Systems biology is the quantitative and qualitative study of interactions among the components of biological systems, and how these interactions give rise to the function and behavior of that system

Omics: genome, transcriptome, proteome, metabolome

Genome-scale metabolic model and other cellular models

Simulation: metabolic flux analysis and dynamic simulation

Integrated analysis at a whole system level

X-omics data

Computational

experiments

Separation

Strain

improvement

Fermentation

Wet experiments

Regulatory

network

Metabolic

network

Signal

transduction

Virtual Cell

Medical

application

Industrial

application

White Biotechnology = Industrial Biotechnology

enabled through systems and synthetic biotechnology

Global Warming

Kyoto Protocol

Carbon tax

Environmental problems

Limited fossil resources

Sustainable system for

chemicals and materials

Current system for

chemicals and materials

Feedstock: renewable biomass

170 billion tons per year

only 6 billion tons are currently used

3.5% of this amount is used in non-food applications

170 billion tons of biomass

75% carbohydrates

20% lignin

5% others (oils, proteins, etc.)

Biorefinery BioEnergy

BioFuels

BioMaterials

BioChemicals

White Biotechnology

Carboxylic acids

Dicarboxylic acids

Alchohols

Diols

Diamines

Aromatics

Amino acids

and many others

+ Unnatural chemicals

Roche @ ExPASy

Ethanol

Diesel

Butanol/Isobutanol

Alkanes

Isoprenoids

Hydorxycarboxylic esters

Polysaccharides

Poly (amino acids)

Polyhydroxyalkanoates

Unnatural polymers

In vitro polymerization of monomers

Organic/inorganic hybrids

O*

CH

2

*

OHR

n

m

Enhanced production of metabolites and other

biologicals that are already produced by host organism

Production of modified or new metabolites and other biologicals that are new to the host organism

Broadening the substrate utilization range

Designing improved or new metabolic pathways

for degradation of various chemicals, especially xenobiotics

Modification of cell properties that facilitate bioprocessing(fermentation and product recovery)

Purposeful modification of

metabolic network to achieve… (after Jay Bailey)

S.Y.Lee

Receptors

Transcription

Factors

Ligands

Cytoplasm

Translation

processing

Intracellular

SignalingGenetic

Regulatory

NetworkmRNA

Protein

Glucose

Metabolic network

ATP

Acetate

Lactate

degradation

Complex Network of Network of Network

Networkn

Lee et al.

(2005) Trends

Biotechnol.,

23: 349-358

An integrated computational environment linking all data with theory, modeling, simulation, and experiments to help understand the biological system as a whole

