computational tools for the analysis of microbial ... · metabolic engineering is the analysis and...

Computational Tools for the Analysis of Microbial Production Systems

E-mail: [email protected]

Web page: fenske.che.psu.edu/faculty/cmaranas

Penn State University

University Park, PA 16802

Costas D. Maranas

Chemical factory on the µµµµm scale

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

Escherichia coli Chemical Process Plant

� Metabolism is the totality of chemical reactions that occur in an organism

� Metabolic Engineering is the analysis and modification of metabolic pathways

• Applications include biochemical production, bioremediation, and drug discovery

What is Metabolism?

DNA

SequenceGenes

Proteins

(Enzymes)Metabolic

Pathways

Cellular

Physiology

Increasing Complexity

HT Technologies for Systems BiologyDNA Sequence

Genes

Enzymes

(Gene Products)

Metabolism and

Cellular Physiology

Automated Sequencing

Genomics

DNA Microarrays

Transcriptomics

2-D Protein Arrays

Proteomics

Metabolic Reconstruction Technology

Genome Annotation:

DNA sequence

ORFs identification

Genes

ORFs assignment

Genes Products

Function

Genome

Database

List of reactions

Pathway

Database

Organism’s

metabolism

Metabolic Reconstruction:

ORF = open

reading frame, a

short fragment of

DNA that is

translated into

RNA message

Manual curation

Wet Lab

Literature Review

Organism-Specific Model Construction:

Complexity of Metabolic NetworksBiodegradation of

Xenobiotics

Metabolism of

Complex Carbohydrates

Nucleotide

MetabolismMetabolism of

Complex Lipids

Carbohydrate

Metabolism

Metabolism of

Other Amino Acids

Amino Acid

Metabolism

Lipid

Metabolism

Metabolism of

Cofactors and VitaminsEnergy

Metabolism

Biosynthesis of

Secondary Metabolites

Glucose

Glc-6-P

Fru-6-P

“Small” E. coli Model (<100 reactions)

Central Metabolism in E. coli( http://gcrg.ucsd.edu/downloads/PathwayFBA/default.htm)

Glucose → G6P

Biomass Composition

Amino acids

Fatty Acids

Glycogen

Energy metabolites (i.e., ATP, GTP)

Succinyl CoA, Acetyl CoA

Biomass

Biomass formation modeled as:

∑(x)(metabolite) → Biomass + ∑ (y)(metabolite)

ADP

Phosphate

Pyro-phosphate

Protons

+

Mass Balances in Metabolic Networks

Reaction network:

2B C

A B

v1

v2

Stoichiometric matrix: A

B

C

v1 v2

-1 0

1 -2

0 1

Mass balance:

dt= v1 – 2 v2

d[B]

Steady-state assumption: ∑=j

jijvS0

i = set of metabolites (chemical species)

j = set of reactions

mmol B

gDW⋅⋅⋅⋅hrv ≡≡≡≡

Metabolic Flux:

Biomass Composition

Flux Balance Analysis

4D

3C

2B

(mmol / gDW)

4D

3C

2B

(mmol /

AAxt B C

D

Dxt

biomassbiomass

biomass

bA

bD

v1 v2

v3

vbio vbio

vbio

“Cell boundary”

AAxt B C

D

Dxt

biomassbiomass

biomass

bA

bD

v1 v2

v3

vbio vbio

vbio

“Cell boundary”

Given:

(1) Stoichiometry of the network

(2) Cellular composition information

(3) Substrate uptake rate

bA = mmol

gDW*hr-10

1000

-4100

-3010

-2-1-11

000-1

1000

-100

-010

--1-11

000-1

=

Optimization Model:

Maximize vbio

subject to

0

0

-10

0

0

-10

5 unknowns > 4 equations

vbio

v1

v2v3

bD

v1,v2,v3,vbio,bD > 0

D

3.33310

D

AAxt B C

D

biomassbiomass

biomass

“Cell boundary”

AAxt B C

Dxt

biomassbiomass

biomass

10

0

4.444

“Cell boundary”

Growth rate: = 1.111 hr-1Model Predictions:

vbio

1.111

1.111

1.111

Units of flux: mmol/(gDW*hr)

Presentation Outline� Systems biology and the constraints-based modeling approach

� Pathway discovery and optimization

� Constraining allowable cellular behavior

How can we identify multiple metabolic manipulations for producing a

desired product and also computationally evaluate of the consequences of

potential network modifications ?

