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Optimality in Carbon Metabolism Ron Milo Department of Plant Sciences Weizmann Institute of Science 1 Arren Bar-Even Elad Noor 2 Uri Alon 3 What limits maximal growth rates? 4 What governs the efficiency of photosynthesis and carbon fixation? Why is Rubisco slow and non specific? What governs maximal growth rates? growth Design principles in photosynthesis – wavelengths used and saturation Synthetic carbon fixation pathways for higher efficiency 5 Are there simplifying principles to the structure of the central carbohydrate metabolism network? 6 An illustrative example: the Pentose Phosphate cycle Converts between 5 and 6 carbon sugars e.g Ribose-5P is used for making nucleotides e.g Fructose-6P is used for building the cell wall Was analyzed as an optimization problem (Meléndez-Hevia & Isodoro 1994) We use this as a starting point The Pentose Phosphate Pathway defined as a game Goal: 5 5 5 5 5 5 Turn 6 Pentoses into 5 Hexoses ? Rules: Transfer 2-3 carbons between two molecules Never leave a molecule with 1-2 carbons 6 6 6 6 6 TK Optimization function: Minimize the number of steps (simplicity) TA E. Meléndez-Hevia et al. (Journal of theoretical Biology 1994) Solution to Pentose Phosphate game in 7 steps Corresponds to natural pathway Doesn't explain why the rules exist Supports the idea of simplicity Are there simplifying principles to the structure of the central carbohydrate metabolism network? 10 We develop a method to find shortest path from A to B N W E S 11 But what are the “steps” allowed in biochemistry? ? ? ? ? 12 All possible reaction types are explored aldehyde dehydrogenase (CoA): pyruvate ↔ acetyl-CoA + CO2 isomerase (keto to enol): pyruvate ↔ enolpyruvate kinase (carboxyl): pyruvate ↔ pyruvate-P 13 Hatzimanikatis et al. (Bioinformatics 2005) EC numbers define 30 possible enzymatic reaction families 14 EC numbers define 30 possible enzymatic reaction families 15 EC rules were encoded into commands C C O C O O C C O C O O C 0 1 0 0 0 0 C 0 2 0 0 0 0 C 1 0 2 1 0 0 C 2 0 1 1 0 0 O 0 2 0 0 0 0 O 0 1 0 0 0 0 C 0 1 0 0 2 1 C 0 1 0 0 2 1 O 0 0 0 2 0 0 O 0 0 0 2 0 0 O 0 0 0 1 0 0 O 0 0 0 1 0 0 Hatzimanikatis et al. (Bioinformatics 2005) Optimization function finds minimal number of steps between any two metabolites The shortest path can be found efficiently using a customized BFS (breadth first search) 17 Are all pairs of metabolites connected by shortest possible paths? (as allowed by biochemistry rules) 18 Are all pairs of metabolites connected by shortest possible paths? (as allowed by biochemistry rules) • • Some pairs are connected by possible shortest paths Other pairs can be connected in less steps via shortcuts 19 Are all pairs of metabolites connected by shortest possible paths? (as allowed by biochemistry rules) • • Some pairs are connected by possible shortest paths Other pairs can be connected in less steps via shortcuts • • Cluster together pairs that connect via shortest paths Define these as minimality modules 20 minimality modules are defined to contain shortest paths A A B B C B C Existing reactions (in organism) D E E Only metabolites connected by shortest possible paths are contained in an minimality module C D D F A F E F Possible EC reactions (biochemistry) Minimality modules 21 Example: possible shortcut in glycolysis break it into modules GLU DHAP DHAP GAP GAP BPG EC 1.2 3PG 2PG PYR BPG GAP 3PG (EC 1.2) is biochemically feasible (exists in plants), but is not part of E. coli central metabolism 3PG 2PG Therefore glycolysis is not as short as possible and breaks down into minimality modules 22 Central carbon metabolism network breaks down to minimality modules Biomass precursors are key metabolites • Design principle: minimal number of enzymatic steps connecting every pair of consecutive precursors central carbon metabolism is a minimal walk between the 13 biomass precursors “Make things as simple as possible but not simpler” Can carbon fixation metabolism be “enhanced”? 26 Can we find “better” ways to achieve carbon fixation? 27 There are several alternative carbon fixation pathways 28 We systematically explore all possible synthetic carbon fixation pathways 29 Future directions – metabolic networks optimization and synthesis • Try to implement alternative carbon fixation in-vitro or in-vivo • “Test case”: can we convert E.coli to being an autotroph? • Couple synthetic carbon fixation to energy sources fuel production from sunlight/wind or at least learn something about the logic of evolution, and how: “evolution is smarter than you are” (Orgel’s law) 30 The number you need, with reference in just a minute BioNumbers – Useful biological numbers database Wiki-like, users edit and comment Over 3500 properties & 5000 users/month www.BioNumbers.org 31 32