Optimality in Carbon Metabolism

1. Optimalityin CarbonMetabolism Ron Milo Department of Plant SciencesWeizmann Institute of Science

2. Arren Bar-Even Elad Noor

3. Uri Alon

4. What limits maximal growth rates?

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

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

7. 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

8. 5 5 5 5 5 5 6 6 6 6 6 TK TA 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)

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

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

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

13. All possible reaction types are explored aldehyde dehydrogenase (CoA): pyruvate ↔ acetyl-CoA + CO2 isomerase (keto to enol): pyruvate ↔ enolpyruvate kinase (carboxyl): pyruvate ↔ pyruvate-P Hatzimanikatis et al. (Bioinformatics 2005)

14. EC numbers define 30 possible enzymatic reaction families

15. EC numbers define 30 possible enzymatic reaction families

16. EC rules were encoded into commands Hatzimanikatis et al. (Bioinformatics 2005)

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

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

22. DHAP GAP EC 1.2 BPG 3PG 2PG Example: possible shortcut in glycolysis break it into modules GLU DHAP GAP 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 BPG 3PG 2PG 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. Can carbon fixation metabolism be “enhanced”?

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

28. There are several alternative carbon fixation pathways

29. We systematically explore all possible synthetic carbon fixation pathways

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)

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