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vanEunen2

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v_1

∅ = GLCi

v_10

BPG = P3G

v_11

P3G = P2G

v_12

P2G = PEP

v_13

PEP = PYR

v_14

PYR = ACALD

v_15

{2.0}PYR = {3.0}NADH

v_16

ACALD + NADH = ∅

v_17

ACALD = NADH

v_2

GLCi = G6P

v_3

∅ = {2.0}GLCi

v_4

G6P = F6P

v_5

{2.0}G6P = ∅

v_6

F6P = F16P

v_7

F16P = {2.0}TRIO

v_8

NADH + TRIO = ∅

v_9

TRIO = BPG + NADH

Parameters

Global parameters

ACE*

ADP*

AMP*

ATP*

CGAPDH*

CO2*

CPFKAMP*

CPFKATP*

CPFKF16BP*

CPFKF26BP*

CiPFKATP*

ETOH*

EXTERNAL*

F26BP*

GLCo*

GLY*

KACE*

KEQADH*

KEQALD*

KEQENO*

KEQGLT*

KEQHK*

KEQPGI*

KEQPGK*

KEQPGM*

KEQTPI*

KGLY*

KIADHACALD*

KIADHETOH*

KIADHNAD*

KIADHNADH*

KIHKT6P*

KMADHACALD*

KMADHETOH*

KMADHNAD*

KMADHNADH*

KMALDDHAP*

KMALDF16P*

KMALDGAP*

KMALDGAPi*

KMENOP2G*

KMENOPEP*

KMGAPDHBPG*

KMGAPDHGAP*

KMGAPDHNAD*

KMGAPDHNADH*

KMGLTGLCi*

KMGLTGLCo*

KMHKADP*

KMHKATP*

KMHKG6P*

KMHKGLCi*

KMPDCPYR*

KMPFKATP*

KMPFKF6P*

KMPGIF6P*

KMPGIG6P*

KMPGKADP*

KMPGKATP*

KMPGKBPG*

KMPGKP3G*

KMPGMP2G*

KMPGMP3G*

KMPYKADP*

KMPYKATP*

KMPYKF16P*

KMPYKPEP*

KPFKAMP*

KPFKF16BP*

KPFKF26BP*

KSUC*

KTRE1*

KTRE2*

KiPFKATP*

L0*

L10*

NADt*

NHPDC*

T6P*

TREH*

VMAXADH*

VMAXALD*

VMAXENO*

VMAXGAPDHf*

VMAXGAPDHr*

VMAXGLT*

VMAXHK*

VMAXPDC*

VMAXPFK*

VMAXPGI*

VMAXPGK*

VMAXPGM*

VMAXPYK*

gR*

n10*

default_compartment*

Constraints

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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Show wireframe*

Show modifiers*

Show compartments*

Gravity*

Repulsion*

Pool threshold*

Species:

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Model schema*

Parameter*

Start*

End*

Steps*

Species

Rates

Assignment rules

Testing biochemistry revisited: how in vivo metabolism can be understood from in vitro enzyme kinetics.

Karen van Eunen
José A L Kiewiet
Hans V Westerhoff
Barbara M Bakker

PLoS Comput. Biol. 2012; 8 (4):

PubMed: 22570597
PubMed Central: PMC3343101
DOI: 10.1371/journal.pcbi.1002483
ISSN: 1553-7358
URL: http://ncbi.nlm.nih.gov/pubmed/22570597

Abstract

A decade ago, a team of biochemists including two of us, modeled yeast glycolysis and showed that one of the most studied biochemical pathways could not be quite understood in terms of the kinetic properties of the constituent enzymes as measured in cell extract. Moreover, when the same model was later applied to different experimental steady-state conditions, it often exhibited unrestrained metabolite accumulation.Here we resolve this issue by showing that the results of such ab initio modeling are improved substantially by (i) including appropriate allosteric regulation and (ii) measuring the enzyme kinetic parameters under conditions that resemble the intracellular environment. The following modifications proved crucial: (i) implementation of allosteric regulation of hexokinase and pyruvate kinase, (ii) implementation of V(max) values measured under conditions that resembled the yeast cytosol, and (iii) redetermination of the kinetic parameters of glyceraldehyde-3-phosphate dehydrogenase under physiological conditions.Model predictions and experiments were compared under five different conditions of yeast growth and starvation. When either the original model was used (which lacked important allosteric regulation), or the enzyme parameters were measured under conditions that were, as usual, optimal for high enzyme activity, fructose 1,6-bisphosphate and some other glycolytic intermediates tended to accumulate to unrealistically high concentrations. Combining all adjustments yielded an accurate correspondence between model and experiments for all five steady-state and dynamic conditions. This enhances our understanding of in vivo metabolism in terms of in vitro biochemistry.

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