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Metabolic networks

Metabolic networks. Guest lecture by Dr. Carlotta Martelli 26_10_2007. ( ... ) coa + nad + pyr --> accoa + co2 + nadh g1p + h2o --> glc-D + pi 2pg <==> h2o + pep g3p + nad +pi <==> 13dpg + h + nadh fdp <==> dhap + g3p fdp + h2o --> f6p + pi f6p <==> dha + g3p

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Metabolic networks

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  1. Metabolic networks Guest lecture by Dr. Carlotta Martelli 26_10_2007

  2. ( ... ) coa + nad + pyr --> accoa + co2 + nadh g1p + h2o --> glc-D + pi 2pg <==> h2o + pep g3p + nad +pi <==> 13dpg + h + nadh fdp <==> dhap + g3p fdp + h2o --> f6p + pi f6p <==> dha + g3p adpglc --> adp + glycogen + h atp + g1p + h --> adpglc + ppi glycogen + pi --> g1p atp + glc-D --> adp + g6p + h 2pg <==> 3pg atp + f6p --> adp + fdp + h g6p <==> f6p 3pg + atp <==> 13dpg + adp atp + h2o + pyr --> amp + (2) h + pep + pi adp + h + pep --> atp + pyr dhap <==> g3p ( ... ) thermodynamics biochemistry

  3. Thermodynamics range of flux feasibility Si≥0 Biochemistry network definition aim bim

  4. It' s a dynamic system !

  5. Optimization principles! • Realistic mathematical models turn to be very expensive: • Detailed rate equations • Reliable rate equations • Understanding the evolutionary layout: • Adaptation • Selection

  6. Stationary state: Flux Balance Analysis convex polytope

  7. Define of objective function Z : biomass production Maximize (or min.) Z, subject to constraints: Linear Z = Linear Programming technics (Simplex Algorithm)

  8. 1) Many other possible target functions exist!! It depends on your problem.

  9. 2) Be carefull with optimization! Not all the organisms live in your optimal state

  10. Von Neumann model reactions metabolites

  11. Problem definition growth: maximize subject to the linear constraints:

  12. Evolving system No mass balance Global optimization FBA vs VN • Stationary state condition • Mass balance • Local optimization * ? FBA =1 n=N/P

  13. Min-over (better than backtraking!) Random initial {Si} * ?   n=N/P

  14. Random vs Realmetabolic networks 1) Optimal growth rate 2) Number of solutions

  15. Conserved pools of metabolites

  16. Random E.coli

  17. VN FBA EXP

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