0.7739 ppi[c]

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -58- in silico M9 Minimal Media Reaction AbbreviationReaction NameFormulaLower BoundUpper Bound EX_ca2(e)Calcium exchangeca2[e] -10001000 EX_cl(e)Chloride exchangecl[e] -10001000 EX_co2(e)CO2 exchangeco2[e] -10001000 EX_cobalt2(e)Co2+ exchangecobalt2[e] -10001000 EX_cu2(e)Cu2+ exchangecu2[e] -10001000 EX_fe2(e)Fe2+ exchangefe2[e] -10001000 EX_fe3(e)Fe3+ exchangefe3[e] -10001000 EX_h(e)H+ exchangeh[e] -10001000 EX_h2o(e)H2O exchangeh2o[e] -10001000 EX_k(e)K+ exchangek[e] -10001000 EX_mg2(e)Mg exchangemg2[e] -10001000 EX_mn2(e)Mn2+ exchangemn2[e] -10001000 EX_mobd(e)Molybdate exchangemobd[e] -10001000 EX_na1(e)Sodium exchangena1[e] -10001000 EX_tungs(e)tungstate exchangetungs[e] -10001000 EX_zn2(e)Zinc exchangezn2[e] -10001000 EX_ni2(e)Ni2+ exchangeni2[e] -10001000 EX_sel(e)Selenate exchangesel[e] -10001000 EX_slnt(e)selenite exchangeslnt[e] -10001000 EX_so4(e)Sulfate exchangeso4[e] -10001000 EX_nh4(e)Ammonia exchangenh4[e] -10001000 EX_pi(e)Phosphate exchangepi[e] -10001000 EX_cbl1(e)Cob(I)alamin exchangecbl1[e] -0.011000 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, 2015 Constraint-based Metabolic Reconstructions & Analysis -59- Ethanol Production (GlucoseEthanol_Mutant_iJO1366.m & GlucoseEthanol_multiProductionEnvelope_iJO1366.m) Picture of ethanol producing map in 1260 Biomass0.246761 EX_ca2(e)-0.00128439 EX_cl(e)-0.00128439 EX_co2(e)6.90492 EX_cobalt2(e)-6.16902e-06 EX_cu2(e)-0.000174953 EX_etoh(e)6.41879 EX_fe2(e)-0.00203652 EX_fe3(e)-0.00192671 EX_glc(e)-20 EX_glyclt(e)0.000165083 EX_h(e)32.3664 EX_h2o(e)7.08385 EX_k(e)-0.048166 EX_lac-D(e)29.9334 EX_meoh(e)4.93522e-07 EX_mg2(e)-0.00214065 EX_mn2(e)-0.000170512 EX_mobd(e)-3.18322e-05 EX_nh4(e)-2.66522 EX_ni2(e)-7.97038e-05 EX_pi(e)-0.238033 EX_so4(e)-0.0622356 EX_succ(e)0.0817924 EX_zn2(e)-8.41455e-05 Flux Through Exchange Reactions Assumptions: Aerobic Glucose = -20 Knockouts (OptKnock): 'PFL','PPC','PPKr' Barely Coupled

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -60- Amino Acid Flux Values & Reduced Costs for Ethanol Production in M9 Minimal Media (GlucoseEthonal_ReducedCosts_iJO1366) Flux Through Exchange Reactions EX_ala-L(e)-1 EX_arg-L(e)-1.83333 EX_asn-L(e)-1 EX_asp-L(e)-1 EX_cys-L(e)-0.833333 EX_gln-L(e)-1.5 EX_glu-L(e)-1.5 EX_gly(e)-0.5 EX_his-L(e)0 EX_ile-L(e)0 EX_leu-L(e)0 EX_lys-L(e)0 EX_met-L(e)0 EX_phe-L(e)0 EX_pro-L(e)-0.5 EX_ser-L(e)-0.833333 EX_thr-L(e)-1.33333 EX_trp-L(e)-0.833333 EX_tyr-L(e)0 EX_val-L(e)0 Amino Acid Reduced Costs Objective Function = EX_etoh(e) Biomass0.246761 EX_ca2(e)-0.00128439 EX_cl(e)-0.00128439 EX_co2(e)6.90492 EX_cobalt2(e)-6.16902e-06 EX_cu2(e)-0.000174953 EX_etoh(e)6.41879 EX_fe2(e)-0.00203652 EX_fe3(e)-0.00192671 EX_glc(e)-20 EX_glyclt(e)0.000165083 EX_h(e)32.3664 EX_h2o(e)7.08385 EX_k(e)-0.048166 EX_lac-D(e)29.9334 EX_meoh(e)4.93522e-07 EX_mg2(e)-0.00214065 EX_mn2(e)-0.000170512 EX_mobd(e)-3.18322e-05 EX_nh4(e)-2.66522 EX_ni2(e)-7.97038e-05 EX_pi(e)-0.238033 EX_so4(e)-0.0622356 EX_succ(e)0.0817924 EX_zn2(e)-8.41455e-05

