Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -1- Dynamic Flux Balance.

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -1- Dynamic Flux Balance Analysis & Dynamic Regulatory Flux Balance Analysis

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -2- LEARNING OBJECTIVES Explain dynamic flux balance analysis. Describe the strengths and limitations of dynamic flux balance analysis. Explain dynamic regulatory flux balance analysis. Explain Boolean transcriptional regulation. Describe the difference between the regular constraint-based FBA models and the regulatory FBA model. Describe the strengths and limitations of regulatory dynamic flux balance analysis. Each student should be able to:

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -3- Lesson Outline Dynamic Flux Balance Analysis (dynamicFBA) Basic Operation Minimal Media Examples Limiting Media Examples Regulatory Flux Balance Analysis (dynamicRFBA) Regulated E.coli Core Model Regulatory Genes Catabolite Repression Examples Growth on Non-glucose Carbon Sources Other Regulatory-based Model Approaches

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -4- Dynamic Flux Balance Analysis FBA can be used to examine dynamic processes such as microbial growth in batch cultures by combining FBA with an iterative approach based on a quasi-steady-state assumption (static optimization–based dynamic FBA). At each time step, FBA is used to predict growth, nutrient uptake and by-product secretion rates. These rates are then used to calculate biomass and nutrient concentrations in the culture at the end of the time step. The concentrations can, in turn, be used to calculate maximum uptake rates of nutrients for the next time step. Using this iterative procedure, dynamic FBA has allowed the simulation of batch experiments. This function will perform dynamic FBA to predict the outcomes of growth in batch culture conditions. Becker, S. A., A. M. Feist, et al. (2007). "Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox." Nature protocols 2(3): 727-738.

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -5- Dynamic Flux Balance Analysis The substrate concentration (S c ) (mmol/L) is determined from the substrate concentration predicted for the previous step (S co ) or from the initial substrate concentration if it is the first time step: S c = S co The substrate concentration is scaled to define the amount of substrate available per unit of biomass per unit of time (mmol gDW -1 h -1 ): where X is the current cell density and X o is the cell density from the previous step. FBA is then used to calculate the substrate uptake ( S u ) and the growth rate (µ). Concentrations for the next time step are calculated from the standard differential equations: The output of dynamic FBA is two graphs: one showing the flux through the objective reaction over time, and one showing the flux through the exchange reactions for the selected metabolites over time.

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -6- Dynamic Flux Balance Analysis The dynamic flux balance analysis function is called as: dynamicFBA(model, substrateRxns, initConcentrations, initBiomass, tStep, nSteps,plotRxns) The list of exchange reactions corresponding to the substrates that are initially in the media (e.g., glucose, ammonia, phosphate) is described in substrateRxns. The initConcentrations variable sets the initial concentrations of substrates in the substrateRxns vector. The initBiomass variable is needed to specify the initial amount of biomass in the simulation. The tStep variable sets the time step size interval (h) and the nSteps variable designates the maximum number of time steps for the analysis. The plotRxns variable is optional and contains the names of the exchange reactions for the metabolites whose time- dependent concentrations should be plotted graphically. Varma, A. and B. O. Palsson (1994). "Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110." Applied and Environmental Microbiology 60(10): 3724-3731.

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -8- Aerobic, Glucose Substrate DynamicFBA Growth % DynamicGrowth_Aerobic_JO1366.m clear; model=readCbModel('ecoli_iJO1366'); model = changeRxnBounds(model, {'EX_glc(e)','EX_o2(e)'},[-10 -30], 'l'); model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M'); % Set-up variables for dynamicFBA substrateRxns = {'EX_glc(e)'}; initConcentrations = [10]; initBiomass =.01; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_acald(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)','EX_lac-L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec] =... dynamicFBA(model,substrateRxns,initConcentrations, initBiomass, timeStep, nSteps, plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -9- Low Aerobic, Glucose Substrate DynamicFBA Growth % DynamicGrowth_Aerobic_JO1366.m clear; model=readCbModel('ecoli_iJO1366'); model = changeRxnBounds(model, {'EX_glc(e)','EX_o2(e)'},[-10 -5], 'l'); model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M'); % Set-up variables for dynamicFBA substrateRxns = {'EX_glc(e)'}; initConcentrations = [10]; initBiomass =.01; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_acald(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)','EX_lac-L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec] =... dynamicFBA(model,substrateRxns,initConcentrations, initBiomass, timeStep, nSteps, plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -10- Substrate Maximum Growth Rate SubstrateAerobic (hr -1 )Anaerobic (hr -1 ) acetate0.38930 acetaldehyde0.60730 2-oxoglutarate1.09820 ethanol0.69960 D-fructose1.79060.5163 fumarate0.78650 D-glucose1.79060.5163 L-glutamine1.16360 L-glutamate1.24250 D-lactate0.74030 L-malate0.78650 pyruvate0.62210.0655 succinate0.84010 ("What is flux balance analysis? - Supplementary tutorial“) The core E. coli model contains exchange reactions for 13 different organic compounds, each of which can be used as the sole carbon source under aerobic or anaerobic conditions.