Mathematical

Modeling, Simulation and Optimization

Database

for Integrating

and Sharing Data

Visualization

of Models and Simulation

Results

Dr. H. Yoon Ms. J. Chu

Mathematical modeling, simulation and optimizationPseudo-steady state simulation

Dynamic simulation

Hybrid simulation

Final release

Visualization of models and simulation resultsData management

Visualization of genome-scale metabolic networks

Visualization of regulatory networks

Visualization of two combined networks using 3D space

User commands

Final release

Automatic

importing

parser

DB Server

User

Public database

Integration of biochemical data

Analysis of metabolic networks

Thorough comparison of different DBs

Standardization

IntegrationenzymeSynomes

FK1,I2 ECNum

Name NameOrder

compoundIndex

PK compoundID

formula casNumber

geneIndex

PK abbreviation

Name

compoundSynomes

compoundName

FK1 compoundID nameOrder

DBLink

FK1 ECNum

linkType linkCode Description

enzymeGenes

FK1 ECNum

FK2,I2 abbreviation gene

enzymeIndex

PK ECNum

I1 name comment reversible

enzymePathways

path

FK1 ECNum pathName

enzymeReactions

FK2 ECNum

FK1 compoundID reactionType reactionOrder stoiNum

Global

Schema

Construction of reaction models

Estimation of flux distributions

Validation of metabolic networks

Metabolic behavior in response to genetic and/or

environmental modifications

Visualization of metabolic networks

DB Query

Modeling

Tool

MFA

Visualization

Dynamic

Simulation

Other software programs

MFAML

Biochemical Reactions

Stoichiometry

Cofactors

Reversibility

Biomass

ATP requirement

RE

Boundary

B C ERA r1 r2

r3

D

r4 r5

r6

r7

RD

RC

A_ex E_ex

C_exin out

A

Steady state Mass Balance

A:+RA-r1=0

B:+r1-r4-r2+r3=0

C:+r2-r5-r6-RC=0

D:+r5+r4-r3-r7-RD=0

E:+r6+r7-RE=0

D_ex

V

0

0

0

0

0

=

S

1 -1 0 0 0 0 0 0 0 0 0

0 1 -1 1 -1 0 0 0 0 0 0

0 0 1 0 0 -1 -1 0 -1 0 0

0 0 0 -1 1 1 0 -1 0 -1 0

0 0 0 0 0 0 1 1 0 0 -1

S•v=0 RA

r1

r2

r3

r4

r5

r6

r7

RC

RD

RE

S = stoichiometric matrix with K metabolites and J reactions [KJ]

v = flux vector [J1]

11 rxns

9 meta’sdX/dt=S•v

dA/dt = RA-r1

Transport flux

Stochiometric matrix(constraints)

Optimization Experimentalparameters

Graphical visualization

Analysis

Input

Output

Metabolic reactions

Objectivefunction

Integrated computational environment for modeling and simulation of the genome-scale metabolic model

User

DB Server

Model

Manager

Database Manager

ADO. Net

G

U

I

File Manager

LibSBMLLibMFAML

XML

Analysis

Manager

Microsoft Solver

Foundation

OML Builder

Network

Manager

Network Editor

Food and animal feed

Medical care

Sports

Health care Beauty care

Amino acid

Val, Leu, Ile : effective in hepatic failure

Glu : antiulcer drug

Arg : immune-enhancing effect

Val, Leu, Ile : Muscle building, increase of stamina, recovery from fatigue

The skin moisturizing effect

Collagen composition

Care of damaged cuticles

Efficient burning of body fat

Platform chemicals

acetohydroxy acid

Synthase I, II, III

acetohydroxy acid

isomeroreductase

dihydroxy acid

dehydratase

branched chain

amino acid

aminotransferase

ilvGMEDAx

Jin Hwan Park

ilvGMEDAPtac

Ptac

41th G A

50th C T

thr dehydratase

2-isopropylmalate

synthase3-methyl-2-oxobutanoate

hydroxymethyltransferase

Auxotrophic

2 mM Leu

2 mM Ile

1.5 mM pantoth

X X

XBase strain

Val {W3110(attilvG::ptac,

attilvB::ptac, ilvHA41G, C50T, ΔlacI,

ΔilvA, ΔleuA, ΔpanB)}

harboring pKKilvBN

DlacI

▶ 1.31 g/L L-valine

by batch fermentation

Val : W3110(attilvG::ptac, attilvB::ptac, ilvHA41G, C50T, ΔlacI, ΔilvA, ΔleuA, ΔpanB), pKKilvBN

Control : W3110 (ΔlacI, pKK223-3)

Medium NM1, Glucose (20 g/L), L-leucine (2mM), L-isoleucine (2mM), D-pantothenate (1.5μM)

Condition 31C, pH 6.0

Time (hr)

0 5 10 15 20

Cel

l gro

wth

(O

D6

00)

0

5

10

15

20

25

30

L-v

ali

ne

(g/l

iter

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Jin Hwan Park

gene enzyme chip data

ilvB acetohydroxy acid

synthase

isoenzyme I

54.46

ilvN acetohydroxy acid

synthase

isoenzyme I

32.50

ilvC acetohydroxy acid

isomeroreductase3.74

ilvD dihydroxy acid

dehydratase4.28

ilvE branched chain

amino acid

aminotransferase

1.32

Relative expression level of L-valine

biosynthetic pathway genes

Co-amplification of the ilvCED genes in pKKilvBN

Increased L-valine production (1.31 3.43 g/L)

Val+pKKilvBN Val+pKKilvBNCED

L-v

ali

ne

(g/L

)

0

1

2

3

4

36.2% decrease (3.732.38 g/L) in L-valine production with lrp deletion

L-v

ali

ne

con

cen

tra

tio

n (

g/L

)