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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 = 0EX_arg-L(e) Lower Bound = -1EX_arg-L(e) Lower Bound = -20 Biomass0.246761 EX_etoh(e)6.41879 EX_glc(e)-20 EX_lac-D(e)29.9334 EX_nh4(e)-2.66522 EX_succ(e)0.0817924 Biomass0.148636 EX_arg-L(e)-1 EX_etoh(e)0 EX_glc(e)-20 EX_lac-D(e)28.0093 EX_nh4(e)2.39461 EX_succ(e)0.889455 Biomass0.412812 EX_ac(e)2.70698 EX_arg-L(e)-2.77733 EX_etoh(e)0 EX_glc(e)-20 EX_lac-D(e)37.1847 EX_nh4(e)6.65062 EX_succ(e)0.136832 'ARGDC‘, 'PFL‘, 'PSP_L' 'PFL‘, 'PPC‘, 'PSERT''PFL‘, 'PPC‘, 'PPKr'

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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 = 0EX_arg-L(e) Lower Bound = -1EX_arg-L(e) Lower Bound = -20 Biomass0.246761 EX_etoh(e)6.41879 EX_glc(e)-20 EX_lac-D(e)29.9334 EX_nh4(e)-2.66522 EX_succ(e)0.0817924 Biomass0.148636 EX_arg-L(e)-1 EX_etoh(e)0 EX_glc(e)-20 EX_lac-D(e)28.0093 EX_nh4(e)2.39461 EX_succ(e)0.889455 Biomass0.412812 EX_ac(e)2.70698 EX_arg-L(e)-2.77733 EX_etoh(e)0 EX_glc(e)-20 EX_lac-D(e)37.1847 EX_nh4(e)6.65062 EX_succ(e)0.136832 'ARGDC‘, 'PFL‘, 'PSP_L' 'PFL‘, 'PPC‘, 'PSERT''PFL‘, 'PPC‘, 'PPKr'

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -63- Biliverdin Production (Biliverdin_Mutants_iJO1366) Biomass0.103363 EX_ca2(e)-0.000538004 EX_cl(e)-0.000538004 EX_co2(e)14.9571 EX_cobalt2(e)-2.58407e-06 EX_cu2(e)-7.32843e-05 EX_fe2(e)-0.00166011 EX_glc(e)-10 EX_h(e)8.14972 EX_h2o(e)45.2869 EX_k(e)-0.0201757 EX_meoh(e)2.06726e-07 EX_mg2(e)-0.000896673 EX_mn2(e)-7.14238e-05 EX_mobd(e)-1.33338e-05 EX_nh4(e)-5.9164 EX_ni2(e)-3.33862e-05 EX_o2(e)-12.3366 EX_pi(e)-0.0997071 EX_so4(e)-0.0260692 EX_zn2(e)-3.52468e-05 EX_biliverdin(e)1.2 EX_co(e)1.2 Flux Through Exchange 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, 2015 Constraint-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, 2015 Constraint-based Metabolic Reconstructions & Analysis -65- Biliverdin Production (Biliverdin_AminoAcid_ReducedCosts_iJO1366) EX_co2(e)14.7976 EX_glc(e)-10 EX_h(e)7.97689 EX_h2o(e)45.3757 EX_nh4(e)-5.31792 EX_o2(e)-12.1387 EX_biliverdin(e)1.32948 EX_co(e)1.32948 Flux Through Exchange Reactions EX_ala-L(e)-0.0646425 EX_arg-L(e)-0.139079 EX_asn-L(e)-0.0705191 EX_asp-L(e)-0.0705191 EX_cys-L(e)-0.0528893 EX_gln-L(e)-0.111655 EX_glu-L(e)-0.110676 EX_gly(e)-0.0381978 EX_his-L(e)0 EX_ile-L(e)0 EX_leu-L(e)0 EX_lys-L(e)0 EX_met-L(e)0 EX_phe-L(e)0 EX_pro-L(e)-0.109696 EX_ser-L(e)-0.0558276 EX_thr-L(e)-0.0950049 EX_trp-L(e)-0.0568071 EX_tyr-L(e)0 EX_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, 2015 Constraint-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 = 0EX_glu-L(e) Lower Bound = -0.1859EX_glu-L(e) Lower Bound = -20 Biomass0.103363 EX_glc(e)-10 EX_nh4(e)-5.9164 EX_o2(e)-12.3366 EX_biliverdin(e)1.2 EX_co(e)1.2 Biomass0.119785 EX_glc(e)-10 EX_glu-L(e)-0.185878 EX_nh4(e)-5.9079 EX_o2(e)-12.4638 EX_biliverdin(e)1.2 EX_co(e)1.2 Biomass0.952339 EX_ac(e)22.8365 EX_glc(e)-10 EX_glu-L(e)-20 EX_nh4(e)4.91396 EX_o2(e)-20 EX_biliverdin(e)1.2 EX_co(e)1.2 EX_biliverdin(e) ≥ 1.2

