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, 2015Constraint-based Metabolic Reconstructions & Analysis -1-

Dynamic Flux Balance Analysis&

Dynamic Regulatory Flux Balance Analysis



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 Outline

• Dynamic Flux Balance Analysis (dynamicFBA)Basic OperationMinimal Media ExamplesLimiting Media Examples

• Regulatory Flux Balance Analysis (dynamicRFBA)

Regulated E.coli Core ModelRegulatory GenesCatabolite Repression ExamplesGrowth on Non-glucose Carbon Sources

• Other Regulatory-based Model Approaches



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.



Dynamic Flux Balance Analysis• The substrate concentration (Sc ) (mmol/L) is determined from the substrate concentration predicted for the previous step

(Sco ) or from the initial substrate concentration if it is the first time step:

Sc= Sco

• The substrate concentration is scaled to define the amount of substrate available per unit of biomass per unit of time (mmol

gDW-1h-1):

where X is the current cell density and Xo is the cell density from the previous step.

• FBA is then used to calculate the substrate uptake ( Su ) 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.

cSSubstrate available

X t

to

dXX X X e

dt

(1 )tc uu c co o

dS SS X S S X e

dt



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 Outline







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);



Low Aerobic, Glucose Substrate DynamicFBA Growth


clear;







initBiomass = .01;







Substrate Maximum Growth Rate

Substrate Aerobic (hr-1) Anaerobic (hr-1)acetate 0.3893 0

acetaldehyde 0.6073 0

2-oxoglutarate 1.0982 0

ethanol 0.6996 0

D-fructose 1.7906 0.5163

fumarate 0.7865 0

D-glucose 1.7906 0.5163

L-glutamine 1.1636 0

L-glutamate 1.2425 0

D-lactate 0.7403 0

L-malate 0.7865 0

pyruvate 0.6221 0.0655

succinate 0.8401 0("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.



Anaerobic, Glucose Substrate DynamicFBA Growth


clear;


model = changeRxnBounds(model, {'EX_glc(e)','EX_o2(e)'},[-10 0], 'l');





initBiomass = .01;







DynamicFBA: Ethanol Production with Glucose Substrate

% DynamicEthanolProduction_JO1366.m

clear;




% Knockouts

model = changeRxnBounds(model, {'PFL','PPC','PPKr'},[-0 -0 -0], 'b');




initBiomass = .01;







Lesson Outline







% Dynamic_Growth_K12media.m

clear;

% Read model

model = readCbModel('ecoli_iJO1366');


%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)'};


%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



Glucose

Phosphate

Ammonium

Potassium

Dynamic_Growth_K12media.m

Low Oxygen: EX_o2(e) = -30



MatlabProperty

Editor

Under View Menu



Glucose

Phosphate

Ammonium

Potassium

Dynamic_Growth_K12media.m Formate

Acetate

Low Oxygen: EX_o2(e) = -5



Aerobic Biliverdin Production% DynamicBiliverdinProduction_JO1366.m

clear;


% Add heme oxygenase enzyme

...


model = changeRxnBounds(model,'EX_biliverdin(e)',1.2,'l');

model = changeRxnBounds(model,'HEMEOX',1.2,'b');


% Set-up for one variables for dynamicFBA


initConcentrations = 20;

initBiomass = .01;


plotRxns = {'EX_ac(e)','EX_biliverdin(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)'};





Low-Aerobic Biliverdin Production% DynamicBiliverdinProduction_JO1366.m

clear;


% Add heme oxygenase enzyme

...


model = changeRxnBounds(model,'EX_biliverdin(e)',1.2,'l');

model = changeRxnBounds(model,'HEMEOX',1.2,'b');





initBiomass = .01;


plotRxns = {'EX_ac(e)','EX_biliverdin(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)'};





Aerobic PHB Production% DynamicPHBProduction_JO1366.m

clear;


% Add PHB Reactions

...