0

1

2

3

4

5

Val+

pK

BR

ilvB

NC

ED

+p

Trc

18

4

Va

l+p

KB

Ril

vB

NC

ED

Va

l+p

KB

Ril

vB

NC

ED

+p

Trc

18

4lr

p

Va

l (Δ

lrp

)+

pK

BR

ilvB

NC

ED

Enhanced production of L-valine by overexpression of lrp

pTrc184lrp

CM(R)

lrp5S

rrnBT1T2 pTrc

P15A ORI

21.6% increase (3.574.34 g/L) in L-valine production with lrp overexpression

Lrp plays an important role in L-valine production

Leucine responsive protein: Lrp Downregulated (ratio: 0.52)

Identification of the E. coli gene homologous to Corynebacterium glutamicum brnF

L-valine exporter in E. coli ?

YgaZH: hypothetical protein

Downregulated to 0.61-0.75 during Val production

Exporter

Engineering

L-v

ali

ne

(g/L

)

0

2

4

6

8113% (3.577.61 g/L)

Synergistic effect of ygaZH and lrp on L-valine production

pTrc184ygaZHlrp

CM(R)

ygaZH5S

rrnBT1T2

lrp

Ptrc

P15A ORI

21.6%

47.1%V

al+

pK

BR

ilvB

N

CE

D+

pT

rc1

84

Va

l+p

KB

Ril

vB

NC

ED

+p

Trc

18

4lr

p

Va

l+p

KB

Ril

vB

NC

ED

+p

Trc

18

4yg

aZ

H

Va

l+p

KB

Ril

vB

NC

ED

+p

Trc

18

4yg

aZ

Hlr

p

pTrc184ygaZH

CM(R)

ygaZH5S

rrnBT1T2Ptrc

P15A ORI

pTrc184lrp

CM(R)

lrp5S

rrnBT1T2 pTrc

P15A ORI

Any more targets to engineer?

-Removed feedback inhibition

-Removed transcriptional attenuation control

-Removed major competing metabolic pathways

-Stepwise improvement based on transcriptome analysis

Metabolic engineering of E. coli for L-Valine production(flask culture for 48 h @ 31C; 50 g/L glucose)

Val+pKKilvBN

1.31 g/L

Exporter

engineering

Global regulator

engineering

Amplification of

L-valine production

pathways

GLC

G6P

F6P

GAP

GBP

PEP PYR

OAA

RL5P

X5P

CIT

ICIT

AKG

SUCOASUC

FUM

MAL

ACA ACP

GLO

ACL KIV Val

Leu

Pan

ilvGM

ilvIH

ilvBNDHV

ilvC ilvD ilvE

ptsG

pgi

pykAF aceEF

gltA

icdA

sucABsucCDsdhACD

fumA

mdh

aceA

aceB

pta ackA

DHAP

6PGL 6PGC

R5P

S7P GAP

E4P F6P

ppc

fba

tpiA

gapA

eno

zwf gndpgl

tktAB

Ace

AlaavtA

rpiA

talAB

tktAB

rpe

pfkA

pgk

gpmA

FBP

Ptac

Ptac

41th GA 50th CT

GLC

G6P

F6P

GAP

GBP

PEP PYR

OAA

RL5P

X5P

CIT

ICIT

AKG

SUCOASUC

FUM

MAL

ACA ACP

GLO

ACL KIV Val

Leu

Pan

ilvGM

ilvIH

ilvBNDHV

ilvC ilvD ilvE

ptsG

pgi

pykAF aceEF

gltA

icdA

sucABsucCDsdhACD

fumA

mdh

aceA

aceB

pta ackA

DHAP

6PGL 6PGC

R5P

S7P GAP

E4P F6P

ppc

fba

tpiA

gapA

eno

zwf gndpgl

tktAB

Ace

AlaavtA

rpiA

talAB

tktAB

rpe

pfkA

pgk

gpmA

FBP

Ptac

Ptac

41th GA 50th CT

Val+pKBRilvBNCED+pTrc184ygaZHlrp

7.61 g/L

Ptrc

pTrc184ygaZHlrp (6.7kb)ori

NcoI KpnI

ygaZH

CmR

lrp

BamHI

PstI

Roche @ ExPASy

Combinatorial gene knockout

1000C2 = 499,500

1000C3 = 166,167,000

1000C4 = 41,417,124,750...