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -67- Biomass0.206854 EX_ca2(e)-0.00107667 EX_cl(e)-0.00107667 EX_co2(e)11.509 EX_cobalt2(e)-5.17134e-06 EX_cu2(e)-0.000146659 EX_fe2(e)-0.00332228 EX_glc(e)-10 EX_h(e)1.90062 EX_h2o(e)26.9719 EX_k(e)-0.0403764 EX_meoh(e)4.13707e-07 EX_mg2(e)-0.00179445 EX_mn2(e)-0.000142936 EX_mobd(e)-2.66841e-05 EX_nh4(e)-2.23419 EX_ni2(e)-6.68137e-05 EX_o2(e)-6.06759 EX_pi(e)-0.199537 EX_so4(e)-0.0521705 EX_zn2(e)-7.05371e-05 DM_phb[c]10 Flux Through Exchange 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, 2015 Constraint-based Metabolic Reconstructions & Analysis -68- EX_co2(e)10.3513 EX_glc(e)-10 EX_h2o(e)22.7635 EX_o2(e)-4.14522 DM_phb[c]12.4122 Flux Through Exchange Reactions EX_ala-L(e)-0.608696 EX_arg-L(e)-1.14783 EX_asn-L(e)-0.652174 EX_asp-L(e)-0.652174 EX_cys-L(e)-0.504348 EX_gln-L(e)-1.0087 EX_glu-L(e)-1 EX_gly(e)-0.347826 EX_his-L(e)0 EX_ile-L(e)0 EX_leu-L(e)0 EX_lys-L(e)0 EX_met-L(e)0 EX_phe-L(e)0 EX_pro-L(e)-0.991304 EX_ser-L(e)-0.530435 EX_thr-L(e)-0.869565 EX_trp-L(e)-0.53913 EX_tyr-L(e)0 EX_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, 2015 Constraint-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 = 0EX_arg-L(e) Lower Bound = -1EX_arg-L(e) Lower Bound = -20 Biomass0.206854 EX_glc(e)-10 EX_nh4(e)-2.23419 EX_o2(e)-6.06759 DM_phb[c]10 Biomass0.30366 EX_arg-L(e)-1 EX_glc(e)-10 EX_nh4(e)0.720224 EX_o2(e)-7.38727 DM_phb[c]10 Biomass1.06729 EX_ac(e)9.52367 EX_arg-L(e)-20 EX_for(e)0.444941 EX_glc(e)-10 EX_nh4(e)39.6441 EX_o2(e)-20 DM_phb[c]10 DM_phb[c] ≥ 10

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -70- EX_co2(e)16.6918 EX_glc(e)-10 EX_h(e)13.1856 EX_h2o(e)36.6126 EX_nh4(e)-13.198 EX_o2(e)-19.0627 EX_so4(e)-0.0247617 DM_flys3[c]0.0123808 Flux Through Exchange 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)0 EX_lys-L(e)-0.00154793 EX_met-L(e)-0.00188018 EX_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, 2015 Constraint-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 = 0 EX_pro-L(e) Lower Bound = 0 EX_ser-L(e) Lower Bound = 0 Biomass0.716261 EX_glc(e)-10 EX_nh4(e)-11.3255 EX_o2(e)-17.9371 DM_flys3[c]0.00336705 Biomass0.871302 EX_ac(e)0.716336 EX_ala-L(e)-1 EX_for(e)1.61531 EX_glc(e)-10 EX_nh4(e)-10.0001 EX_o2(e)-20 EX_pro-L(e)-1 EX_ser-L(e)-1 DM_flys3[c]0.00336705 Biomass1.3575 EX_ac(e)42.9806 EX_ala-L(e)-20 EX_for(e)33.8487 EX_glc(e)-10 EX_nh4(e)26.4335 EX_o2(e)-20 EX_pro-L(e)-4.6849 EX_ser-L(e)-20 DM_flys3[c]0.00336705 DM_flys3 [c] ≥ 0.0034 EX_ala-L(e) Lower Bound = -1 EX_pro-L(e) Lower Bound = -1 EX_ser-L(e) Lower Bound = -1 EX_ala-L(e) Lower Bound = -20 EX_pro-L(e) Lower Bound = -20 EX_ser-L(e) Lower Bound = -20

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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 ChemicalConcentration K12 Medium:KH 2 PO 4 2 g/L K 2 HPO 4.3H 2 O4 g/L (NH 4 ) 2 HPO 4 5 g/L Yeast Extract5 g/L Glucose25 g/L MgSO 4.7H 2 O0.5 g/L Thiamine2.5 mg/L K12 trace metal5 ml/L K12 trace metal solution:NaCl5 g/L ZnSO 4.7H 2 O1 g/L MnCl 2.4H 2 O4 g/L FeCl 3.6H 2 O4.75 g/L CuSO 4.5H 2 O0.4 g/L H 3 BO 3 0.575 g/L NaMoO 4.2H 2 O0.5 g/L 6N H 2 SO412.5 ml/L

