Lee metbolic eng synthetic bio BME 162 GuestLecture Apr2_2012

In the example right the weights for the reac0ons are

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Unformatted text preview: d from a list of candidate reac0ons in the KEGG database that involve the target metabolite as a main product •  Selec0on occurs probabilis0cally based on a weigh0ng scheme determined by the connec0vity of the candidate reac0ons’ metabolites –  Add the selected reac0on to the tree (represented by an edge). –  Edge expands the tree by amaching new nodes represen0ng the reactant metabolites and cofactors of the selected reac0on. •  Each of these nodes is a new root for the recursion, unless the corresponding metabolite or cofactor is already present in the host organism or has been added to the tree before. 4/2/12 BME 162 Guest Lecture 31 Connec0vity ­based Weigh0ng Scheme •  Metabolite connec0vity follows a power law distribu0on •  Should reac0ons be selected to enrich/deplete hub metabolites in the pathway? –  In the example (right), the weights for the reac0ons are: WR1 = min (4, 3) = 3, WR2 = 2 and WR3 = min (4, 1) = 1. –  High ­connec0vity bias: set probabili0es of selec0ng reac0ons 1, 2 and 3 to 0.5, 0.33 and 0.17, respec0vely 4/2/12 BME 162 Guest Lecture 4 10 3 10 2 10 Frequency 10 1 P(k) = 3.48k-2.04 R2 = 0 .92 0 10 0 10 1 10 Metabolite connectivity 10 2 A R1 R2 B C D R3 B E Metabolites B through E have the following degree connectivity values: deg(B) = 4, deg (C) = 3, deg(D) = 2 and deg(E) = 1 32 Evalua0on •  •  •  •  Maximum yield calcula0ons by Flux Balance Analysis Host: genome ­scale model of E. coli metabolism (iAF1260) FBA objec0ve: maximize flux forming target product FBA constraints –  Biomass flux needs to exceed 80% of the wild ­type biomass flux –  Stochiometric constraints of host network –  Stoichiometric constraints of reac0ons in the “synthe0c” pathway •  Algorithm may iden0fy...
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