� Metabolic network structural and topological analysis

How can we systematically select the appropriate set of pathways/genes

to recombine into existing production systems?

Desired

Phenotype

E. coli metabolic

capabilities

How can we identify gene knockouts that will force biochemical

overproduction by coupling it with cell growth?

E. coli metabolic

capabilities

Expanding the CapabilitiesE. coli Stoichiometric Models:

• Pramanik & Keasling (1997)

(300 reactions, 289 metabolites)

• Edwards & Palsson (2000)


• Reed, Vo, Schilling & Palsson (2003)


+

5,700

reactions

from KEGG

and MetaCyc

databases

Multi-organism

Reaction

Network

• Burgard, A.P. and C.D. Maranas (2001), “Probing the Performance Limits of the Escherichia coli Metabolic Network Subject to

Gene Additions or Deletions,” Biotechnology and Bioengineering, 74, 364-375.

Pharkya, P., Burgard, A.P and C.D. Maranas (2004), “OptStrain: A Computational Framewrok for Redesign of Microbial

Production Systems,” Genome Research, 14, 2367-2376.

Desired

Phenotype

E. coli metabolic

capabilities

~ 1000 reactions

KEGG database

~ 6000 reactions

Gene Addition Study

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

GL3P

Glycerol

3-HP

1,3 PD

IPP

DMAPPPYR

Non-mevalonate pathway

MEV

Mevalonate

pathway

Useful in Apparels, Upholstries, etc.

Precursors to anticancer

and antimalarial drugs

Mathematical Framework∑

j

jjvc

0=∑j

jijvS

=0

1jy

if reaction flux switched “on”

if reaction flux switched “off”

Maximize

Subject to

jjj yv ⋅≤≤ max0 ν

∑=

reactionsnonEcolij

jy


Procedure: 1) Find maximum theoretical yield using all reactions in

multi-organism reaction network

2) Find minimum number of non-E. coli reactions necessary to

achieve maximum yield

0=∑j

jijvS

Yield = maximum theoretical

Minimize

Subject to

1) Pathway Discovery

Example: Glucose to 1,3-Propanediol using KEGG database

Typical Shortest Path Result

D-Glucose

Glycerol

D-Fructose

D-Fructose-1-phosphate

D-glyceraldehyde

1,3-Propanediol

3-Hydroxypropanal

Balanced Shortest Path Result

at Max. Yield

D-Glucose

Glycerol

D-Fructose


Dihydroxyacetone phosphate D-glyceraldehyde

1,3-Propanediol

3-Hydroxypropanal

sn-Glycerol-3-phosphate

Max. Yield: 2 mol 13PD

1 mol GLC

Net Cost: 2 NADH

2 NADPH

� Branched pathways

� Energy/cofactor requirements

� Maximum yield information

Key Advantages

ATP

ADP

NADPH

NADP

ATPADP

NADH

NAD

NADPH

NADP

1) Pathway Discovery

Example: Glucose to 1,3-Propanediol using KEGG database

Typical Shortest Path Result

D-Glucose

Glycerol

D-Fructose


D-glyceraldehyde

1,3-Propanediol

3-Hydroxypropanal

Alternative Pathway

D-Glucose

Glycerol-3-phosphate

D-Glucose-6-Phosphate


Dihydroxyacetone phosphate

1,3-Propanediol

3-Hydroxypropanal

D-Glyceraldehyde-3-phosphate

Max. Yield: 2 mol 13PD

1 mol GLC

Net Cost: 2 ATP

2 NADH

� Branched pathways

� Energy/cofactor requirements

� Maximum yield information

Key Advantages

ATP

ADP

NADH

NAD

NADH

NAD

D-Fructose-1,6-phosphate

Glycerol

ATP

ADP










Desired

Phenotype

E. coli metabolic

capabilities



Desired

Phenotype

E. coli metabolic

capabilities

E. coli metabolic

capabilities

Motivating Challenge

Maximum

Theoretical Yields

(mol/mol glucose)

1.71

2.00

Acetate

Succinate

Ethanol

2.00Lactate

2.78Formate

2.40

Experimental

Yields

(mol/mol glucose)

0.34

0.84

Acetate

Succinate

Ethanol

0.10Lactate

1.16Formate

0.81

(Stokes, J. Bacteriology, 1949)

Cellular

ObjectiveBioengineering

Objective(e.g.,

Max. biomass production

Min. metabolic adjustment

Max. ATP production, etc.)