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -11- Anaerobic, Glucose Substrate DynamicFBA Growth % DynamicGrowth_Aerobic_JO1366.m clear; model=readCbModel('ecoli_iJO1366'); model = changeRxnBounds(model, {'EX_glc(e)','EX_o2(e)'},[-10 0], 'l'); model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M'); % Set-up variables for dynamicFBA substrateRxns = {'EX_glc(e)'}; initConcentrations = [10]; initBiomass =.01; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_acald(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)','EX_lac-L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec] =... dynamicFBA(model,substrateRxns,initConcentrations, initBiomass, timeStep, nSteps, plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -12- DynamicFBA: Ethanol Production with Glucose Substrate % DynamicEthanolProduction_JO1366.m clear; model=readCbModel('ecoli_iJO1366'); model = changeRxnBounds(model, {'EX_glc(e)','EX_o2(e)'},[-10 -0], 'l'); model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M'); % Knockouts model = changeRxnBounds(model, {'PFL','PPC','PPKr'},[-0 -0 -0], 'b'); % Set-up variables for dynamicFBA substrateRxns = {'EX_glc(e)'}; initConcentrations = [20]; initBiomass =.01; timeStep =.25; nSteps = 125; plotRxns = {'EX_ac(e)','EX_acald(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)','EX_lac-L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec] =... dynamicFBA(model,substrateRxns,initConcentrations, initBiomass, timeStep, nSteps, plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -14- % Dynamic_Growth_K12media.m clear; % Read model model = readCbModel('ecoli_iJO1366'); model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M'); %Setting carbon source and oxygen model = changeRxnBounds(model,'EX_glc(e)',-10,'l'); model = changeRxnBounds(model,'EX_o2(e)',-30,'l'); % Set uptake values for amino acids & Minerals;... % Set-up variables for dynamicFBA: % NOTE- substrate rxns and plot rxns need to be in the order that they appear in the model initBiomass =.01; timeStep = 0.5; nSteps = 100; substrateRxns = {'EX_ala-L(e)','EX_arg-L(e)','EX_asn-L(e)','EX_asp-L(e)','EX_cl(e)','EX_cu2(e)','EX_cys-L(e)','EX_fe3(e)','EX_glc(e)','EX_gln-L(e)', 'EX_glu-L(e)','EX_gly(e)','EX_his-L(e)','EX_ile-L(e)','EX_k(e)','EX_leu-L(e)','EX_lys-L(e)','EX_met-L(e)','EX_mg2(e)','EX_mn2(e)','EX_mobd(e)','EX_na1(e)','EX_nh4(e)', 'EX_phe-L(e)','EX_pi(e)','EX_pro-L(e)','EX_ser-L(e)','EX_so4(e)','EX_thm(e)','EX_thr-L(e)','EX_tyr-L(e)','EX_val-L(e)','EX_zn2(e)'}; initConcentrations = [2.525535975,0.832376579,1.211020285,1.202103681,1.750214773,0.008010093,0.165070981,0.087865335,138.7655417,2.360749966,2.344865085, 1.798321567, 0.386722527,1.105435694,49.74894838,1.600975833,1.504890895,0.301588365,2.028562155,0.101058487,0.019031286,1.295164976,75.71742258, 0.726436225,70.08147994, 0.998870842,1.237034922,2.11641021,0.009421519,1.133310947,0.496716154,1.408450704,0.017388304]; plotRxns = {'EX_ac(e)','EX_acald(e)','EX_ala-L(e)','EX_arg-L(e)','EX_asn-L(e)','EX_asp-L(e)','EX_cl(e)','EX_cu2(e)','EX_cys-L(e)','EX_etoh(e)','EX_fe3(e)','EX_for(e)', 'EX_glc(e)','EX_gln-L(e)','EX_glu-L(e)','EX_gly(e)','EX_his-L(e)','EX_ile-L(e)','EX_k(e)','EX_lac-L(e)','EX_leu-L(e)','EX_lys-L(e)','EX_met-L(e)','EX_mg2(e)', 'EX_mn2(e)','EX_mobd(e)','EX_na1(e)','EX_nh4(e)','EX_phe-L(e)','EX_pi(e)','EX_pro-L(e)','EX_ser-L(e)','EX_so4(e)','EX_succ(e)','EX_thm(e)','EX_thr-L(e)', 'EX_tyr-L(e)','EX_val-L(e)','EX_zn2(e)'}; dynamicFBA(model,substrateRxns,initConcentrations, initBiomass, timeStep, nSteps, plotRxns); %labeling subplot(1,2,1); title('Growth rate'); xlabel('Time steps'); ylabel('g biomass/gDW*h'); subplot(1,2,2); title('Substrate Concentrations'); xlabel('Time steps'); ylabel('Concentrations (mmol/L)'); DynamicFBA: Growth on Glucose with limiting K12 Media

