v0

MGlcout = MGlcin

v1

v1

MATP + MGlcin = MGlc6P

v10

M2PGri = M3PGri

v11

M2PGri = MPEP

v12

v12

MPEP = MPyr + MATP

v13

MPyr = MNAD + MLac

v14

v14

MPyr = MNADP + MLac

v15

MATP = MPi

v16

∅ = MATP + MAMP

v17

v17

MGlc6P + MNADP = M6PGlcA

v18

v18

MNADP + M6PGlcA = MRul5P

v19

v19

∅ = {2.0}MGSH + MNADP

v2

MFru6P = MGlc6P

v20

{2.0}MGSH = ∅

v21

MRul5P = MXul5P

v22

MRul5P = MRib5P

v23

MXul5P + MRib5P = MSed7P + MGraP

v24

MSed7P + MGraP = MFru6P + ME4P

v25

MATP + MRib5P = MAMP

v26

MXul5P + ME4P = MFru6P + MGraP

v27

MPiex = MPi

v28

MLacex = MLac

v29

MPyrex = MPyr

v3

v3

MATP + MFru6P = MFru16P2

v4

MGraP + MDHAP = MFru16P2

v5

MGraP = MDHAP

v6

M13P2Gri = MNAD + MGraP + MPi

v7

M13P2Gri = MATP + M3PGri

v8

M13P2Gri = M23P2Gri

v9

M23P2Gri = M3PGri + MPi

Global parameters

Assignment rules

MGSSG = (PGStotal - MGSH) / 2.0

PMgAMP = MAMP * PMgf / (PKdAMP + PMgf)

PATPf = MATP - PMgATP

PMgADP = MADP * PMgf / (PKdADP + PMgf)

PMgATP = MATP * PMgf / (PKdATP + PMgf)

MADP = PAtot - MATP - MAMP

PADPf = MADP - PMgADP

PAMPf = MAMP - PMgAMP

PNADH = PNADtot - MNAD

MNADPH = PNADPtot - MNADP

PMg23P2Gri = M23P2Gri * PMgf / (PKd23P2G + PMgf)

Pprotein1f = Pprotein1 / (1.0 + PNADPf / PKd1 + PNADPHf / PKd3)

Pprotein2f = Pprotein2 / (1.0 + PNADPf / PKd2 + PNADPHf / PKd4)

P23P2Grif = M23P2Gri - PMg23P2Gri

Function definitions

Note that constraints are not enforced in simulations. It remains the responsibility of the user to verify that simulation results satisfy these constraints.


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Kinetic hybrid models composed of mechanistic and simplified enzymatic rate laws--a promising method for speeding up the kinetic modelling of complex metabolic networks.

  • Sascha Bulik
  • Sergio Grimbs
  • Carola Huthmacher
  • Joachim Selbig
  • Hermann-Georg Holzhütter
FEBS J. 2009; 276 (2): 410-424
Abstract
Kinetic modelling of complex metabolic networks - a central goal of computational systems biology - is currently hampered by the lack of reliable rate equations for the majority of the underlying biochemical reactions and membrane transporters. On the basis of biochemically substantiated evidence that metabolic control is exerted by a narrow set of key regulatory enzymes, we propose here a hybrid modelling approach in which only the central regulatory enzymes are described by detailed mechanistic rate equations, and the majority of enzymes are approximated by simplified(non mechanistic) rate equations (e.g. mass action, LinLog, Michaelis-Menten and power law) capturing only a few basic kinetic features and hence containing only a small number of parameters to be experimentally determined. To check the reliability of this approach, we have applied it to two different metabolic networks, the energy and redox metabolism of red blood cells, and the purine metabolism of hepatocytes, using in both cases available comprehensive mechanistic models as reference standards. Identification of the central regulatory enzymes was performed by employing only information on network topology and the metabolic data for a single reference state of the network [Grimbs S, Selbig J, Bulik S, Holzhutter HG & Steuer R (2007) Mol Syst Biol 3, 146, doi:10.1038/msb4100186].Calculations of stationary and temporary states under various physiological challenges demonstrate the good performance of the hybrid models. We propose the hybrid modelling approach as a means to speed up the development of reliable kinetic models for complex metabolic networks.

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