(e.g.,

Max. 1,3-propanediol yield

Max. lactate yield, etc.)

Motivating Challenge

Maximum

Theoretical Yields

(mol/mol glucose)

1.71

2.00

Acetate

Succinate

Ethanol

2.00Lactate

2.78Formate

2.40

Experimental

Yields

(mol/mol glucose)

0.34

0.84

Acetate

Succinate

Ethanol

0.10Lactate

1.16Formate

0.81

(Stokes, J. Bacteriology, 1949)

Cellular

Objective

Bioengineering

Objective

??

(e.g.,

Max. biomass production

Min. metabolic adjustment

Max. ATP production, etc.)

(e.g.,

Max. 1,3-propanediol yield

Max. lactate yield, etc.)

OptKnock Bilevel Optimization Framework

Inner Problem:adjust reaction fluxes

� optimize cellular objective

− Max. biomass yield

− Min. metabolic adjustment

− Max. ATP yield

Maximize

s.t.

Biomass Yield

� Fixed substrate uptake rate

� Network connectivity

(over fluxes)

s.t.

MaximizeOuter Problem:adjust knockouts

� optimize bioeng. objective

− Max. 1,3-propanediol yield

− Max. lactate yield

Biochemical Yield

� Blocked reactions identified

by outer problem

�Minimum biomass yield

� # Knockouts ≤≤≤≤ limit

(over gene knockouts)

• Burgard, A.P., Pharkya, P., and C.D. Maranas (2003), “OptKnock: A bilevel programming framework for identifying gene

knockout strategies for microbial strain optimization,” Biotechnology and Bioengineering, 84, 647-657.

• Pharkya, P., Burgard, A.P., and C.D. Maranas (2003), “Exploring the overproduction of amino acids using the bilevel

optimization framework OptKnock,” Biotechnology and Bioengineering, 84, 887-899.

PRIMAL (Inner problem)

subject to

LP Duality Theory

uptakevGLC =

BiomassPRIMAL vZ =Maximize

0=∑j

jijvS

0≥jv

ZDUAL

ZPRIMAL

Optimal solution

if and only if:

ZDUAL = ZPRIMAL

vj

DUAL

guptakeZDUAL ⋅=Minimize

subject to

ui, g

ui

g

multipliers

0, =+∑ gSui

GLCii

1, ≥∑i

BiomassiiSu

0≥∑i

ijiSu

PRIMAL (Inner problem)

subject to

LP Duality Theory

0, =+∑ gSui

GLCii

DUAL

uptakevGLC =

BiomassPRIMAL vZ =Maximize

0=∑j

jijvS

0≥jv

guptakeZDUAL ⋅=Minimize

1, ≥∑i

BiomassiiSu

subject to

ZDUAL

ZPRIMAL

0≥∑i

ijiSu

=⋅ guptake Biomassv

Maximize Productvyj, ui, g, vj

subject to

{ }1,0∈jy

0, =+∑ gSui

GLCii

1, ≥∑i

BiomassiiSu

0≥∑i

ijiSu

uptakevGLC =

0=∑j

jijvS

ui, g

vj

∑ ≤−j

jy )1(


Dual

Primal

MILP problem

# of knockouts

Optimal Gene Knockout Identification

Case Studies:

(1) 1,3 Propanediol (2) Lactate

Questions:

� Identify single, double, triple, and quadruple knockout strategies?

� Characterize allowable envelope of biomass vs. biochemical production?

?

?

?

Growth

Biochemical

Production

1,3 PD Overproduction

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8 1 1.2

Growth Rate (1/hr)

1,3 PD Production Limits

(mmol/hr)

Complete E. coli network

Basis: 10 mmol/hr glucose, 1 gDW cells

Maximum Growth:

E. coli (wild type)

GL3P

Non-E. colireaction

Glycerol

GPP1& 2

3-HP

dhaB

1,3 PD

dhaT

Non-E. coli genes/enzymes: GPP1&2: glycerol-3-phosphatase

dhaB: glycerol dehydratase

dhaT: 1,3 PD oxidoreductase

Klebsiella pneumoniae

Klebsiella pneumoniae

Saccharomyces cerevisiae

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

1,3 PD Overproducing Mutants

GLAL


Maximum Biomass :

