Download Metabolic networks

Metabolic networks
Guest lecture by Dr. Carlotta Martelli
26_10_2007
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coa + nad + pyr --> accoa + co2 + nadh
g1p + h2o --> glc-D + pi
thermodynamics
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
biochemistry
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Thermodynamics
Biochemistry
range of flux feasibility
Si≥0
network definition
aim bim
It' s a dynamic system !
Optimization principles!
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Realistic mathematical models turn to be very
expensive:
– Detailed rate equations
– Reliable rate equations
Understanding the evolutionary layout:
– Adaptation
– Selection
Stationary state:
Flux Balance Analysis
convex
polytope
Define of objective function Z : biomass production
Maximize (or min.) Z, subject to constraints:
Linear Z = Linear Programming technics
(Simplex Algorithm)
1)
Many other possible target functions exist!!
It depends on your problem.
2)
Be carefull with optimization!
Not all the organisms live in your optimal state
Von Neumann model
reactions
metabolites
Problem definition
growth:
maximize subject to the linear constraints:
FBA
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Stationary state
condition
Mass balance
Local optimization
* ?
=1
vs
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VN
Evolving system
No mass balance
Global optimization
FBA
n=N/P
Min-over (better than backtraking!)
Random initial {Si}
* ?


n=N/P
Random vs Real
metabolic networks
1) Optimal growth rate
2) Number of solutions
Conserved pools of metabolites
Random
E.coli
VN
FBA
EXP