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -15- Glucose Phosphate Ammonium Potassium Dynamic_Growth_K12media.m Low Oxygen: EX_o2(e) = -30

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -16- Matlab Property Editor Under View Menu

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -17- Glucose Phosphate Ammonium Potassium Dynamic_Growth_K12media.m Formate Acetate Low Oxygen: EX_o2(e) = -5

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -18- Aerobic Biliverdin Production % DynamicBiliverdinProduction_JO1366.m clear; model=readCbModel('ecoli_iJO1366'); % Add heme oxygenase enzyme... model = changeRxnBounds(model, {'EX_glc(e)','EX_o2(e)'},[-10 -20], 'l'); model = changeRxnBounds(model,'EX_biliverdin(e)',1.2,'l'); model = changeRxnBounds(model,'HEMEOX',1.2,'b'); model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M'); % Set-up for one variables for dynamicFBA substrateRxns = {'EX_glc(e)'}; initConcentrations = 20; initBiomass =.01; timeStep =.25; nSteps = 250; plotRxns = {'EX_ac(e)','EX_biliverdin(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec] =... dynamicFBA(model,substrateRxns,initConcentrations, initBiomass, timeStep, nSteps, plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -19- Low-Aerobic Biliverdin Production % DynamicBiliverdinProduction_JO1366.m clear; model=readCbModel('ecoli_iJO1366'); % Add heme oxygenase enzyme... model = changeRxnBounds(model, {'EX_glc(e)','EX_o2(e)'},[-10 -10], 'l'); model = changeRxnBounds(model,'EX_biliverdin(e)',1.2,'l'); model = changeRxnBounds(model,'HEMEOX',1.2,'b'); model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M'); % Set-up for one variables for dynamicFBA substrateRxns = {'EX_glc(e)'}; initConcentrations = 20; initBiomass =.01; timeStep =.25; nSteps = 250; plotRxns = {'EX_ac(e)','EX_biliverdin(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec] =... dynamicFBA(model,substrateRxns,initConcentrations, initBiomass, timeStep, nSteps, plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -20- Aerobic PHB Production % DynamicPHBProduction_JO1366.m clear; model=readCbModel('ecoli_iJO1366'); % Add PHB Reactions... % Add demand reaction model = addDemandReaction(model,'phb[c]'); model = changeRxnBounds(model,'PHBpoly',10,'l'); % Set key parameters model = changeRxnBounds(model, {'EX_glc(e)','EX_o2(e)'},[-10 -20], 'l'); model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M'); % Set-up for one variables for dynamicFBA substrateRxns = {'EX_glc(e)'}; initConcentrations = 20; initBiomass =.01; timeStep =.25; nSteps = 400; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)','DM_phb[c]'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec] =... dynamicFBA(model,substrateRxns,initConcentrations, initBiomass, timeStep, nSteps, plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -21- Aerobic Spider Silk Production % DynamicSpiderSilkProduction_JO1366.m clear; 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]'); model = changeRxnBounds(model,'FlYS3',0.00336705,'b'); % Add demand reaction model = addDemandReaction(model,'flys3[c]'); %'DM_flys3[c]' % Set key parameters model = changeRxnBounds(model, {'EX_glc(e)','EX_o2(e)'},[-10 -20], 'l'); model = changeObjective(model,'Ec_biomass_iJO1366_core_53p95M'); % Set-up for one variables for dynamicFBA substrateRxns = {'EX_glc(e)'}; initConcentrations = 20; initBiomass =.01; timeStep =.25; nSteps = 400; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)','DM_flys3[c]'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec] =... dynamicFBA(model,substrateRxns,initConcentrations, initBiomass, timeStep, nSteps, plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -22- dynamicFBA Limitations The dynamicFBA tool cannot simulate the fed batch mode. There is no way to account for substrates that enter the medium via fed batch mode, so it can only show what becomes of the initial concentrations. The dynamicFBA was created to optimize the biomass reaction, so there is currently no way to maximize reactions for protein production, or to maximize both the growth rate and protein production at the same time. The predicted growth rate can reach values higher than possible because the calculated growth rate is constantly in the exponential phase. These aspects make the dynamicFBA tool more useful for qualitative rather than quantitative study.