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -73- Calculated Metabolite Uptake Rates for K-12 Media MetaboliteMW (g/mol)g/L in mediammol/L in media Lower bound (mmol gDW -1 h -1 ) Glucose180.1625138.7655417-11 Ammonium18.038511.36582948475.71742258-6.002150375 Phosphate94.97146.65573626470.08147994-5.555386948 Potassium39.09831.94509930949.74894838-3.943619038 Sulfate96.070.2033235292.11641021-0.167768684 Chloride35.4530.0620503641.750214773-0.138740225 Copper63.5460.0005090090.008010093-0.000634963 Iron (III)55.8450.004906840.087865335-0.00696512 Magnesium24.3050.0493042032.028562155-0.160804933 Manganese54.9380440.0055519560.101058487-0.008010947 Molybdate95.950.0018260520.019031286-0.001508618 Sodium22.989769280.0297755441.295164976-0.102668246 Thiamine265.350.00250.009421519-0.000746848 Zinc65.380.0011368470.017388304-0.001378378 Alanine89.090.2252.525535975-0.200200247 Arginine174.20.1450.832376579-0.065982824 Asparagine132.120.161.211020285-0.095998062 MetaboliteMW (g/mol)g/L in mediammol/L in media Lower bound (mmol gDW -1 h -1 ) Aspartic acid133.10.161.202103681-0.09529124 Cysteine121.160.020.165070981-0.013085243 Glutamine146.140.3452.360749966-0.187137594 Glutamic Acid147.130.3452.344865085-0.185878393 Glycine75.070.1351.798321567-0.14255367 Histidine155.150.060.386722527-0.030655649 Isoleucine131.170.1451.105435694-0.08762833 Leucine131.170.211.600975833-0.126909995 Lysine146.190.221.504890895-0.119293303 Methionine149.210.0450.301588365-0.02390703 Phenylalanine165.190.120.726436225-0.05758489 Proline115.130.1150.998870842-0.079180891 Serine105.090.131.237034922-0.098060253 Threonine119.120.1351.133310947-0.089838012 Tyrosine181.190.090.496716154-0.039374888 Valine117.150.1651.408450704-0.111648451 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, 2015 Constraint-based Metabolic Reconstructions & Analysis -74- Impact of Limiting Amino Acids Uptake for Ethanol Production % GlucoseEthanol_AminoAcid_ReducedCosts_iaf1260e.m clear; changeCobraSolver('glpk','all'); % Set constraints model=readCbModel('ecoli_iaf1260'); model = changeRxnBounds(model, {'EX_o2(e)', 'EX_glc(e)'}, [-0 -20], 'l'); % model = changeObjective(model,'Ec_biomass_iAF1260_core_59p81M'); model = changeObjective(model,'EX_etoh(e)'); % Set 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)) % GlucoseEthanol_AminoAcid_ReducedCosts_iaf1260e.m clear; changeCobraSolver('glpk','all'); % Set constraints model=readCbModel('ecoli_iaf1260'); model = changeRxnBounds(model, {'EX_o2(e)', 'EX_glc(e)'}, [-0 -20], 'l'); % model = changeObjective(model,'Ec_biomass_iAF1260_core_59p81M'); model = changeObjective(model,'EX_etoh(e)'); % Set 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, 2015 Constraint-based Metabolic Reconstructions & Analysis -75- Amino Acid Flux Values & Reduced Costs for Ethanol Production in M9 Minimal Media (GlucoseEthonal_ReducedCosts_iJO1366) Flux Through Exchange Reactions EX_ala-L(e)-1 EX_arg-L(e)-1.83333 EX_asn-L(e)-1 EX_asp-L(e)-1 EX_cys-L(e)-0.833333 EX_gln-L(e)-1.5 EX_glu-L(e)-1.5 EX_gly(e)-0.5 EX_his-L(e)0 EX_ile-L(e)0 EX_leu-L(e)0 EX_lys-L(e)0 EX_met-L(e)0 EX_phe-L(e)0 EX_pro-L(e)-0.5 EX_ser-L(e)-0.833333 EX_thr-L(e)-1.33333 EX_trp-L(e)-0.833333 EX_tyr-L(e)0 EX_val-L(e)0 Amino Acid Reduced Costs Objective Function = EX_etoh(e) Biomass0.246761 EX_ca2(e)-0.00128439 EX_cl(e)-0.00128439 EX_co2(e)6.90492 EX_cobalt2(e)-6.16902e-06 EX_cu2(e)-0.000174953 EX_etoh(e)6.41879 EX_fe2(e)-0.00203652 EX_fe3(e)-0.00192671 EX_glc(e)-20 EX_glyclt(e)0.000165083 EX_h(e)32.3664 EX_h2o(e)7.08385 EX_k(e)-0.048166 EX_lac-D(e)29.9334 EX_meoh(e)4.93522e-07 EX_mg2(e)-0.00214065 EX_mn2(e)-0.000170512 EX_mobd(e)-3.18322e-05 EX_nh4(e)-2.66522 EX_ni2(e)-7.97038e-05 EX_pi(e)-0.238033 EX_so4(e)-0.0622356 EX_succ(e)0.0817924 EX_zn2(e)-8.41455e-05

