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Platzhalter für Bild, Bild auf Titelfolie hinter das Logo einsetzen
Veronique [email protected]
Exercise Systems & Synthetic Biotechnologyelementary flux mode analysis
- 2 -
Todays exercise
1. Update network construction
2. What to do with a metabolic network?
3. What are elementary flux modes?
4. What knowledge can we gain from elementary mode analysis?
5. Processing of flux modes in MATLAB®
- 3 -
Update network construction
Project:
5 groups • Corynebacterium glutamicum• Aspergillus niger• Saccharomyces cerevisiae• Escherichia coli• Pseudomonas putida
Network construction: Which pathways? Information required: stoichiometry, reversibility, localization, biomass synthesis requirements Biomass synthesis: protein composition and precursor demand
substrates:• Glucose• Xylose• Saccharose• Fructose• Glycerin• Lactate
- 4 -
What are elementary flux modes?
• Mode = a single independent pathway• Number of modes = the number of all possible, independent flux distributions in a steady state
- 5 -
What knowledge can we gain from elementary mode analysis?
00001111000100000000010
00011100000100000110010
0000323131031313131031323200000320
2121000000002121021212100000210
000021002121212100211100000210
313100031313231313200321100000310
1
1
1
1
1
1
Eno
AspC
on
SucC
oCon
AlaC
onG
luCo
nAs
pAAs
pCPp
s
Pyk
AceE
F
GltA
Pck
Ppc
Acn
Icl
Mas
Fum
Sdh
SucC
D
Gdh
IlvE/
AvtA
SucA
B
Icd
Mdh
169
1311
810
No.
Em
odes
nSucCoACon ni
110001121120023300000103
110011110010011100000001
100101121120023300010003
110000000011011100000102
100100000011011100010002
000021101111012200000203
000011110001000000000101
000010011110012200000102
000111000001000000100101
000101121110012200000012
000001121110112201000001
000100110000000001100000
001100000000000100001001
000100000001000001000011
000000000000000110000000
000000000001100000000000
Eno
AspC
onSu
cCoC
on
AlaC
onG
luCo
nAs
pAAs
pCPp
sPy
k
AceE
F
GltA
Pck
Ppc
Acn
Icl
Mas
Fum
Sdh
SucC
D
Gdh
IlvE/
AvtA
SucA
BIc
d
Mdh
16
9
13
11
8
10
No.
Em
odes
14
15
12
2
6
4
1
3
7
5
Example network of E. coli
• Most meaningful values are fluxes of an entire pathway that are normalized with respect to a flux of interest, e.g. substrate uptake
- 6 -
What knowledge can we gain from elementary mode analysis?
48.6 %
46.2 %
45.6 %
51.9 %
43.5 %
48.9 %
0 10 20 30 40 50 60 70 80 90 1000
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90
100
0 10 20 30 40 50 60 70 80 90 1000
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80
90
100
0 10 20 30 40 50 60 70 80 90 1000
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80
90
100
0 10 20 30 40 50 60 70 80 90 1000
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90
100
0 10 20 30 40 50 60 70 80 90 1000
10
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90
100
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
Met [%C]
Met [%C]
bio
mass [%
C]
Met [%C]
D
E
FC
bio
mass [%
C]
bio
mass [%
C]
Met [%C] Met [%C]
bio
mass [%
C]
bio
mass [%
C]
B
A
bio
mass [%
C]
Met [%C]
Elementarmoden-Analyse
methionine [%C]
- 7 -
Processing of flux modes in MATLAB®
http://www.csb.ethz.ch/tools/software/efmtool.html
rformulas={' --> A''A --> C''C --> P + D''P --> ''A --> B''B <==> C''B --> P'' <==> B''D --> '}; mnet=CalculateFluxModes(rformulas);
- 8 -
Command Window Workspace
Command
History
Work pad
MATLAB principles
- 9 -
What knowledge can we gain from elementary mode analysis?
00001111000100000000010
00011100000100000110010
0000323131031313131031323200000320
2121000000002121021212100000210
000021002121212100211100000210
313100031313231313200321100000310
1
1
1
1
1
1
Eno
AspC
on
SucC
oCon
AlaC
onG
luCo
nAs
pAAs
pCPp
s
Pyk
AceE
F
GltA
Pck
Ppc
Acn
Icl
Mas
Fum
Sdh
SucC
D
Gdh
IlvE/
AvtA
SucA
B
Icd
Mdh
169
1311
810
No.
Em
odes
nSucCoACon ni
110001121120023300000103
110011110010011100000001
100101121120023300010003
110000000011011100000102
100100000011011100010002
000021101111012200000203
000011110001000000000101
000010011110012200000102
000111000001000000100101
000101121110012200000012
000001121110112201000001
000100110000000001100000
001100000000000100001001
000100000001000001000011
000000000000000110000000
000000000001100000000000
Eno
AspC
onSu
cCoC
onAl
aCon
Glu
Con
AspA
AspC
Pps
Pyk
AceE
F
GltA
Pck
Ppc
Acn
Icl
Mas
Fum
Sdh
SucC
D
Gdh
IlvE/
AvtA
SucA
BIc
d
Mdh
16
9
13
11
8
10
No.
Em
odes
14
15
12
2
6
4
1
3
7
5
Example network of E. coli
for i=1:size(mnet.efms,2)EFMS(:,i)=mnet.efms(:,i)./mnet.efms($$,i);end for j=size(mnet.efms,2):-1:1 if isnan(EFMS($$,j))==1 EFMS(:,j)=[]; endend
Substrate reaction number
- 10 -
Processing of flux modes in MATLAB®
48.6 %
46.2 %
45.6 %
51.9 %
43.5 %
48.9 %
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
Met [%C]
Met [%C]
bio
mass [%
C]
Met [%C]
D
E
FC
bio
mass [%
C]
bio
mass [%
C]
Met [%C] Met [%C]
bio
mass [%
C]
bio
mass [%
C]
B
A
bio
mass [%
C]
Met [%C]
Elementarmoden-Analyse
methionine [%C]
plot(EFMS($$,:)*10/3,EFMS($$,:)*37.287/3,'s','MarkerFaceColor','k')
Product reaction number
Biomass reaction number
Carbon atoms product
Carbon atoms biomass
Carbon atoms substrate
- 11 -
Processing of flux modes in MATLAB®
indices=find(EFMS($$,:)==max(EFMS($$,:)));MaxYield=EFMS(:,indices);
Product reaction number