1,3 PD:1.05 hr-1

0.00 mmol/hr“Wild” type:

Maximum Biomass :

1,3 PD:0.21 hr-1

9.66 mmol/hrMutant A:

Mutant A:

(3) Fructose-1,6-bisphosphatase (fbp) or Fructose-1,6-bisphosphate aldolase (fba)

(2) Phosphoglycerate kinase (pgk) or Glyceraldehyde-3-phosphate dehydrogenase (gapA, gapC1C2)

(1) Aldehyde dehydrogenase (adhC)

GL3P

Non-E. colireaction

Glycerol

2KD6PG

Entner-Doudoroff

3-HP

1,3 PD

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

1,3 PD Overproducing Mutants

Non-E. colireaction

Maximum Biomass :

1,3 PD :

0.11 hr-1

9.84 mmol/hrMutant C:

Maximum Biomass :

1,3 PD :

0.14 hr-1

9.78 mmol/hr

Mutant D:

Maximum Biomass :

1,3 PD :

0.16 hr-1

9.75 mmol/hrMutant E:

GLAL

DR5P

ACAL

Maximum Biomass :

1,3 PD :

0.29 hr-1

9.67 mmol/hrMutant B:

Mutant B:(2) Triose phosphate isomerase (tpiA)

(3) Glucose 6-phosphate-1-dehydrogenase (zwf) or 6-Phosphogluconolactonase (pgl)

(1) Aldehyde dehydrogenase (adhC)

(4) Deoxyribose-phosphate aldolase (deoC)

Maximum Biomass :

1,3 PD :0.21 hr-1

9.66 mmol/hrMutant A:


Maximum Biomass :

1,3 PD :1.05 hr-1

0.00 mmol/hr“Wild” type:

GL3P

Glycerol

3-HP

1,3 PD

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8 1 1.2

Growth Rate (1/hr)

1,3 PD Production Limits

(mmol/hr)

Complete E. coli network

1,3 PD Mutant CharacterizationBasis: 10 mmol/hr glucose, 1 gDW cells

Maximum Growth:

E. coli (wild type)

Mutant A

Mutant B

Mutant C

Mutant D

Mutant E

Mutant A

Mutant B

Lactate Mutant Experimentation

60 days ~ 500 generations

Three designs constructed:

Blattner Lab: Strain Construction

Palsson Lab: Adaptive Evolution

1) pta-adhE (5 strains evolved)

2) pta-pfk (3 strains evolved)

3) pta-adhE-pfk-glk (3 strains evolved)

Increased glucose uptake rates or increased lactate yields ?

0

5

10

15

20

25

30

35

40

0 0.1 0.2 0.3

Growth rate (1/hr)

Lactate Secretion R

ate (mmol/g-

DW.hr)

pta-pfk SB1

pta-pfk SB2

pta-pfk SB3

pta-adhE-pfk-glk SB1



pta-adhe SB1

pta-adhE SB2

pta-adhE SB3

pta-adhE SB4

pta-adhE SB5










Desired

Phenotype

E. coli metabolic

capabilities



Desired

Phenotype

E. coli metabolic

capabilities

E. coli metabolic

capabilities

Application to genome-scale models:

1. Helicobacter pylori 389 reactions

2. Escherichia coli 627 reactions

3. Saccharomyces cerevisiae 1173 reactions

(Schilling et al., J Bacteriol, 2002)

(Edwards and Palsson, PNAS, 2000)

(Forster et al., Genome Res, 2003)

G6P

F16P

F6P

13P2DG

3PG

2PG

PEP

PYR

GAPDHAP

GLC

v1

v2

Steady-state Flux Coupling Analysis

Identify sets of coupled reactions, equivalent knockouts, and

affected reactions in genome-scale stoichiometric models.

Objective:

How are the two fluxes v1 and v2 related?

1. Does v1 imply v2 ?

2. Do v1 and v2 imply each other?

3. Are v1 and v2 completely uncoupled?

Questions:Coupled Reactions

Equivalent Knockouts

Which reactions could alternatively be deleted to force the flux

through a particular reaction to zero?

Affected Reactions

Which reaction fluxes will be forced to zero if a particular reaction

is removed from the network?

