Mitocondrial pyruvate metabolism

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Mitocondrial pyruvate metabolism(MPM) is a pseudo reaction that represents the total ATP production from one unit of pyruvate in the mitochondrian.

Chemical reaction

 Pyruvate  + 13ADP + 13Pi \leftrightarrow 13ATP

Rate equation

  • Chemical reactions proceed to equilibrium within closed systems. For a simple reaction A \rightarrow B it is defined as K_{eq} = \frac{[B]_{eq}}{[A]_{eq}} where forward and reverse rates are equal.
  • Equilibrium is not reached in open system due to influx and outflux. Mass action ratio[1] \tau for A \rightarrow B reaction is defined as \tau = \frac{[B]_{ob}}{[A]_{ob}} where subscript ob represents observable at a given point.
  • Deviation from equilibrium is measured with Disequilibrium constant \rho as \rho = \frac{\tau}{K_{eq}}
  • Given the simple uni molecular reaction A \leftrightarrow B the mass action equation can be modified as
v=K_1A-K_2B

 v=K_1A \left(1-\frac{K_2B}{K_1A} \right)

Considering K_{eq} = \frac{K_1}{K_2} and \tau = \frac{P_1P_2 \ldots}{S_1S_2 \ldots} we have,

 v=K_1A \left(1-\frac{\tau}{K_{eq}} \right)

  • The generalized arbitrary mass action ratio gives us
 v = K_1A^{n_1}B^{n_2} \ldots \left(1-\frac{\tau}{K_{eq}} \right)
Or,

 v = K_1A^{n_1}B^{n_2} \ldots (1 - \rho)

For eg. for the reaction 2A + B \leftrightarrow C + 2D, the rate law would be K_1A^2B \left(1-\rho\right)

  • This equation demonstrates how a rate expression can be divided into parts that include both kinetics and thermodynamic properties [2].
  • Given the net rate of reaction  v = v_f \left( 1 - \frac{v_r}{v_f} \right), we have
 v = v_f \left(1 - \rho \right)

In this model

  • The rate law is defined as
    v = K_1[Pyruvate][ADP]^{13}[Pi]^{13}\left(1-\frac{\frac{[ATP]^{13}}{[Pyruvate][ADP]^{13}[Pi]^{13}}}{K_{eq}}\right)

  • The overall standard free-energy change for Pyruvate metabolism is \Delta G^o{'}= -50.3 Kj/Mol[3][4]. Note The overall standard free energy is calculated by adding the standard free energy for all the reactions in TCA cycle.
Calculating K_{eq} value from these free energy gives \Delta G' = - 50.3\ kJ.mol^{-1}, Failed to parse (Cannot store math image on filesystem.): Keq = exp(-\frac{\Delta G'}{RT}) = exp(\frac{50300}{8.31*298.15}) \approx 654904512.15 . In order to ensure that the uncertainty does not affect the model equilibrium a small uncertainty of 5% can be considered for transporter. In our model we have applied this approach. So Failed to parse (Cannot store math image on filesystem.): K_{eq} = 654904512.15 \pm 32745226 .
  • The Flux of pyruvate consumed by mitochondria measured for AS_30D is  v = 1.8 [5].
  • The steady state concentrations for substrates and products are ATP=8.7 \pm 3 (5), ADP = 2.7 \pm 1.3, Pyruvate = 8.5 \pm 3.6 and Pi = 7.5.
  • The K_1 value calculated from the above mentioned values are Failed to parse (Cannot store math image on filesystem.): 7.78E-015
  • To calculate the uncertainty of K_1 we first looked at the uncertainty on the substrate and product concentration. The maximum uncertainty reported for these values are \approx 50%. In our model we considered this 50% uncertainty in its mean value giving value of 7.78E^{-015} \pm 3.89E^{-015}
  • Alternative The above mentioned formulate fails to reach steady state. So a constant flux mentioned in Hernandez et. al. can be used.

Parameter values

Parameter Value Organism Remarks
K_1 7.78E^{-015}
K_{eq} 654904512.15

Parameters with uncertainty

Parameter Value Organism Remarks
K_1 7.78E^{-015} \pm 3.89E^{-015}
K_{eq} 654904512.15 \pm 32745226


References

  1. Hess B. and Brand K. (1965), Enzymes and metabolite profiles. In Control of energy metabolism. III. Ed. B. Chance, R. K. Estabrook and J. R. Williamson. New York: Academic Press
  2. Sauro H M, Enzyme Kinetics for Systems Biology, Second Edition, Ambrosius Publishing (2013), ISBN-10: 0-9824773-3-3
  3. Nelson D. and Cox M. (2008), Lehninger Principles of Biochemistry, Fight Edition, W.H. Freeman and Company, ISBN-10: 071677108X
  4. http://crystal.res.ku.edu/taksnotes/Biol_638/notes/chp_16.pdf
  5. Marín-Hernández A, Gallardo-Pérez JC, Rodríguez-Enríquez S et al (2011) Modeling cancer glycolysis. Biochim Biophys Acta 1807:755–767 (doi)