Ehsan Ullah Prof Soha Hassoun Department of Computer Science Mark Walker Prof Kyongbum Lee Department of Chemical and Biological Engineering Tufts University Engineered Pathway Interventions ID: 371497
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Slide1
Predictably Profitable Paths in Metabolic Networks
Ehsan Ullah, Prof. Soha HassounDepartment of Computer ScienceMark Walker, Prof. Kyongbum LeeDepartment of Chemical and Biological EngineeringTufts UniversitySlide2
Engineered Pathway Interventions
(Atsumi
et al
., 2008
) (
Trinh
et al
., 2006) (Steen et al., 2010)
Embedding
new pathways
Removing
pathways
Improving
existingpathways
2Slide3
Enumeration Elementary Flux Mode (Schuster
et al., 2000)Graph traversalDominant-Edge Pathway Algorithm(Ullah et al., 2009)Favorite Path Algorithm*Pathway Analysis
s
b
R
1
c
R
2
e
R
4
t
R
6
R
5
d
R
3
Dominant-Edge
1
st
3
rd
2
nd
4
th
3
*UnpublishedSlide4
Flux variations arise from different conditions
Given a metabolic network graph G = (V,E), source vertex s and destination vertex t and a flux range associated with each edge, find the predictably profitable path in the graph
Problem: Pathway Analysis in Presence of Flux Variations
4Slide5
R
5 (4)
d
R
3
(4)
R
5
(4)
d
R
3
(4)
A network in which any path from
s
to
t
can carry at minimum
v
p
amount of flux
G
p
= G(V,E)
such that
w
e
≥
v
p
v
p
is obtained from the best flux-limiting step
Profitable Network
s
b
R
1
(10)
c
R
2
(6)
e
R
4 (6)
t
R
6
(10)
5Slide6
R
5 [3 11]
d
R
3
[7 12
]
R
5
[3 11]
d
R
3
[7 12]
A path in the network having reactions with smallest variations in flux
Predictable Path
s
b
R
1
[10 15]
c
R
2
[8 14]
e
R
4
[6 10]
t
R
6
[9 18]
6Slide7
Identification of profitable network
Assign the lower limit of each flux range as edge weightFind flux limiting step using favorite path algorithmPrune all edges having weight less than the flux liming step found in (b)Identification of predictable path in profitable networkAssign the flux ranges as edge weight
Use favorite path algorithm to find predictably profitable path
Approach to Find
Predictably Profitable Path
7Slide8
Escherichia coli62 Reactions51 Compounds
Liver Cell121 Reactions126 CompoundsTest Cases8Slide9
Escherichia coli
9Production of ethanol from glucose in anaerobic stateFlux data
generated from
Carlson, R., Scrienc, F.
2004Slide10
10
glucose
ethanol
Escherichia
coli
PEP
PyruvateSlide11
Flux-limiting step
11
Flux Limiting Step
glucose
ethanol
Escherichia
coli
PEP
PyruvateSlide12
Flux-limiting step
Profitable network
12
Profitable Network
glucose
ethanol
Escherichia
coli
PEP
PyruvateSlide13
Flux-limiting step
Profitable network
Predictably profitable path
Glycolysis is more predictable than PPP
Matches maximal production path identified by (Trinh et al., 2006)
13
Glycolysis
glucose
ethanol
Escherichia
coli
PEP
PyruvateSlide14
Production of glutathione from glucoseFlux data taken from HepG2 cultures*Two observed states
Drug free stateDrug fed state (0.1mM of Troglitazone)Liver Cell*Unpublished results
14Slide15
Liver Cell
15
glucose
glu
cys
ala
gly
glu
glutathione
akg
akg
lysSlide16
Liver Cell
Drug free state
16
glucose
glu
cys
ala
gly
glu
glutathione
akg
akg
lysSlide17
Liver Cell
Drug free statePPP, Alanine biosynthesis, Lysine degradation
17
glucose
glu
cys
ala
gly
glu
glutathione
akg
akg
lysSlide18
Liver Cell
Drug fed state
18
glucose
glu
cys
ala
gly
glu
glutathione
akg
akg
lysSlide19
Liver Cell
Drug fed statePPP, Cystine biosynthesis
19
glucose
glu
cys
ala
gly
glu
glutathione
akg
akg
lysSlide20
Efficient way of identifying target pathways for analyzing and engineering metabolic networksCapable of handling variations in flux data
Polynomial runtimeConclusions20