• Burgard, A.P., Nikolaev, E.V., and C.D. Maranas (2004), “Flux coupling analysis of genome-scale metabolic network

reconstructions ,” Genome Research, 14, 301-312.

max v1 / v2 = Rmax

min v1 / v2 = Rmin

Rmin= 0 Rmax= ∞Uncoupled:

Rmin= c1 Rmax= c2

Partially Coupled:

v1 ↔ v2

0∞

Rmin= Rmax= c

Fully Coupled:

v1 ⇔ v2

0∞

Rmin ≤ ≤ Rmaxv1

v2

Identifying Coupled Reactions

Rmin= 0 Rmax= c

Directionally Coupled:

v1 → v2 ∞

Directionally Coupled: v2 → v1

Rmin= c Rmax= ∞0

Potential Flux Ratio Outcomes:

12 =v)

Fractional programming formulation:

To find the presence and directionality of coupling between two fluxes v1 and v2:

transformation

Identifying Coupled Reactions

maximize (or minimize) v1 / v2

01

=∑=

M

j

jijvS

0≥jv

subject to

vuptake ≤ vuptake_max

vj / v2

1v)

01

=∑=

M

j

jijvS)

12 =v)

maximize (or minimize)

subject to

uptakev)

≤ vuptake_max⋅t

0≥jv)

t ≥ 0

Linear formulation: where =jv)

� Network size ↑ % Coupled reactions ↓

� Presence of Large coupled

biomass reaction reaction sets

� Conditions only slightly affect coupling.

19 sets of 2 reactions complex

media

glucose

minimal

complex

media

glucose

minimal

complex

media

glucose

minimal

complex

media

glucose

minimal

complex

media

glucose

minimal

complex

media

glucose

minimal

19 (2) 15 (2) 25 (2) 23 (2) 45 (2) 33 (2) 48 (2) 37 (2) 51 (2) 45 (2) 51 (2) 46 (2)

8 (3) 7 (3) 10 (3) 9 (3) 9 (3) 6 (3) 9 (3) 9 (3) 13 (3) 11 (3) 14 (3) 14 (3)

2 (4) 1 (5) 3 (4) 2 (4) 4 (4) 1 (4) 4 (4) 3 (4) 6 (4) 5 (4) 7 (4) 5 (4)

1 (6) 1 (7) 1 (5) 2 (5) 3 (5) 1 (5) 5 (5) 5 (5) 2 (5) 3 (5) 2 (5) 3 (5)

2 (7) 1 (10) 2 (6) 3 (6) 2 (6) 3 (7) 2 (6) 2 (6) 2 (6) 2 (6) 2 (6) 2 (6)

1 (10) 1 (174) 3 (7) 2 (7) 1 (7) 1 (10) 2 (7) 2 (7) 1 (7) 1 (7) 1 (7) 1 (7)

1 (148) 1 (8) 1 (8) 1 (8) 1 (112) 1 (8) 1 (8) 2 (8) 2 (8) 2 (8) 2 (8)

1 (9) 1 (9) 2 (9) 3 (9) 3 (9) 1 (9) 1 (9) 1 (9) 1 (9)

4 (10) 4 (10) 1 (66) 1 (10) 1 (10) 1 (12) 1 (12) 1 (12) 1 (12)

1 (13) 1 (13) 1 (17) 1 (17) 1 (30) 1 (34) 1 (17) 1 (17)

1 (20) 1 (20)

total reactions

in subsets:248 247 220 213 259 236 252 226 261 248 255 242

total subsets: 34 26 52 49 68 46 76 64 80 72 82 76

H. pylori E. coli S. cerevisiaebiomass reaction no biomass reaction biomass reaction no biomass reaction biomass reaction no biomass reaction

Coupled to

biomass formation

→

Reaction Coupling Statistics

Com

plex (B

io)

Glc (B

io)

Com

plex

(No Bio)

Glc (N

o Bio)

H. pylori

E. coli

S. cerevisiae

0%

10%

20%

30%

40%

50%

60%

70%

% of Reactions in Coupled Sets

H. pylori

E. coli

S. cerevisiae

Directional Coupling

v1

v2

v3

v*

Reactions “affected” by v*

Corresponding flux ratio outcomes,

v1,2, or 3 ≤≤≤≤ c⋅⋅⋅⋅v*

v4

v5

v6

v* ≤≤≤≤ c⋅⋅⋅⋅v4,5, or 6

“Equivalent KO’s” for v*

Equivalent Knockouts vs. Affected Reactions

E. coli Central Metabolic Network Coupling

Growth on glucose minimal media

Steady-state conditions

� The forward and backward directions of the major

metabolic pathways show significant internal coupling.

