Scale-rich metabolic networks

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Scale-rich metabolic networks

• Wang Chao
• Nov 9, 2004

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Reiko Tanaka, John Doyle, Scale-rich metabolic
networks background and introduction,
arXivq-bio.MN/0410009 v1 7 Oct 2004
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S-matrix and S-graph

• Example
• two reactions
• S-matrix is given by
• S-graph

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Precursor and Carrier Metabolites?13 precursor
metabolites?list of carrier metabolitesPhosphate
group transfer ATP/ADP/AMPHydrogen transfer
NADH/NAD, NADPH/NADP, FADH/FAD,

OTHIO/RTHIO, MK/MKH2Amino group transfer
AKG/GLUAcetyl group transfer
ACCOA/COAOne carbon unit transfer
THF/METTHF/FTHF/MTHF/METHFOthers
CO2,NH3,O2,H2O2, H2CO3 H2S, H2SO3,
NO2
Sulfate, Acetate, H, Phosphate, Pyrophosphate,

ACP
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HOT bowtie structure
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S-matrix for H.Pylori metabolism of with 325
metabolites and 315 reactions
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Coefficient of Variation CV

• Coefficients of variation of metabolite node
  degree distribution

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Rank of metabolite node degree for metabolic
networks of H.Pylori
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Scale-rich

• The overall high variability and thus apparent
  power-law is created by a mixture of the high
  degree of the sum of degrees of a few shared
  carriers with the many low degree of other
  metabolites unique to each reaction module
• The entire network consists of widely different
  scales

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• A simple model to show how shared common carriers
  make high variability at the full system level
  despite low variability within modules
• Far from self-similar or scale-free, these highly
  structured, scale-rich, and self-dissimilar
  features are the intrinsic features of metabolic
  networks
• The high variability is thus due to the highly
  optimized and structured protocol that uses
  common carriers and precursor metabolites, and
  power laws are simply the natural null
  statistical hypothesis for such high variability
  data

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The End