1. 1 Optimality in Carbon Metabolism Ron Milo Department of Plant Sciences Weizmann Institute of Science

2. 2 Elad Noor Arren Bar-Even

3. 3 Uri Alon

4. 4 What limits maximal growth rates?

5. 5 growth Why is Rubisco slow and non specific? What governs maximal growth rates? Design principles in photosynthesis – wavelengths used and saturation Synthetic carbon fixation pathways for higher efficiency What governs the efficiency of photosynthesis and carbon fixation?

6. 6 Are there simplifying principles to the structure of the central carbohydrate metabolism network?

7. 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 An illustrative example: the Pentose Phosphate cycle

8. The Pentose Phosphate Pathway defined as a game Goal: Turn 6 Pentoses into 5 Hexoses Rules: Transfer 2-3 carbons between two molecules Never leave a molecule with 1-2 carbons Optimization function: Minimize the number of steps (simplicity) ? E. Meléndez-Hevia et al. (Journal of theoretical Biology 1994) TK TA 555555 66666

9. Solution to Pentose Phosphate game in 7 steps Corresponds to natural pathway Doesn't explain why the rules exist Supports the idea of simplicity

10. 10 Are there simplifying principles to the structure of the central carbohydrate metabolism network?

11. 11 We develop a method to find shortest path from A to B N S WE

12. 12 But what are the “steps” allowed in biochemistry? ? ? ??

13. 13 All possible reaction types are explored aldehyde dehydrogenase (CoA): pyruvate ↔ acetyl-CoA + CO 2 isomerase (keto to enol): pyruvate ↔ enolpyruvate kinase (carboxyl): pyruvate ↔ pyruvate-P Hatzimanikatis et al. (Bioinformatics 2005)

14. 14 EC numbers define 30 possible enzymatic reaction families

15. 15 EC numbers define 30 possible enzymatic reaction families

16. EC rules were encoded into commands CCOCOO C010000 C102100 O020000 C010021 O000200 O000100 CCOCOO C020000 C201100 O010000 C010021 O000200 O000100 Hatzimanikatis et al. (Bioinformatics 2005)

17. 17 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)

18. 18 Are all pairs of metabolites connected by shortest possible paths? (as allowed by biochemistry rules)

19. 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

20. 20 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

21. 21 minimality modules are defined to contain shortest paths Only metabolites connected by shortest possible paths are contained in an minimality module Existing reactions (in organism) Possible EC reactions (biochemistry) Minimality modules A B C D E F A B C D E F A B C D E F

22. 22 GAP  3PG (EC 1.2) is biochemically feasible (exists in plants), but is not part of E. coli central metabolism Therefore glycolysis is not as short as possible and breaks down into minimality modules GAP BPG 3PG DHAP 2PG EC 1.2 Example: possible shortcut in glycolysis break it into modules GAP BPG 3PG DHAP 2PG GLU PYR

23. Central carbon metabolism network breaks down to minimality modules

24. Biomass precursors are key metabolites

25. 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”

26. 26 Can carbon fixation metabolism be “enhanced”?

27. 27 Can we find “better” ways to achieve carbon fixation?

28. 28 There are several alternative carbon fixation pathways

29. 29 We systematically explore all possible synthetic carbon fixation pathways

30. 30 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)

31. 31 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

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