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -23- Dynamic FBA Review Questions 1.Explain the basic operation of dynamic flux balance analysis. 2.What are the key inputs required for dynamicFBA operation? 3.Why aren’t the fermentation products used as carbon sources after all the glucose has been used in an anaerobic environment? 4.In an environment with a large number of plotted metabolites how can the Matlab Property Editor be useful? 5.What are the strengths of dynamic FBA? 6.What are the weaknesses of dynamic FBA?

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -25- Transcriptional Regulatory Networks In addition to the metabolic reconstruction, the core E. coli model also contains a Boolean representation of part of the associated transcriptional regulatory network. In response to external and internal stimuli, in silica transcription factors can either activate or repress genes associated with metabolic reactions. This regulation improves the predictive fidelity of the metabolic model by imposing additional context dependent constraints on certain reactions. The transcriptional regulatory reconstruction consists of a set of Boolean rules that dictate whether a gene is either fully induced or fully repressed. If the genes associated with an enzyme or transport protein/complex are repressed, then in silica flux is set to zero for the corresponding reaction. The solution space of the network shrinks when these additional constraints are imposed. Reactions that are not used due to regulatory effects are thus restricted, so when using flux balance analysis, the optimal flux distribution will be consistent with known regulation. This optimal flux distribution may be different from the flux distribution of an unregulated model. In this case, the flux distribution of the unregulated model violated at least one regulatory constraint, making it biologically unrealistic. The use of computationally implemented Boolean rules in a genome scale model has been shown to lead to more accurate flux balance analysis predictions Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010)

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -26- Transcriptional Regulatory Networks E.coli metabolic core networkE.coli core regulatory network Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010)

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -27- Boolean Regulatory Networks A gene is considered to be induced when evaluation of the corresponding Boolean rule gives 'true'. In contrast, a gene is considered to be repressed, when evaluation of the corresponding Boolean rule gives 'false'. Boolean logic is used to evaluate each Boolean rule. Complex regulatory conditions will be represented with variables that represent a complex regulatory rule for a transcription factor that cannot be accurately represented with only one variable. By using Boolean logic, all rules in a regulatory network can be reduced to either 'true' or 'false', and ultimately this dictates whether each metabolic gene is induced or repressed. Not every gene in the metabolic network is controlled by the regulatory network, so the unregulated genes are assumed to always be active, and their fluxes are never constrained to zero. Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010)

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -28- dynamicRFBA - perform dynamic rFBA simulation using the static optimization approach [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(model,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns,exclUptakeRxns) model a regulatory COBRA model substrateRxns list of exchange reaction names for substrates initially in the media that may change (i.e. not h2o or co2) initConcentrations initial concentrations of substrates (in the same structure as substrateRxns) initBiomass initial biomass timeStep time step size nSteps maximum number of time steps plotRxns reactions to be plotted exclUptakeRxns list of uptake reactions whose substrate concentrations do not change (opt, default {'EX_co2(e)','EX_o2(e)','EX_h2o(e)','EX_h(e)'}) concentrationMatrix matrix of extracellular metabolite concentrations excRxnNames names of exchange reactions for the EC metabolites timeVec vector of time points biomassVec vector of biomass values drGenes vector of downregulated genes constrainedRxns vector of downregulated reactions states vector of regulatory network states If no initial concentration is given for a substrate that has an open uptake in the model (i.e. model.lb 0, the maximum uptake rate specified in the model is used instead of the calculated uptake rate. Dynamic Regulatory Flux Balance Analysis

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -29- Growth with Glucose Substrate in Low Aerobic Environment % Glucose_Low_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-5, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_glc(e)'}; initConcentrations = [20]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fru(e)','EX_glc(e)', 'EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -30- What is a Regulated Model? A regulated model will include the following extra cells A list of the regulatory genes b4014, b4015, etc. A list of the external metabolites that are regulatory inputs (regulatoryInputs1) A list of the internal reactions that are regulatory changes (regulatoryInputs2) A list of the regulatory rules true Crp NOT ArcA NOT PdhR OR Fis CRPnoGLM AND (NOT ArcA) AND DcuR See core_regulatory_rules.xls