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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)0.119293 EX_ala_L(e)-0.2002 EX_arg_L(e)-0.0659828 EX_asn_L(e)-0.0959981 EX_asp_L(e)-0.0952912 EX_co2(e)42.0184 EX_cys_L(e)-0.0130852 EX_etoh(e)41.3615 EX_fe2(e)0.00696512 EX_fe3(e)-0.00696512 EX_glc(e)-20 EX_gln_L(e)-0.187138 EX_glu_L(e)-0.185878 EX_gly(e)-0.142554 EX_h2o(e)-1.77129 EX_h2s(e)0.0130852 EX_h(e)-1.98611 EX_lys_L(e)-0.119293 EX_nh4(e)1.65859 EX_pro_L(e)-0.00348256 EX_ser_L(e)-0.0980603 EX_thr_L(e)-0.089838 Flux Through Exchange Reactions EX_ala_L(e)-1 EX_arg_L(e)-1.83333 EX_asn_L(e)-1 EX_asp_L(e)-1 EX_cys_L(e)-0.833333 EX_gln_L(e)-1.5 EX_glu_L(e)-1.5 EX_gly(e)-0.5 EX_his_L(e)0 EX_ile_L(e)0 EX_leu_L(e)0 EX_lys_L(e)0 EX_met_L(e)0 EX_phe_L(e)0 EX_pro_L(e)0 EX_ser_L(e)-0.833333 EX_thr_L(e)-1.33333 EX_trp_L(e)-0.833333 EX_tyr_L(e)0 EX_val_L(e)0 Amino Acid Reduced Costs Objective Function = EX_etoh(e)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -77- Biliverdin Production (Biliverdin_AminoAcid_ReducedCosts_iJO1366) EX_co2(e)14.7976 EX_glc(e)-10 EX_h(e)7.97689 EX_h2o(e)45.3757 EX_nh4(e)-5.31792 EX_o2(e)-12.1387 EX_biliverdin(e)1.32948 EX_co(e)1.32948 Flux Through Exchange Reactions EX_ala-L(e)-0.0646425 EX_arg-L(e)-0.139079 EX_asn-L(e)-0.0705191 EX_asp-L(e)-0.0705191 EX_cys-L(e)-0.0528893 EX_gln-L(e)-0.111655 EX_glu-L(e)-0.110676 EX_gly(e)-0.0381978 EX_his-L(e)0 EX_ile-L(e)0 EX_leu-L(e)0 EX_lys-L(e)0 EX_met-L(e)0 EX_phe-L(e)0 EX_pro-L(e)-0.109696 EX_ser-L(e)-0.0558276 EX_thr-L(e)-0.0950049 EX_trp-L(e)-0.0568071 EX_tyr-L(e)0 EX_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, 2015 Constraint-based Metabolic Reconstructions & Analysis -78- Biliverdin Production (Biliverdin_AminoAcid_ReducedCosts_iJO1366) EX_15dap(e)0.119293 EX_ala-L(e)-0.2002 EX_arg-L(e)-0.0659828 EX_asn-L(e)-0.0959981 EX_asp-L(e)-0.0952912 EX_co2(e)16.3765 EX_cys-L(e)-0.0130852 EX_glc(e)-10 EX_gln-L(e)-0.187138 EX_glu-L(e)-0.185878 EX_gly(e)-0.142554 EX_h(e)6.533 EX_h2o(e)46.7738 EX_h2s(e)0.0130852 EX_lys-L(e)-0.119293 EX_nh4(e)-4.00022 EX_o2(e)-12.8919 EX_pro-L(e)-0.0791809 EX_ser-L(e)-0.0980603 EX_thr-L(e)-0.089838 EX_biliverdin(e)1.43363 EX_co(e)1.43363 Flux Through Exchange Reactions EX_ala-L(e)-0.0646425 EX_arg-L(e)-0.123408 EX_asn-L(e)-0.0705191 EX_asp-L(e)-0.0705191 EX_cys-L(e)-0.0528893 EX_gln-L(e)-0.110676 EX_glu-L(e)-0.110676 EX_gly(e)-0.036239 EX_his-L(e)0 EX_ile-L(e)0 EX_leu-L(e)0 EX_lys-L(e)0 EX_met-L(e)0 EX_phe-L(e)0 EX_pro-L(e)-0.109696 EX_ser-L(e)-0.0558276 EX_thr-L(e)-0.0920666 EX_trp-L(e)-0.0568071 EX_tyr-L(e)0 EX_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, 2015 Constraint-based Metabolic Reconstructions & Analysis -79- EX_co2(e)10.3513 EX_glc(e)-10 EX_h2o(e)22.7635 EX_o2(e)-4.14522 DM_phb[c]12.4122 Flux Through Exchange Reactions EX_ala-L(e)-0.608696 EX_arg-L(e)-1.14783 EX_asn-L(e)-0.652174 EX_asp-L(e)-0.652174 EX_cys-L(e)-0.504348 EX_gln-L(e)-1.0087 EX_glu-L(e)-1 EX_gly(e)-0.347826 EX_his-L(e)0 EX_ile-L(e)0 EX_leu-L(e)0 EX_lys-L(e)0 EX_met-L(e)0 EX_phe-L(e)0 EX_pro-L(e)-0.991304 EX_ser-L(e)-0.530435 EX_thr-L(e)-0.869565 EX_trp-L(e)-0.53913 EX_tyr-L(e)0 EX_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, 2015 Constraint-based Metabolic Reconstructions & Analysis -80- EX_15dap(e)0.119293 EX_ala-L(e)-0.2002 EX_arg-L(e)-0.0659828 EX_asn-L(e)-0.0959981 EX_asp-L(e)-0.0952912 EX_co2(e)11.6396 EX_cys-L(e)-0.0130852 EX_glc(e)-10 EX_gln-L(e)-0.187138 EX_glu-L(e)-0.185878 EX_gly(e)-0.142554 EX_h(e)-2.06877 EX_h2o(e)22.4335 EX_h2s(e)0.0130852 EX_lys-L(e)-0.119293 EX_nh4(e)1.73429 EX_o2(e)-4.33723 EX_pro-L(e)-0.0791809 EX_ser-L(e)-0.0980603 EX_thr-L(e)-0.089838 DM_phb[c]13.3701 Flux Through Exchange Reactions EX_ala-L(e)-0.608696 EX_arg-L(e)-1.11304 EX_asn-L(e)-0.652174 EX_asp-L(e)-0.652174 EX_cys-L(e)-0.504348 EX_gln-L(e)-1 EX_glu-L(e)-1 EX_gly(e)-0.347826 EX_his-L(e)0 EX_ile-L(e)0 EX_leu-L(e)0 EX_lys-L(e)0 EX_met-L(e)0 EX_phe-L(e)0 EX_pro-L(e)-0.991304 EX_ser-L(e)-0.530435 EX_thr-L(e)-0.869565 EX_trp-L(e)-0.53913 EX_tyr-L(e)0 EX_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, 2015 Constraint-based Metabolic Reconstructions & Analysis -81- EX_co2(e)16.6918 EX_glc(e)-10 EX_h(e)13.1856 EX_h2o(e)36.6126 EX_nh4(e)-13.198 EX_o2(e)-19.0627 EX_so4(e)-0.0247617 DM_flys3[c]0.0123808 Flux Through Exchange 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)0 EX_lys-L(e)-0.00154793 EX_met-L(e)-0.00188018 EX_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, 2015 Constraint-based Metabolic Reconstructions & Analysis -82- EX_15dap(e)0.111878 EX_ac(e)6.38362 EX_ala-L(e)-0.2002 EX_arg-L(e)-0.0659828 EX_asn-L(e)-0.0959981 EX_asp-L(e)-0.0952912 EX_co2(e)11.183 EX_cys-L(e)-0.0130852 EX_for(e)15.9244 EX_glc(e)-10 EX_gln-L(e)-0.187138 EX_glu-L(e)-0.185878 EX_gly(e)-0.142554 EX_h(e)28.0127 EX_h2o(e)20.113 EX_h2s(e)0.0130852 EX_his-L(e)-0.0306556 EX_ile-L(e)-0.00741544 EX_lys-L(e)-0.119293 EX_met-L(e)-0.0148309 EX_nh4(e)-6.00215 EX_o2(e)-20 EX_pro-L(e)-0.0791809 EX_ser-L(e)-0.0980603 EX_thr-L(e)-0.089838 EX_tyr-L(e)-0.0393749 DM_flys3[c]0.00741544 Flux Through Exchange 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)0 EX_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 Costs Spider Silk Production (Spider_Silk_AminoAcid_ReducedCosts_iJO1366) Objective Function = DM_flys3[c]

