ehsan ullah, prof. soha hassoun department of computer science mark walker, prof. kyongbum lee...
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Predictably Profitable Paths in Metabolic Networks
Ehsan Ullah, Prof. Soha HassounDepartment of Computer Science
Mark Walker, Prof. Kyongbum LeeDepartment of Chemical and Biological Engineering
Tufts University
Engineered Pathway Interventions
(Atsumi et al., 2008) (Trinh et al., 2006) (Steen et al., 2010)
Embedding
new pathways
Removing
pathways
Improving
existing
pathways
2
Enumeration ◦Elementary Flux Mode
(Schuster et al., 2000) Graph traversal
◦ Dominant-Edge Pathway Algorithm(Ullah et al., 2009)
◦ Favorite Path Algorithm*
Pathway Analysis
s
b
R1
c
R2
e
R4
t
R6
R5
d
R3
Dominant-Edge 1st
3rd
2nd
4th
3
*Unpublished
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
4
R5
(4)
d
R3
(4)
R5
(4)
d
R3
(4)
A network in which any path from s to t can carry at minimum vp amount of fluxGp = G(V,E)
such that we ≥ vp
vp is obtained from the best flux-limiting step
Profitable Network
s
b
R1
(10)
c
R2
(6)
e
R4
(6)
t
R6
(10)
5
R5
[3 11]
d
R3
[7 12]
R5
[3 11]
d
R3
[7 12]
A path in the network having reactions with smallest variations in flux
Predictable Path
s
b
R1
[10 15]
c
R2
[8 14]
e
R4
[6 10]
t
R6
[9 18]
6
1. Identification of profitable networka) Assign the lower limit of each flux range as edge
weightb) Find flux limiting step using favorite path algorithmc) Prune all edges having weight less than the flux
liming step found in (b)
2. Identification of predictable path in profitable network
a) Assign the flux ranges as edge weightb) Use favorite path algorithm to find predictably
profitable path
Approach to Find Predictably Profitable Path
7
Escherichia coli◦ 62 Reactions◦ 51 Compounds
Liver Cell◦ 121 Reactions◦ 126 Compounds
Test Cases
8
Escherichia coli
9
Production of ethanol from glucose in anaerobic state
Flux data generated from Carlson, R., Scrienc, F. 2004
10
glucose
ethanol
Escherichia coli
PEP
Pyruvate
Flux-limiting step
11
Flux Limiting
Step
glucose
ethanol
Escherichia coli
PEP
Pyruvate
Flux-limiting step Profitable network
12
Profitable
Network
glucose
ethanol
Escherichia coli
PEP
Pyruvate
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
Pyruvate
Production of glutathione from glucose Flux data taken from HepG2 cultures* Two observed states
◦ Drug free state◦ Drug fed state (0.1mM of Troglitazone)
Liver Cell
*Unpublished results
14
Liver Cell
15
glucose
glu
cys
ala
gly
gluglutathione
akg
akg
lys
Liver Cell
Drug free state
16
glucose
glu
cys
ala
gly
gluglutathione
akg
akg
lys
Liver Cell
Drug free state◦ PPP, Alanine
biosynthesis, Lysine degradation
17
glucose
glu
cys
ala
gly
gluglutathione
akg
akg
lys
Liver Cell
Drug fed state
18
glucose
glu
cys
ala
gly
gluglutathione
akg
akg
lys
Liver Cell
Drug fed state◦ PPP, Cystine
biosynthesis
19
glucose
glu
cys
ala
gly
gluglutathione
akg
akg
lys
Efficient way of identifying target pathways for analyzing and engineering metabolic networks
Capable of handling variations in flux data Polynomial runtime
Conclusions
20