Department of Computer Science
This content discusses the production or overproduction of various products through synthetic pathways, such as bioplastics, biofuels, and drugs like antimalarial and anticancer agents. It covers topics like pathway identification, integration with host systems, probabilistic graph search algorithms, and metabolite connectivity analysis to optimize production processes. The use of enzymes, metabolic pathways, and connectivity within databases is explored to develop efficient and sustainable production methods.
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Presentation Transcript
Mona Yousofshahi, Prof. Soha Hassoun Department of Computer Science Prof. Kyongbum Lee Chemical & Biological Engineering Tufts University 1
Production or overproduction by synthetic pathways Bioplastics Organic plastics Derived from biomass sources instead of petroleum Biofuels Alcohol Diesel Drugs Antimalarial Anticancer 2
1. Pathway identification Identify a coherent set of enzyme-catalyzed reactions from existing databases 2. Integration with the host Ensure that the pathway minimally affects growth and other essential functions of the host 3
Probabilistic graph search algorithm based on metabolite connectivity Graph construction begins with a target metabolite and ends in a host Explicitly accounts for cofactors Search criteria is metabolite connectivity within the KEGG database: Number of reactions in which a metabolite participates More diversity in the search space Host Target metabolite Database 4
P(k) 3.48 k-2.04 104 Number of metabolites 103 2 10 101 0 10 100 101 102 Metabolite connectivity 5
A R1 R2 Metabolite connectivity: The number of reactions in which a metabolite participates B C D Weighting of a reaction: Minimum connectivity in a reaction is the bottleneck WR = minimum metabolite connectivity of the metabolites in reaction R (on the side opposite to the parent metabolite) 6 6
Target metabolite Construct the graph recursively starting from the target metabolite Select a random reaction based on metabolite connectivity Search termination Limit the number of reactions Perform flux balance analysis on the constructed pathways Host 7
Constructing the tree recursively, starting from the root and by adding all reactions to the tree Applying FBA to rank the constructed pathways 8
Genome-scale model of E. coli (iAF1260)(Feist, Henry et al. 2007) as a host Target metabolites Drug: Isopentenyl diphosphate Biofuels: Biodiesel, Fatty acid methyl ester Biofuel feedstock: Triacylglycerol Polymer: 1, 3-propanediol Compare three search algorithms based on yield results Probabilistic, random and exhaustive Yield is defined as the optimal flux of the target metabolite Fixed biomass flux 9
Probabilistic algorithm Probabilistic algorithm Random algorithm Random algorithm Exhaustive algorithm Exhaustive algorithm Metabolite name Number of pathways Max. Yield Number of pathways Max. Yield Number of pathways Max. Yield Isopentenyl diphosphate 1,3- Propanediol Biodiesel Fatty acid methyl ester 11 1.28 14 1.28 15 1.28 1 2.19 1 2.19 1 2.19 17 3.30 19 2.09 504 3.58 69 1.25 46 0.76 1121 1.25 Triacylglycerol 71 1.94 45 0.44 2949 1.97 Run times: Exhaustive search for maximum 10 reactions in a pathway: hours Probabilistic and random search: minutes 10
Identified pathway for isopentenyl diphosphate by probabilistic algorithm: Acetyl-CoA + Acetoacetate (S)-3-Hydroxy-3-methylglutaryl- CoA (R)-Mevalonate (R)-5-Phosphomevalonate (R)-5- Diphosphomevalonate Isopentenyl diphosphate (Martin, Piteral et al. 2003) 11
Triacylglycerol yield distribution 2 2 1.8 1.8 1.6 1.6 1.4 1.4 1.2 1.2 Yield Yield 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 500 1000 Number of pathways 1500 2000 2500 0 10 20 Number of pathways 30 40 50 60 Probabilistic search Exhaustive search 12
50 runs for each iteration 1.4 1.2 Maximum yield 1 0.8 0.6 maximum mean 0.4 200 400 Number of iterations 600 800 1000 Fatty acid methyl ester 13
50 runs for each iteration 1.4 1.2 Maximum yield 1 0.8 0.6 mean(Metabolite connectivity weighting) max(Metabolite connectivity weighting) mean(uniform weighting) max(uniform weighting) 0.4 200 400 Number of iterations 600 800 1000 Fatty acid methyl ester 14
PathMiner (McShan, Rao et al. 2003) exploring the biochemical state space using a heuristic search based on minimizing the cost of transformation Atom mapping (Blum, Kohlbacher 2008) Optstrain (Pharkya, Burgard et al. August 2004) building a framework for identifying stoichiometrically balanced pathways while maximizing product yield Requires database curation 15
A probabilistic graph search algorithm to identify synthetic pathways Using the notion of the metabolite connectivity Does not require any database curation Reproduce experimentally obtained pathways reported in the literature Future work: Integration with the host Gene interactions 16