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -31- bNumGeneRule b4401ArcANOT o2[e] b3357CrpCRPnoGLC b4124DcuRDcuS b4125DcuSsucc[e] OR fum[e] OR mal-L[e] b1187FadRglc-D[e] OR (NOT ac[e]) b3261FisBiomass_Ecoli_core_w_GAM b1334FnrNOT o2[e] b0080FruRNOT surplusFDP b2980GlcCac[e] b3868GlnGNOT nh4[e] b4018IclRFadR b1594MlcNOT glc-D[e] b1988NacNRI_low b0113PdhRNOT surplusPYR b0399PhoBPhoR b0400PhoRNOT pi[e] CRPnoGLCNOT glc-D[e] CRPnoGLMNOT (glc-D[e] OR mal-L[e] OR lac-D[e]) NRI_hiNRI_low NRI_lowGlnG surplusFDP((NOT FBP) AND (NOT (TKT2 OR TALA OR PGI))) OR fru[e] surplusPYR(NOT (ME2 OR ME1)) AND (NOT (GLCpts OR PYK OR PFK OR LDH_D OR SUCCt2_2)) Regulatory Genes in the Regulated Core Model core_regulatory_rules.xls

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -32- Regulatory Inputs Metabolite Inputs o2[e] glu-L[e] glc-D[e] nh4[e] succ[e] fum[e] mal-L[e] ac[e] pi[e] lac-D[e] fru[e] Reaction Inputs FBP TKT2 TALA PGI ME2 ME1 GLCpts PYK PFK LDH_D SUCCt2_2 Biomass_Ecoli_core_w_GAM core_regulatory_rules.xls

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -33- Regulatory Map of the Regulated Core Model

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -35- ArcA Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) Low oxygen availability signals the activation of the global regulator ArcA. Represses the transporters for malate, fumarase, lactate, and succinate. Downregulates the glycoxylate cycle Downregulates the energy producing portion of the TCA cycle Upregulates the fermentation pathway for formate Downregulates oxidative phosphorylation NOT o2[e]-> ArcA ArcA

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -36- CRPnoGLC Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) The activity of the cAMP receptor protein, Crp is modeled when no glucose is present in the media using CRPnoGLM. Upregulates the reductive pathway in the TCA cycle (fumB) Upregulates the formate and acetate fermentation pathways Upregulates the conversion from glutamate to glutamine Downregulates the conversion from glutamine to glutamate NOT glc-D[e] -> CRPnoGLC fumB CRPnoGLC

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -37- DcuR & DcuS Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) Activated when malate, fumarate, or succinate are present in the media. DcuS and DcuR form a two component histidine kinase system. Upregulates the reductive pathway in the TCA cycle (fumB) Upregulates the transport pathways for malate, fumarate, and succinate (C 4 -dicarboxylate compounds) DcuSDcuR fumB succ[e] OR fum[e] OR mal-L[e] -> DcuS DcuS -> DcuR

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -38- FadR, IclR & GlcC Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) FadR and IclR are activated when either glucose is present in the media or acetate is not. FadR and IclR form a two component histidine kinase system. Down regulates the glycoxylate cycle. GlcC is activated when acetate is present in the media Upregulates the transport pathway for D-lactate FadR glc-D[e] OR ( NOT ac[e] ) -> FadR FadR -> IclR IclRGlcC ac[e] -> GlcC glcB aceB glcA

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -39- Fis Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) Fis is activated when the cell is in exponential growth phase. Down regulates the energy producing portion of the TCA cycle Up regulates the ethanol pathway Up regulates SUCDi When modeling the balanced steady state growth typical of the exponential growth phase, the state of Fis is always set to be true. Fis Biomass Objective Function Biomass_Ecoli_core_w_GAM -> Fis

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -40- FnR Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) FnR is activated in anaerobic conditions Downregulates the energy producing portion of the TCA cycle Upregulates the reductive pathway in the TCA cycle (fumB) Upregulates the fermentation pathway for formate and acetate Downregulates oxidative phosphorylation FnR fumB NOT o2[e] -> FnR