% Add demand reaction

model = addDemandReaction(model,'phb[c]');

model = changeRxnBounds(model,'PHBpoly',10,'l');

% Set key parameters






initBiomass = .01;


plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)','DM_phb[c]'};





Aerobic Spider Silk Production% DynamicSpiderSilkProduction_JO1366.m

clear;


%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






initBiomass = .01;


plotRxns = {'EX_ac(e)','EX_etoh(e)','EX_for(e)','EX_glc(e)','DM_flys3[c]'};





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.



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 Outline







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 predictionsReconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide by Orth, Fleming, and Palsson (2010)



Transcriptional Regulatory Networks

E.coli metabolic core network E.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)



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.




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 concentration is assumed to be high enough to not be limiting. If the uptake rate for a nutrient is calculated to exceed the maximum uptake rate for that nutrient specified in the model and the max uptake rate specified is > 0, the maximum uptake rate specified in the model is used instead of the calculated uptake rate.

Dynamic Regulatory Flux Balance Analysis



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);



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);



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

DcuRSee core_regulatory_rules.xls



bNum Gene Ruleb4401 ArcA NOT o2[e]b3357 Crp CRPnoGLCb4124 DcuR DcuSb4125 DcuS succ[e] OR fum[e] OR mal-L[e]b1187 FadR glc-D[e] OR (NOT ac[e])b3261 Fis Biomass_Ecoli_core_w_GAMb1334 Fnr NOT o2[e]b0080 FruR NOT surplusFDPb2980 GlcC ac[e]b3868 GlnG NOT nh4[e]b4018 IclR FadRb1594 Mlc NOT glc-D[e]b1988 Nac NRI_lowb0113 PdhR NOT surplusPYRb0399 PhoB PhoRb0400 PhoR NOT pi[e]

CRPnoGLC NOT glc-D[e]CRPnoGLM NOT (glc-D[e] OR mal-L[e] OR lac-D[e])NRI_hi NRI_lowNRI_low GlnGsurplusFDP ((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



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_GAMcore_regulatory_rules.xls



Regulatory Map of the Regulated

Core Model



Lesson Outline







ArcA

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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



CRPnoGLC

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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



DcuR & DcuS

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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

(C4-dicarboxylate compounds)

DcuS

DcuR

fumB

succ[e] OR fum[e] OR mal-L[e] -> DcuS

DcuS -> DcuR



FadR, IclR & GlcC

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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] ) -> FadRFadR -> IclR

IclR

GlcC

ac[e] -> GlcC

glcB

aceB

glcA



Fis

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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 Objectiv

e Functio

n

Biomass_Ecoli_core_w_GAM -> Fis



FnR

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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



FruR &surplusFDP

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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]

NOT surplusFDP -> FnR



GlnG, Nac & NRI

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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

GlnG

Nac



Mlc

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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



PdhR &surplusPYR

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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))

NOT surplusPYR -> PdhR



PhoB & PhoR

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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



CRPnoGLM

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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 Outline







CataboliteRepression

• 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



Aerobic Glucose & Fructose% GlucoseFructose_Aerobic.m

clear;

load('modelReg');


modelReg = changeRxnBounds(modelReg, 'EX_fru(e)',-10, 'l');



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)'};





Anoxic Growth“Low Oxygen”

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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



Low-Aerobic Glucose & Fructose% GlucoseFructose_Aerobic.m

clear;

load('modelReg');





substrateRxns = {'EX_fru(e)','EX_glc(e)'};


initBiomass = .035;

timeStep = .25;

nSteps = 100;






Aerobic Glucose & Fumerate% GlucoseFumerateCatabolite_Repression.m

clear;

load('modelReg');


modelReg = changeRxnBounds(modelReg, 'EX_fum(e)',-10, 'l');



substrateRxns = {'EX_glc(e)','EX_fum(e)'};


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)'};





Comparing dynamicFBA and dynamicRFBA

dynamicGlucoseFumerate_Core.m GlucoseFumerateCatabolite_Repression.m

Fumerate is not used until after glucose is goneGlucose and

fumerate start at the same time



Low-Aerobic Glucose & Fumerate% GlucoseFumerateCatabolite_Repression.m

clear;

load('modelReg');





substrateRxns = {'EX_glc(e)','EX_fum(e)'};