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -83- Impact of Uptaking Minerals % GlucoseEthanol_AminoAcid_ReducedCosts_iaf1260e.m clear; changeCobraSolver('glpk','all'); % Set constraints model=readCbModel('ecoli_iaf1260'); model = changeRxnBounds(model, {'EX_o2(e)', 'EX_glc(e)'}, [-0 -20], 'l'); % model = changeObjective(model,'Ec_biomass_iAF1260_core_59p81M'); model = changeObjective(model,'EX_etoh(e)'); % Set 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)) % GlucoseEthanol_AminoAcid_ReducedCosts_iaf1260e.m clear; changeCobraSolver('glpk','all'); % Set constraints model=readCbModel('ecoli_iaf1260'); model = changeRxnBounds(model, {'EX_o2(e)', 'EX_glc(e)'}, [-0 -20], 'l'); % model = changeObjective(model,'Ec_biomass_iAF1260_core_59p81M'); model = changeObjective(model,'EX_etoh(e)'); % Set 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, 2015 Constraint-based Metabolic Reconstructions & Analysis -84- Mineral Flux Values & Reduced Costsfor Ethanol Production in Calculated K-12 Media (GlucoseEthonal_AminoAcid_ReducedCosts_iaf1260) EX_15dap(e)0.119293 EX_ala_L(e)-0.2002 EX_arg_L(e)-0.0659828 EX_asn_L(e)-0.0959981 EX_asp_L(e)-0.0952912 EX_co2(e)42.0184 EX_cys_L(e)-0.0130852 EX_etoh(e)41.3615 EX_fe2(e)0.00696512 EX_fe3(e)-0.00696512 EX_glc(e)-20 EX_gln_L(e)-0.187138 EX_glu_L(e)-0.185878 EX_gly(e)-0.142554 EX_h2o(e)-1.77129 EX_h2s(e)0.0130852 EX_h(e)-1.98611 EX_lys_L(e)-0.119293 EX_nh4(e)1.65859 EX_pro_L(e)-0.00348256 EX_ser_L(e)-0.0980603 EX_thr_L(e)-0.089838 Flux Through Exchange Reactions EX_ca2(e)0 EX_cobalt2(e)0 EX_ni2(e)0 EX_nh4(e)0 EX_pi(e)0 EX_k(e)0 EX_so4(e)0 EX_cl(e)0 EX_cu2(e)0 EX_fe3(e)-0.833333 EX_mg2(e)0 EX_mn2(e)0 EX_mobd(e)0 EX_na1(e)0 EX_thm(e)0 EX_zn2(e)0 Mineral Reduced Costs