Genome-scale metabolic flux analysis

to identify new gene-deletion targets

Tae Yong Kim

Single knockout Double knockout

In silico triple gene knockout

Tae Yong Kim

Acetyl-CoA

malate

ED and PPP

aceF/E/lpdA pyruvate dehydrogenase

pfkA/B phosphofructokinase

mdh malate dehydrogenase

Flux redirection

by triple knock-out

Selection of genes

for triple knock-out

mutation

37.75 g L-valine

from 100 g glucose

pTrc184ygaZH

CM(R)

ygaZH5S

rrnBT1T2Ptrc

P15A ORI

Glucose

G6P

F6P

G3P

PGA

PEP PYR

OAA

RL5P

X5P

CIT

ICIT

AKG

SUCOASSUC

FUM

MAL

ACA Ace-P

GLO

Aclac Kival Val

Leu

Pan

ilvBN, ilvGM, ilvIH

DhivalilvC ilvD ilvE

ptsG

pgi

pykAF aceEF

gltA

acnAB

icdA

sucABsucCDsdhABCD

fumA

mdh

aceA

aceB aceK

pta ackA

DHAP

D6PGL D6PGC

R5P

S7P GAP

E4P F6P

ppc

fbatpiA

gapA

eno

zwf gndpgl

tktAB

Ace

rpiA

talAB

tktAB

rpe

leuA

panB

F16P

pfkAB

L-valine

overproducer

L-Threonine

Ketobutyrate

Acetolactate

Ketoisovalerate

L-ValineL-Leucine Pantothenate

Dihydroxyisovalerate

Pyruvate

Pyruvate

Acetohydroxybutyrate

L-Isoleucine

ilvA

leuA panB

ilvBN ilvGM ilvIH

ilvC

ilvD

ilvE

ilvC

ilvD

ilvE

ilvBN, ilvGM, ilvIH

leuCD

leuB

ilvE tyrB

panE

panC

Systems metabolic engineering of E. coli for L-valine production

pTrc184lrp

CM(R)

lrp5S

rrnBT1T2 pTrc

P15A ORI

Replacement of

attenuator with tac

promoter

Removal of feedback inhibition

Transcriptome

analysis

In silico knock-out

simulation

E. coli W3110

Base strain

Lrp

+

+_

Power of

Systems

Metabolic

Engineering

regulation

threonine

efflux

central

carbon flux

degradation

branched

pathway

biosynthetic

pathway

byproduct

Kwang Ho Lee

Time (h)

0 2 4 6 8 10 12 14

Glu

co

se

(g

/l)

0

2

4

6

8

10

Bio

ma

ss

(g

/l)

0

1

2

3

4

5

6

7

L-t

hre

on

ine

, o

rga

nic

ac

ids

(g

/l)

0.0

0.5

1.0

1.5

2.0

TH07 (pBRThrABC, pACYC-ppc)

(ppc amplified strain)

PPC

sp

ecif

ic a

cti

vit

y o

f P

PC

(U

/mg

pro

tein

)

0

50

100

150

200

250

300

350

ICL

sp

ecif

ic a

cti

vit

y o

f IC

L (

U/m

g p

rote

in)

0

2

4

6

8

10

12

14

16

18

20

control control

ppc

-amplified

ppc

-deleted

32.0

298.4

1.72.2

5.3

ppc

-amplifiedppc

-deleted

Enzyme assay

9.3 fold ↑

Time (h)

0 2 4 6 8 10 12 14

Glu

co

se

(g

/l)

0

2

4

6

8

10

Bio

ma

ss

(g

/l)

0

1

2

3

4

5

6

7

L-t

hre

on

ine

, o

rga

nic

ac

ids

(g

/l)

0.0

0.5

1.0

1.5

2.0

1.46g/l

TH07 (pBRThrABC, pACYC177)

(control strain)

glucose

Biomass

L-Thr

pyruvate

acetate

succinate

1.21g/l

Time (h)

0 5 10 15 20

Glu

co

se

(g

/l)

0

2

4

6

8

10

Bio

ma

ss

(g

/l)

0

1

2

3

4

5

6

7

L-t

hre

on

ine

, o

rga

nic

ac

ids

(g

/l)

0.0

0.5

1.0

1.5

2.0

TH11C (pBRThrABC, pACYC177)

(ppc deleted strain)