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -41- FruR & surplusFDP Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) When FruR is activated by low fructose levels (surplusFDP = false) FruR reverses the flow of carbon to replenish glycolytic intermediates Upregulates the glycoxylate cycle Upregulates the Gluconeogenesis pathway Downregulates ethanol and acetaldehyde fermentation pathway Downregulates the uptake of glucose fumB surplusFDP FruR The surplusFDP condition is met when fructose, fru [e] is present in the media or the reactions FBP and any of TKT2, TALA, or PGI have zero flux. ((NOT FBP) AND (NOT (TKT2 OR TALA OR PGI))) OR fru[e] The surplusFDP condition is met when fructose, fru [e] is present in the media or the reactions FBP and any of TKT2, TALA, or PGI have zero flux. ((NOT FBP) AND (NOT (TKT2 OR TALA OR PGI))) OR fru[e] NOT surplusFDP -> FnR

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -42- GlnG, Nac & NRI Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) GlnG is activated by a low extracellular ammonium (nh4[e]) concentration and then activates the low-level (fast) nitrogen response, NRI_low. NRI_low activates Nac which down regulates the production of L-glutamate from 2- Oxoglutarate. NRI_low also activates NRI_hi (high-level, slow response) which down regulates the production of L-glutamate from L-glutamine and 2- Oxoglutarate. The total response is to reduce the production of L-gluamate. NOT nh4[e] -> GlnG NRI_hi NRI_low GlnGNac

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -43- Mlc Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) mlc is activated when no glucose is present in the media. When no glucose is present in the media, the transporters for both glucose and fructose are down regulated. glc-D [e] -> mlc mlc

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -44- PdhR & surplusPYR Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) The dual transcriptional regulator pdhR down regulates pyruvate dehydrogenase, PDH, when the pyruvate concentration in the cell is low. High pyruvate concentration is represented by the variable surplusPYR, which is true when there is no flux through MEl or ME2, and no flux through either one of GLGpts, PYK, PFK, LDH_D, or SUGGt2_2. surplusPYR PdhR The surplusPYR condition is true when there is no flux through MEl or ME2, and no flux through either one of GLGpts, PYK, PFK, LDH_D, or SUGGt2_2. (NOT (ME2 OR MEl)) AND (NOT (GLCpts OR PYK OR PFK OR LDH_D OR SUCCt2_2)) The surplusPYR condition is true when there is no flux through MEl or ME2, and no flux through either one of GLGpts, PYK, PFK, LDH_D, or SUGGt2_2. (NOT (ME2 OR MEl)) AND (NOT (GLCpts OR PYK OR PFK OR LDH_D OR SUCCt2_2)) NOT surplusPYR -> PdhR

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -45- PhoB & PhoR Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) Phosphorus uptake is regulated by the two- component system phoR/ phoB. phoR codes for a sensor kinase that is phosphorylated when extracellular inorganic phosphate is not present. The phosphorylated enzyme is activated, and it phosphorylates the transcriptional regulator PhoB. Phosphorylated PhoB then represses the phosphate transporter, PIt2r. The overall effect of phosphorus regulation is to down regulate the phosphate transport reaction, Plt2r, when no extracellular inorganic phosphate is present. PhoR NOT pi[e] -> PhoR PhoB PhoR -> PhoB NOT PhoR -> PIt2r

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -46- CRPnoGLM Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) GLM stands for glucose, lactate or malate The activity of the cAMP receptor protein, Crp is modeled when no glucose, malate or lactate are present in the media using CRPnoGLM. Upregulates the transport pathways for fructose, malate, fumarate, and succinate CRPnoGLC NOT ( glc-D[e] OR mal-L[e] OR lac-D[e] ) -> CRPnoGLM

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -47-

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -49- Catabolite Repression In media containing glucose and other sugar substrates (lactate or malate), E. coli preferentially catabolizes glucose until it is depleted, and then switches to the other substrates. The activity of the cAMP receptor protein, Crp is modeled using CRPnoGLM and CRPnoGLC. The CRPnoGLM regulatory condition is true when either glucose (glc-D[e]), malate (mal- L[e]) or lactate (lac-D[e]) are not present. The CRPnoGLC regulatory condition is true when glucose (glc-D[e]) is absent. The transcription factor, mlc, is also activated when glucose is not present. No glucose: Fermentation pathways are upregulated No GLM: glucose and fructose pathways are downregulated No GLM: Upregulate malate, fumerate, succinate, and fructose transporters No glucose: Change glutamate pathway