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -85- Ethanol Production with Limited Nutrients: Robustness Analysis (GlucoseEthanol_Limited_Media_Mutant_iJO1366.m) Unconstrained MediaConstrained Media

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

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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.m PHB_Robustness_iJO1366.m Desired Operating Point Unconstrained Media Constrained Media

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -88- Spider Silk Production Limitations Uptake 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 Media Constrained Media

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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 Plasmid Bearing The plasmid equation consists of the biosynthetic precursors and energetic requirement for pOri2 plasmid DNA replication and marker protein expression. The pOri2 (4,575 bp) requires four nucleotide precursors for its replication based on an approximated E. coli K-12 GC content of 51%: 2,315.0 dGTP + 2,315.0 dCTP + 2,260.1 dATP + 2,260.1 dTTP -> plasmid The biosynthetic precursor balance for the sole plasmid encoded antibiotic marker protein (β-lactamase) combined with energetic requirements of 4.306 mol ATP/mol amino acids for its expression can be formulated as follows: 28 Ala + 3 Cys + 16 Asp + 20 Glu + 9 Phe + 21 Gly + 7His + 17 Ile + 11 Lys + 33 Leu + 10 Met + 8Asn + 14 Pro + 9Gln + 19 Arg + 17 Ser + 20 Thr + 16 Val + 4 Trp + 4 Tyr + 1,231.52 ATP -> b-lactamase + 1,231.52 ADP + 1,231.52 PI These equations are incorporated to formulate the plasmid production rate on the basis of the experimentally determined production rates of plasmid DNA and β-lactamase protein, which is estimated as 3% of total cellular protein from two-dimensional gel electrophoresis analysis. Ow, D. S., D. Y. Lee, et al. (2009). "Identification of cellular objective for elucidating the physiological state of plasmid-bearing Escherichia coli using genome-scale in silico analysis." Biotechnology progress 25(1): 61-67.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -91- Review Questions 1.In addition to the designated carbon source in a growth media what other components of the media can act as carbon sources? 2.What components in a growth media can impact the maximum bioproduct production? 3.How is the M9 minimal media modeled? 4.In the standard E.coli models (textbook, iAF1260, and iJO1366) what are the typical default lower bounds for most amino acids and minerals? What are the default upper bounds? 5.What relationship do the amino acids have with the biomass function? 6.How can amino acid and mineral uptake rates impact growth rate and bioproduct production? 7.How can reduced costs be used to understand the impact of an amino acid or mineral on bioproduct production? 8.Are regulatory constraints included in the standard E.coli constraint-based models? 9.What impacts can be observed by adding a plasmid to a host cell? 10.How can plasmids be modeled?

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

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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, 2015 Constraint-based Metabolic Reconstructions & Analysis -96- Flux Differences: Wild Type, GDLS, & MOMA ReactionWTGDLSMOMAReactionWTGDLSMOMAReactionWTGDLSMOMA ACALD2.27E-13-7.38652-5.38586EX_lac_D(e)002.39504NADTRHD01.11720 ACONTa3.443461.873570.010514EX_nh4(e)-2.26617-0.27522-0.05314NH4t2.266170.2752210.05314 ACONTb3.443461.873570.010514EX_o2(e)-11.8336-0.909560O2t11.83360.9095570 AKGDH2.995071.819110EX_pi(e)-1.52886-0.18568-0.03585PDH5.0010305.36461 ALCD2x0-7.38652-5.38586FBA3.898144.922543.95219PFK3.898144.922543.95219 ATPM8.39 FORti09.449250.068288PFL09.449250.068288 ATPS4r24.87120.0943321.27156FRD7001.49752PGI2.84944.90793.94936 Biomass0.4155980.0504730.009745FUM2.995071.819110PGK-8.20675-9.83858-7.90311 CO2t-12.314-3.6298-5.36298G6PDH2r2.065410.0817520.015785PGL2.065410.0817520.015785 CS3.443461.873570.010514GAPD8.206759.838587.90311PGM-7.58501-9.76307-7.88854 CYTBD23.66711.819110GLCpts553.96714PIt2r1.528860.1856760.035851 D_LACt200-2.39504GLNS0.1062680.0129060.002492PPC1.190940.1446360.163454 ENO7.585019.763077.88854GLUDy-2.1599-0.26232-0.05065PYK1.178344.592233.75288 ETOHt2r0-7.38652-5.38586GND2.065410.0817520.015785RPE1.078210.0182210.003518 EX_co2(e)12.3143.62985.36298H2Ot-15.34143.38905-0.04811RPI-0.9872-0.06353-0.01227 EX_etoh(e)07.386525.38586ICDHyr3.443461.873570.010514SUCDi2.995071.819111.49752 EX_for(e)09.449250.068288LDH_D00-2.39504SUCOAS-2.99507-1.819110 EX_glc(e)-5 -3.96714MDH2.995071.81911-0.13553TALA0.6141180.0182210.003518 EX_h2o(e)15.3414-3.389050.048112ME2000.135527TKT10.6141180.0182210.003518 EX_h(e)8.3368910.46172.65882NADH1620.67200TKT20.46408800 TPI3.898144.922543.95219 EthanolProduction_GDLS_Mutants_MOMA.m