0.19g/l

the flux of PPC is presented in the < 4 mmol/gDCW/h

in aerobic condition (Fisher et al., Anal. Chem. 2004)

biomass

Genome-scale metabolic model

E. coli MBEL979

Tae Yong Kim

Hyun Uk Kim

aspartate

threonine

homoserine phosphate

homoserinemethionine

lysineaspartate semialdehyde

aspartyl phosphate

(aspartokinase I, II, III)

(threonine synthase)

(homoserine kinase)

(homoserine dehydrogenase I, II)

(aspartate semialdehyde dehydrogenase)

thrA metL lysC

asd

thrAmetL

thrC

thrB

isoleucine

thrABC

Replacement of attenuator

with tac promoter

Removal of feedback

inhibition

1034th CT

1055th CT

Thr

GLC

G6P

F6P

GAP

GBP

PEP PYR

OAA

RL5P

X5P

CIT

ICIT

AKG

SUCOASUC

FUM

MAL

ACA ACP

GLO

ptsG

pgi

pykAF aceEF

gltA

icdA

sucABsucCDsdhACD

fumA

mdh

aceA

aceB

pta ackA

DHAP

6PGL 6PGC

R5P

S7P GAP

E4P F6P

ppc

fba

tpiA

gapA

eno

zwf gndpgl

tktAB

Ace

rpiA

talAB

tktAB

rpe

pfkA

pgk

gpmA

FBP

ppc

aceBA

iclR

_

rhtABC

acs

tdcC

ΔlacI

tdhglycine

isoleucineilvA

290th CT

Transcriptome analysis & in silico flux

response analysisBase strainPtrc

acs

Ptac

Systems metabolic engineering of E. coli for the production of L-threonine

THE28C

Time (h)

0 5 10 15 20 25 30 35 40 45 50 55

Glu

co

se

, L

-th

reo

nin

e (

g/l

), c

ell

gro

wth

(O

D6

00)

0

10

20

30

40

50

60

70

80

90

100

L-a

ce

tate

, L

-la

cta

te (

g/l

)0

2

4

6

8

10

12

14

16

18

20

OD600

Glucose

L-threonine

L-acetate

L-lactate

82.4 g/l

L-Thr Kwang Ho Lee

Park et al. 2008. Trends

Biotechnol 26: 404-412

Moving towards

genome-scale synthetic biology

“systems metabolic engineering”

Specialty

chemicals

Bioenergy

Ethanol

H2

DieselVitamins

Amino

acidsCommodity

chemicals

Biopolymers

Plastics

Elastomers

Fermentation and

midstream

processing

Biosepration and

downstream

processing

Metabolic engineering/

Cellular engineering

BiotechnologyWhite

Chiral

chemicals

Systems biotechnology

Systems metabolic engineering

Sustainable system for

chemicals, fuels and materials

Green Growth

Red Biotechnology

enabled through systems biotechnology

Multiple

enzymes?

X

X

X

X

X

X

XX X

X

X

X

X

X

X

X

X

X

X

X

X

X

Single gene deletion tests

reaction flux (j) = 0

if biomass = 0

then, reaction flux (j) = primary drug target candidate

R1

C

R2 +1

-1

-2R3

XX

Kim et al. PNAS 104:13638 (Aug 2007)

In collaboration with Prof. H. Jeong, KAIST

From gene knockout to

metabolite knockout ?

Tae Yong Kim

Hyun Uk Kim

Tae Yong Kim

Hyun Uk Kim

Systems biology allows systems-level understanding of cellular and metabolic characteristics

Systems biotechnology allows development of bioprocesses that are much more efficient than those developed by traditional strategies

Systems biological analysis of cellular network allows identification of new targets for treatment

KAIST-Microsoft Collaborative Research Project on SB.NET provides a computational platform for systems-level studies

Ministry of Education, Science & TechnologyKorean Systems Biology Research

Genome-based Integrated Bioprocess Development

BK21 Program

WCU Program

SB.NET teamDr. Hongseok Yoon Dr. Tae Yong Kim Dr. Jin Hwan Park Mr. Hyun Uk Kim

Mr. Jong Myung Park Mr. Seung Bum Sohn Ms. Jung Suk Chu Mr. Jung Ho Park

http://mbel.kaist.ac.kr Thank you !

© 2009 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the

current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information

provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.