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -50- Aerobic Glucose & Fructose % GlucoseFructose_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_fru(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-30, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_fru(e)','EX_glc(e)'}; initConcentrations = [5 10]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fru(e)','EX_glc(e)','EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -51- Anoxic Growth “Low Oxygen” Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) Upregulates the fermentation pathway for formate, acetate, and ethanol Upregulates the reductive pathway in the TCA cycle Downregulates the transporters for malate, fumarase, lactate, and succinate. Downregulates the glycoxylate cycle Downregulates the energy producing portion of the TCA cycle Downregulates oxidative phosphorylation Fnr (fumB) Fnr ArcA

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -52- Low-Aerobic Glucose & Fructose % GlucoseFructose_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_fru(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-5, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_fru(e)','EX_glc(e)'}; initConcentrations = [5 10]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fru(e)','EX_glc(e)','EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -53- Aerobic Glucose & Fumerate % GlucoseFumerateCatabolite_Repression.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_fum(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-30, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_glc(e)','EX_fum(e)'}; initConcentrations = [10 10]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fru(e)','EX_fum(e)','EX_glc(e)','EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -54- Comparing dynamicFBA and dynamicRFBA dynamicGlucoseFumerate_Core.mGlucoseFumerateCatabolite_Repression.m Fumerate is not used until after glucose is gone Glucose and fumerate start at the same time

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -55- Low-Aerobic Glucose & Fumerate % GlucoseFumerateCatabolite_Repression.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_fum(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-5, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_glc(e)','EX_fum(e)'}; initConcentrations = [10 10]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fru(e)','EX_fum(e)','EX_glc(e)','EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -56- Aerobic Glucose, Fructose & Lactate % GlucoseFructoseLactate_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_fru(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-8, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_lac_D(e)',-6, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-30, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_fru(e)','EX_glc(e)','EX_lac_D(e)'}; initConcentrations = [10 8 6]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fru(e)','EX_glc(e)','EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -57- Low-Aerobic Glucose, Fructose & Lactate % GlucoseFructoseLactate_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_fru(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-8, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_lac_D(e)',-6, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-5, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_fru(e)','EX_glc(e)','EX_lac_D(e)'}; initConcentrations = [10 8 6]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fru(e)','EX_glc(e)','EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -59- Growth on Acetate Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) FadR and IclR are activated when either glucose is present in the media or acetate is not. FadR and IclR form a two component histidine kinase system. Down regulates the glycoxylate cycle. GlcC is activated when acetate is present in the media GlcC Upregulates the transport pathway for D-lactate FadR glc-D[e] OR ( NOT ac[e] ) -> FadR FadR -> IclR IclRGlcC ac[e] -> GlcC glcB aceB glcA

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -60- Aerobic Acetate & Lactate % AcetateLactate_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-0, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_ac(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_lac_D(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-30, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_ac(e)','EX_lac_D(e)'}; initConcentrations = [10 8]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fru(e)','EX_glc(e)','EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -61- Growth on C4-Dicarboxylate Compounds Ana TCA OxP PPP Glyc Ferm N N Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010) Activated when malate, fumarate, or succinate (C4-dicarboxylate compounds) are present in the media. DcuS and DcuR form a two component histidine kinase system. The presence of malate, fumarate, and succinate upregulates the reductive pathway in the TCA cycle (fumB) Upregulates the transport pathways for malate, fumarate, and succinate DcuSDcuR fumB succ[e] OR fum[e] OR mal-L[e] -> DcuS DcuS -> DcuR

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -62- Aerobic Fumerate % Fumerate_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-0, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_fum(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-30, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_fum(e)'}; initConcentrations = [10]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fum(e)','EX_fru(e)','EX_glc(e)',... 'EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -63- Aerobic Succinate % Succinate_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-0, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_succ(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-30, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_succ(e)'}; initConcentrations = [10]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fum(e)','EX_fru(e)','EX_glc(e)','EX_gln_L(e)',... 'EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -64- Aerobic Fumerate & Succinate % FumerateSuccinate_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-0, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_fum(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_succ(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-30, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_fum(e)','EX_succ(e)'}; initConcentrations = [10 8]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fum(e)','EX_fru(e)','EX_glc(e)',... 'EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -65- Aerobic Pyruvate % Pyruvate_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-0, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_pyr(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-30, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_pyr(e)'}; initConcentrations = [10]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fum(e)','EX_fru(e)','EX_glc(e)',... 'EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_pyr(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -66- Anaerobic Pyruvate % Pyruvate_Anaerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-0, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_pyr(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-5, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_pyr(e)'}; initConcentrations = [10]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fum(e)','EX_fru(e)','EX_glc(e)',... 'EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_pyr(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns);

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -67- dynamicRFBA Limitations The dynamicRFBA tool cannot simulate the fed batch mode. There is no way to account for substrates that enter the medium via fed batch mode, so it can only show what becomes of the initial concentrations. The dynamicRFBA was created to optimize the biomass reaction, so there is currently no way to maximize reactions for protein production, or to maximize both the growth rate and protein production at the same time. The predicted growth rate can reach values higher than possible because the calculated growth rate is constantly in the exponential phase. The dynamicFBA tool more useful for qualitative rather than quantitative study.