(1) glycolysis

(2) pentose phosphate pathway

(3) TCA cycle

� Anaplerotic and respiration reactions are uncoupled

from the rest of central metabolism.

� The major pathways above are uncoupled from one

another.

Nodes: Reactions

Arcs: Couplings

1

10

100

1 10 100

k

N(k)

1

10

100

1 10 100

k

N(k)

Helicobacter pylori Escherichia coli Saccharomyces cerevisiae

N(k) = 94.6 k -1.154

R2 = 0.707

N(k) = Number of reactions implying k reactions

k = Number of reactions

N(k) = 167.7 k -1.436

R2 = 0.848

N(k) = 323.7 k -1.624

R2 = 0.864

vj

vj’

vj’’

vj’’’

k = 3 rxns

1

10

100

1000

1 10 100

k

N(k)

Ex.

Scale-free Nature of Directional Coupling

Scale Free Architectures

Metabolite Centered Graphs

Reaction Flux Centered Graphs (Burgard, et. al., Genome Res., 2004)

(Jeong et al., Nature, 2000)(Wagner and Fell, Proc. R. Soc. Lond. B. Biol. Sci., 2001)

“Gaps” in metabolic networks

http://doegenomestolife.org

• Genes not annotated

• Genes not assigned function

• Incomplete curation of models

Bridging of gaps required !

Church et al., 2004, BMC Bioinformatics

Green and Karp, 2004, Bioinformatics

A B

Stoichiometric Metabolic reconstructions are

inherently incompleteChurch et al. BMC Bioinformatics (2004)

Green & Karp, Bioinformatics (2004)

Gaps in Metabolic Pathways

Missing biochemical

reactions

What are Gaps ? Causes for Gaps ?

• Gene not annotated

• Function not assigned

• Incomplete curation

Bridging Gaps in Metabolic pathways

STEP 1: Compile candidate reaction database

MetaCyc

KEGG

(~ 3,900 reactions)

G6P

F16P

F6P

T3P1T3P2

GLCPEP

PYRD6PGL RL5P

X5P

S7P

E4P F6P

T3P1

R5P

D6PGC

pts

pgi

pfkAB fbp

fba

tpiA

tktAB

zwf pgl gnd

rpiABrpe

tktAB

talB

Database Compilation

Database Curation

� Eliminate compounds with no chemical formulae from the compound list and

remove the corresponding reactions e.g., (1-4-beta-D-Glucan)(M+N)

� Link the compounds in individual stoichiometric models with those in the

compiled database and assign unique ids e.g., Urea MetaCyc compound id: 4619 CID:123

� Link the reactions in individual metabolic models with those in the

database and assign unique ids

Development of experimental and computational tools to evaluate metabolic flux

Labeled

Isotopes

GLX

PEP

G6P

F16P

F6P

13P2DG

3PG

2PG

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL

FUM

ACCOA

SUCC

ACT

AC

RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

ATP H+ QH2

NADHNADPH FADH

10.0

7.1 2.9 2.9 2.9

8.5

8.5

17.5

17.5

16.5

16.5

12.7

10.9

8.9

8.98

8

8

88

7.2

9.76.7

0.9

0.60.9

� Isotopomer analysis using GC/MS(Christensen & Neilsen, 1999;Fischer & Sauer, 2003)

� Isotopomer analysis using NMR spectra(Schmidt et.al., 1999)

� Computational models for flux elucidation(Zupke at al. , 1994;Wiechert & Graff, 1996; Wiechert et.al. 1996;Mollney et.al. 1999 )

� Optimization algorithms(Ghosh et.al.2004; Riascos et.al.2004; Phalakornkule et.al. 2001)

GC-MS

NMR

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETHAlac-S

Leucine

Valine

Pro-LGlu-L

Gln-L

Acglu

Arginine

Methionine

Proline

Glx

4pasp Asp-L

Asn-LAspsaHom-L

Phom

Threonine

Lysine

3php

Serine

Glycine

Cystenine

Pser-L

Acser

co2

Limitations� Employed metabolic models are of limited scope

(i.e. 30-50 rxns)� For genome-scale models (~1,000 rxns), measurables do not

always elucidate unique flux distributions

2dda7p

pphn

34hpp

Tyrosine

Phpyr

Phenyl

alaninePrpp

Pran

Tyrphtophan

Many relevant pathways

are absent…

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

PEP

G6P

F16P

F6P

13P2DG

3PDGL

2PDGL

PEP

PYR

OA

MAL

SUCCOACIT

AKG

GAP

ICIT

DHAP

GLC

PYRD6PGL RL5P

X5P

S7P

E4P F6P

GAP

R5P

D6PGC

FUM

ACCOA

SUCC

ACTP

AC

LAC ETH

Wet-lab experimental system (Keasling lab)