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -97- Flux Maps: Wild Type, GDLS, & MOMA EthanolProduction_GDLS_Mutant.mEthanolProduction_WT.m EthanolProduction_GDLS_Mutants_MOMA.m Wild TypeGDLS-MutantMOMA Result X X X X X X Loop q8, q8h2 nadh for TCA Lactate

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -98- Production Envelope – Ethanol Production Mutant (0.050473, 7.3865) (0.009745, 5.38586) MOMA Operating Point FBA Operating Point

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-based Metabolic Reconstructions & Analysis -101- Desired Cell Evolution (0.050473, 7.3865) (0.009745, 5.38586)

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -102- Adaptive Laboratory Evolution B. Palsson (2010). “Adaptive Laboratory Evolution.” Microbe, 6(2):69-74

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-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, 2015 Constraint-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 networks Conrad, T. M., N. E. Lewis, et al. (2011). "Microbial laboratory evolution in the era of genome-scale science." Molecular Systems Biology 7: 509.

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

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -107- 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

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -108- Growth of E.coli K-12 on Glucose Ibarra, R. U., J. S. Edwards, et al. (2002). "Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth." Nature 420(6912): 186-189.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -109- Growth of E.coli K-12 on Glycerol (Ibarra, 2002) 30°C 37°C

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -110- Review Questions 1.What don’t the first generation of transformed cells normally achieve the optimal performance predicted in the FBA models? 2.How can cells evolve after 100’s of generations to operate on the line of optimality? 3.What is the relationship between MOMA and adaptive laboratory evolution? 4.What are the number of generations required for adaptive laboratory evolution? Ibarra, R. U., J. S. Edwards, et al. (2002). "Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth." Nature 420(6912): 186-189.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -112- Evolution of Bioproduction Tools 200020012002200320042005200620072008200920102011201220132014 MOMA OptKnock OptStrain OptGene ROOM OptReg GDLS OptOrf OptForce EMILiO SimOptStrain Fiest Available in Cobra ToolboxGAMS OptComK-OptForce Matlab CPLEX ILOG Solver

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -113- OptStrain OptStrain uses a multi-step process to first identify non-native reactions that would improve the host organism’s maximum production capabilities. Reaction deletions can then be found which couple production and growth in the modified host metabolic network. 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.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -114- OptReg OptReg is an extension of OptKnock (Burgard et al., 2003) to allow for up- and/or downregulation in addition to gene knock-outs to meet a bioproduction goal. The objective is to computationally identify which reactions should be modulated, (i.e., repressed or activated) or knocked-out such that the biochemical of interest is overproduced. Gene up- or downregulation can be tuned by using widely available promoter libraries In many cases, a gene deletion is lethal whereas its downregulation is not. 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.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -115- 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

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -116- OptForce OptForce is a process that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. By superimposing the flux ranges for a given reaction in the wild-type vs. the overproducing network a number of possible outcomes are revealed. If there is any degree of overlap between the two reaction flux ranges then it may be possible to achieve the overproduction target without changing the value of the corresponding reaction flux in the wild-type strain. In contrast, if the flux ranges for a reaction in the wild-type metabolic network are completely to the left or to the right of the corresponding ranges for the overproducing metabolic network then the overproduction target cannot be achieved unless the reaction flux is directly or indirectly changed. Note that if the reaction flux range collapses to zero then the corresponding reaction needs to be eliminated (e.g., through a gene knock-out). 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

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -117- SimOptStrain SimOptStrain simultaneously considers gene deletions in a host organism and reaction additions from a universal database such as KEGG or MetaCyc. Previously, the OptStrain framework used a multi-step procedure to first identify a minimal set of non-native reactions to add to the metabolic network to achieve the theoretical maximum production (TMP) of a biochemical target (Step 1), and then identify deletion strategies in the expanded metabolic network using OptKnock (Step 2). The current multi-step OptStrain procedure may miss higher production strategies by not evaluating additions and deletions simultaneously. Kim, J., J. L. Reed, et al. (2011). "Large-scale bi-level strain design approaches and mixed-integer programming solution techniques." PLoS One 6(9): e24162.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -118- 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.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -119- 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 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.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -120- 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.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -121- 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()

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -122- 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.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -123- 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.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -124- The Strain Designs Generated for Five Different Targets from Glucose and Xylose Anaerobically glucosexylose 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 H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -125- OptCom OptCom, a comprehensive flux balance analysis framework for microbial communities. OptCom is general enough to capture any type of interactions (positive, negative or combinations thereof) and is capable of accommodating any number of microbial species (or guilds) involved. OptCom demonstrates the importance of 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 H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -126- 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.

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -127- 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 H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -129- 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 Constraint-based Metabolic Reconstructions & Analysis -1- Bioproduct.

Similar presentations

Presentation on theme: "Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -1- Bioproduct."— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -1- Bioproduct.

Similar presentations

Presentation on theme: "Lesson: Bioproduct ProductionBIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -1- Bioproduct."— Presentation transcript:

Similar presentations

About project

Feedback