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -68- Dynamic Regulatory FBA Review Questions 1.What is a transcriptional regulatory network? 2.What is a boolean regulatory network? 3.What are the key inputs required for dynamic regulatory FBA operation? 4.What is a regulated model? How is it different from a normal FBA model? 5.What is a regulatory rule? 6.What is the difference between a regulatory gene and a gene that is controlled by regulation? 7.What are some of the differences between the output of dynamicFBA and dynamicRFBA? 8.What are the strengths of dynamic regulatory FBA? 9.What are the weaknesses of dynamic regulatory FBA?

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -70- imc1010v2 Model Regulatory genes have been added to the ijo904 model Based on dynamicRFBA Covert, M. W., E. M. Knight, et al. (2004). "Integrating high-throughput and computational data elucidates bacterial networks." Nature 429(6987): 92-96. SR-FBA This method works by iteratively predicting a regulatory and metabolic steady state for short successive time intervals. For each time interval, a regulatory state that is consistent with the metabolic steady state of the previous interval (and with the availability of nutrients in the changing growth media) is computed. Then, FBA is used to find a steady-state flux distribution that is consistent with the regulatory state of the current time interval. Shlomi, T., Y. Eisenberg, et al. (2007). "A genome-scale computational study of the interplay between transcriptional regulation and metabolism." Molecular Systems Biology 3: 101. Feuer Combination of iAF1260 and iMC1010v2: The computation of a regulatory model combined with metabolic model was outlined by Covert et al. Based on dynamicRFBA Feuer, R., K. Gottlieb, et al. (2012). "Model-based analysis of an adaptive evolution experiment with Escherichia coli in a pyruvate limited continuous culture with glycerol." EURASIP J Bioinform Syst Biol 2012(1): 14. Tiger TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. Jensen, P. A., K. A. Lutz, et al. (2011). "TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks." BMC systems biology 5: 147. Other Regulatory-based Model Approaches

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -72- EXTRA SLIDES

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -73- Aerobic Malate % Malate_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-0, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_mal_L(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-30, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_mal_L(e)'}; initConcentrations = [10]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fum(e)','EX_fru(e)','EX_glc(e)',... 'EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns); X

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -74- Aerobic Malate, Fumerate & Succinate % MalateFumerateSuccinate_Aerobic.m clear; load('modelReg'); modelReg = changeRxnBounds(modelReg, 'EX_glc(e)',-0, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_mal_L(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_fum(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_succ(e)',-10, 'l'); modelReg = changeRxnBounds(modelReg, 'EX_o2(e)',-30, 'l'); [FBAsols,DRgenes,constrainedRxns,cycleStart,states]= optimizeRegModel(modelReg); substrateRxns = {'EX_fum(e)','EX_mal_L(e)','EX_succ(e)'}; initConcentrations = [10 8 6]; initBiomass =.035; timeStep =.25; nSteps = 100; plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_fum(e)','EX_fru(e)','EX_glc(e)',... 'EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_succ(e)'}; [concentrationMatrix,excRxnNames,timeVec,biomassVec,drGenes,constrainedRxns,states] =... dynamicRFBA(modelReg,substrateRxns,initConcentrations,initBiomass,timeStep,nSteps,plotRxns); X

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -75- Nac, NRI_low, & NRI_hi Regulation of nitrogen metabolism in the E.coli core model. Extracellular ammonium, nh 4 [e]: activates the low and high level nitrogen responses, NRI_low and NRI_ hi, which, along with extracellular glutamate, glu-L[e], inhibit the reactions glutamate dehydrogenase, GLUDy: and glutamate synthase, GLUSy. Glutaminase, GLUN, is also activated by extracellular ammonium. Extracellular glucose, glc-D[e], through CRPnoGLC, inhibits glutamine synthetase, GLNS, and glutaminase, GLUN. Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010)

Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -1- Dynamic Flux Balance.

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Lesson: dFBA & dRFBABIE 5500/6500Utah State University H. Scott Hinton, 2015 Constraint-based Metabolic Reconstructions & Analysis -1- Dynamic Flux Balance.

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