Ehsan Ullah, Prof. Soha Hassoun Department of Computer Science Mark Walker, Prof. Kyongbum Lee Department of Chemical and Biological Engineering Tufts University

(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al., 2009) ◦ Favorite Path Algorithm* s b R1R1 c R2R2 e R4R4 t R6R6 R5R5 d R3R3 Dominant- Edge 1 st 3 rd 2 nd 4 th 3 *Unpublished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 4

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 s b R 1 (10) c R 2 (6) e R 4 (6) t R 6 (10) 5

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 s b R 1 [10 15] c R 2 [8 14] e R 4 [6 10] t R 6 [9 18] 6

1. Identification of profitable network a)Assign the lower limit of each flux range as edge weight b)Find flux limiting step using favorite path algorithm c)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 weight b)Use favorite path algorithm to find predictably profitable path 7

 Escherichia coli ◦ 62 Reactions ◦ 51 Compounds  Liver Cell ◦ 121 Reactions ◦ 126 Compounds 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) *Unpublished results 14

15 glucose glu cys ala gly gluglutathione akg lys

 Drug free state 16 glucose glu cys ala gly gluglutathione akg lys

 Drug free state ◦ PPP, Alanine biosynthesis, Lysine degradation 17 glucose glu cys ala gly gluglutathione akg lys

 Drug fed state 18 glucose glu cys ala gly gluglutathione akg lys

 Drug fed state ◦ PPP, Cystine biosynthesis 19 glucose glu cys ala gly gluglutathione akg lys

 Efficient way of identifying target pathways for analyzing and engineering metabolic networks  Capable of handling variations in flux data  Polynomial runtime 20