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smallbone14

ADHADH1

AcAld + NADH = NAD + EtOH

ADHADH5

AcAld + NADH = NAD + EtOH

AK

{2.0}ADP = AMP + ATP

ATPase

ATP = ADP

ENOENO1

P2G = PEP

ENOENO2

P2G = PEP

FBA

F16bP = DHAP + GAP

GPD

GPD

NADH + DHAP = NAD + G3P

GPM

P3G = P2G

GPP

G3P = GLY

HXKGLK1

ATP + GLC = ADP + G6P

HXKHXK2

HXKHXK2

ATP + GLC = ADP + G6P

HXT

GLCx = GLC

PDCPDC1

PYR = AcAld

PDCPDC5

PYR = AcAld

PDCPDC6

PYR = AcAld

PFK

PFK

ATP + F6P = ADP + F16bP

PGI

G6P = F6P

PGK

ADP + BPG = ATP + P3G

PGM

G6P = G1P

PYK

ADP + PEP = ATP + PYR

TDHTDH1

GAP + NAD = BPG + NADH

TDHTDH2

GAP + NAD = BPG + NADH

TDHTDH3

GAP + NAD = BPG + NADH

TPI

DHAP = GAP

TPP

T6P = TRH

TPS

G6P + UDG = T6P + UDP

UGP

G1P + UTP = UDG

acetatebranch

AcAld + NAD = NADH + ACE

succinatebranch

PYR + {3.0}NAD = {0.75}SUC + {3.0}NADH

udptoutp

ATP + UDP = ADP + UTP

Global parameters

Assignment rules

energycharge = (ADP/2 + ATP)/sumAXP

sumPXG = P2G + P3G

fitconc = Sqrt((1 - (NA*sumPXG*volume)/1618640)^2 + (1 - (NA*volume*DHAP)/3496987)^2 + (1 - (NA*volume*F16bP)/13800392)^2 + (1 - (NA*volume*F6P)/708930)^2 + (1 - (NA*volume*G6P)/2326001)^2 + (1 - (NA*volume*GAP)/951170)^2 + (1 - (NA*volume*GLC)/18909525)^2 + (1 - (NA*volume*PEP)/1836769)^2 + (1 - (NA*volume*PYR)/6348755)^2)/3

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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A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes.

  • Kieran Smallbone
  • Hanan L Messiha
  • Kathleen M Carroll
  • Catherine L Winder
  • Naglis Malys
  • Warwick B Dunn
  • Ettore Murabito
  • Neil Swainston
  • Joseph O Dada
  • Farid Khan
  • Pınar Pir
  • Evangelos Simeonidis
  • Irena Spasić
  • Jill Wishart
  • Dieter Weichart
  • Neil W Hayes
  • Daniel Jameson
  • David S Broomhead
  • Stephen G Oliver
  • Simon J Gaskell
  • John E G McCarthy
  • Norman W Paton
  • Hans V Westerhoff
  • Douglas B Kell
  • Pedro Mendes
FEBS Lett. 2013; 587 (17): 2832-2841
Abstract
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.

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