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)'};





Aerobic Glucose, Fructose & Lactate% GlucoseFructoseLactate_Aerobic.m

clear;

load('modelReg');



modelReg = changeRxnBounds(modelReg, 'EX_lac_D(e)',-6, 'l');



substrateRxns = {'EX_fru(e)','EX_glc(e)','EX_lac_D(e)'};

initConcentrations = [10 8 6];

initBiomass = .035;

timeStep = .25;

nSteps = 100;






Low-Aerobic Glucose, Fructose & Lactate% GlucoseFructoseLactate_Aerobic.m

clear;

load('modelReg');






substrateRxns = {'EX_fru(e)','EX_glc(e)','EX_lac_D(e)'};


initBiomass = .035;

timeStep = .25;

nSteps = 100;






Lesson Outline







Growth on Acetate

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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] ) -> FadRFadR -> IclR

IclR

GlcC

ac[e] -> GlcC

glcB

aceB

glcA



Aerobic Acetate & Lactate

% AcetateLactate_Aerobic.m

clear;

load('modelReg');


modelReg = changeRxnBounds(modelReg, 'EX_ac(e)',-10, 'l');




substrateRxns = {'EX_ac(e)','EX_lac_D(e)'};


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)'};





Growth on C4-Dicarboxylate

Compounds

Ana TCA

OxP

PPP

Glyc

Ferm

N


• 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

DcuS

DcuR

fumB

succ[e] OR fum[e] OR mal-L[e] -> DcuS

DcuS -> DcuR



Aerobic Fumerate

% Fumerate_Aerobic.m

clear;

load('modelReg');





substrateRxns = {'EX_fum(e)'};


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)', ...






Aerobic Succinate

% Succinate_Aerobic.m

clear;

load('modelReg');


modelReg = changeRxnBounds(modelReg, 'EX_succ(e)',-10, 'l');



substrateRxns = {'EX_succ(e)'};


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)'};





Aerobic Fumerate & Succinate

% FumerateSuccinate_Aerobic.m

clear;

load('modelReg');






substrateRxns = {'EX_fum(e)','EX_succ(e)'};


initBiomass = .035;

timeStep = .25;

nSteps = 100;







Aerobic Pyruvate

% Pyruvate_Aerobic.m

clear;

load('modelReg');


modelReg = changeRxnBounds(modelReg, 'EX_pyr(e)',-10, 'l');



substrateRxns = {'EX_pyr(e)'};


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)'};





Anaerobic Pyruvate

% Pyruvate_Anaerobic.m

clear;

load('modelReg');


modelReg = changeRxnBounds(modelReg, 'EX_pyr(e)',-10, 'l');



substrateRxns = {'EX_pyr(e)'};


initBiomass = .035;

timeStep = .25;

nSteps = 100;


'EX_gln_L(e)','EX_glu_L(e)','EX_lac_D(e)','EX_mal_L(e)','EX_pyr(e)','EX_succ(e)'};





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.



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 Outline







• 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 Outline







EXTRA SLIDES



Aerobic Malate

% Malate_Aerobic.m

clear;

load('modelReg');


modelReg = changeRxnBounds(modelReg, 'EX_mal_L(e)',-10, 'l');



substrateRxns = {'EX_mal_L(e)'};


initBiomass = .035;

timeStep = .25;

nSteps = 100;





X



Aerobic Malate, Fumerate & Succinate

% MalateFumerateSuccinate_Aerobic.m

clear;

load('modelReg');


modelReg = changeRxnBounds(modelReg, 'EX_mal_L(e)',-10, 'l');





substrateRxns = {'EX_fum(e)','EX_mal_L(e)','EX_succ(e)'};


initBiomass = .035;

timeStep = .25;

nSteps = 100;





X



Nac, NRI_low, & NRI_hi

• Regulation of nitrogen metabolism in

the E.coli core model.

• Extracellular ammonium, nh4[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.


Documents

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