� E. coli strain that produces amorphadiene

(precursor to anti-malarial drug artemisinin)

� Measure labeling of 14 amino acids

� Average standard deviation of measurements is 3%

� Includes 353 reactions and 184 metabolites

� Yields 15,500 isotopes (Iik)

� Balances cofactors such as ATP, NADH, NADPH

� Weights measurements by their standard deviation

Isotopomer mapping model (Burgard & Van Dien)

-10

-8

-6

-4

-2

0

2

4

6

0 20 40 60 80 100 120 140 160

Calculated labeling matches well with those measured

� Average of 1.4% difference between calculated

labeling and measured labeling patterns

average percent deviation of

measurements (3 %)

observation #

percent difference

average difference between measurements and model predictions

(1.4 %)

Networks…

Signaling NetworksMetabolic Networks

http://doegenomestolife.org

Publications (fenske.che.psu.edu/faculty/cmaranas)Strain design

• Pharkya, P. and C.D. Maranas (2005), “An Optimization Framework for Identifying Reaction

Activation/Inhibition or Elimination Candidates for Overproduction in Microbial Systems”, submitted.

• Pharkya, P., A.P. Burgard and C.D. Maranas (2004), “OptStrain: A Computational Framework for Redesign

of Microbial Production Systems”, Genome Research, 14, 2367-2376.

• Pharkya, P., A.P. Burgard and C.D. Maranas (2003), “Exploring the Overproduction of Amino Acid Using the

Bilevel Optimization Framework OptKnock,” Biotechnology and Bioengineering, 84, 887-899.

• Burgard, A.P., P. Pharkya and C.D. Maranas (2003), “OptKnock: A Bilevel Programming Framework for

Identifying Gene Knockout Strategies for Microbial Strain Optimization,” Biotechnology and Bioengineering,

84, 647-657

Metabolite flux and concentration coupling analysis

• Nikolaev, E.V., A.P. Burgard, and C.D. Maranas (2005), "Elucidation and Structural Analysis of Conserved

Pools for Genome-Scale Metabolic Reconstructions," Biophysical Journal, 88, 37-49.

• Burgard, A.P.†, E.V. Nikolaev†, C.H. Schilling and C.D. Maranas (2004), “Flux Coupling Analysis of Genome-

scale Metabolic Network Reconstructions,” Genome Research, 14, 301-312

Inferring and Testing Metabolic Objective Functions

• Burgard, A.P. and C.D. Maranas (2002), “An Optimization-based Framework for Inferring and Testing

Hypothesized Metabolic Objective Functions,” Biotechnology and Bioengineering, 82, 670-677.

Minimal Reaction Set Identification

• Burgard, A.P., S. Vaidyaraman and C.D. Maranas (2001), “Minimal Reaction Sets for Escherichia coli

Metabolism under Different Growth Requirements and Uptake Environments,” Biotech. Progress,17, 791-797.

Pathway discovery and optimization

•Burgard, A.P. and C.D. Maranas (2001), “Probing the Performance Limits of the Escherichia coli Metabolic Network Subject to Gene Additions or Deletions,” Biotechnology and Bioengineering, 74, 364-375.

Signaling networks

• Dasika, M., Burgard, A.P. and C.D. Maranas (2006), "A computational framework for topological analysis and targeted disruption of signal transduction networks" Biophysical Journal, in press.

Acknowledgements

Graduate Researchers:

Post-Doctoral Researcher:

Collaborators:

Funding Sources:

Tony Burgard

Priti Pharkya

Madhukar Dasika

Vinay Satish Kumar

Evgeni Nikolaev

Patrick Suthers

Bernhard Palsson

Fred Blattner

National Science Foundation and Department of Energy

University of Wisconsin

University of California, San Diego

computational tools for the analysis of microbial ... · metabolic engineering is the analysis and...

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