Difference between revisions of "Kinetic model of Central Metabolism"

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(Quantifying the flux)
(Quantifying the flux)
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<math>K_X = 0.0017 min^{-1}</math><br>
 
<math>K_X = 0.0017 min^{-1}</math><br>
  
Now we know, <math>f_X = K_X \times X^T</math>. The intracellular concentration of <math>X^T = 3 \frac{mmol}{vol}</math>.
+
Now we know, <math>f_X = K_X \times X^T</math>. The intracellular concentration of <math>X^T = 3 \text{mM or}\frac{mmol}{vol}</math>.
  
 
So, <math>f_x = K_X \times X^T</math><br>
 
So, <math>f_x = K_X \times X^T</math><br>

Revision as of 11:36, 11 November 2014

A kinetic model of glycolysis with serine activation is constructed from the literature data [1][2][3][4][5].

Description of the model

Schematic diagram of the model is given here. The dotted line represents activation(+) or inhibition(-) and the dashed arrow indicate Pentose Phosphate Pathway reactions not included in the model. Click on a reaction to have more information

GLUTHKHPIPFK-1ALDOTPIGAPDHPGKPGAMENOPYKLDHMCTAKATPasePPPTKGPGSDHasesNDKPPASEUPPPGLMSHMTSERoutGlYCoutPSPPSAPDHMPMGlycolysis with Serine Activation
About this image


Reactions

Details of the abbreviations for this model is listed here. Reactions of the model are listed below.

Initial concentration of the metabolites can be found here

Model File

The SBML file of the model can be found here

Notes

Necessary observation during model building and simulation is noted here.

Global parameters

Calculating V_{max}

Some of the Vmax value in the paper "Modeling cancer glycolysis" is given in  U \times \text{(mg total cellular protein)}^{-1} unit [1]. To homogenize the units it is then converted back to \frac{mM}{min} by multiplying with 65 as the HeLa cell was incubated in  65 \frac{\text{mg protein}}{\text{mL cell volume}}.

  • Converting  U \times \text{(mg total cellular protein)}^{-1} to mM \times min^{-1}:

 U \times \text{(mg total cellular protein)}^{-1}  = \frac{\mu mole}{min \cdot \text{(mg total cellular protein)}} = \frac{10^{-6} mole}{min \cdot \text{(mg total cellular protein)}}
Also, \frac{mM}{min} = \frac{10^{-3} mole}{min \cdot liter}
We want to convert 1  U \times \text{(mg total cellular protein)}^{-1} to mM \times min^{-1} where HeLa cell was incubated in  65 \frac{\text{mg protein}}{\text{mL cell volume}}
Therefore,   \frac{ 1 \times 10^{-6} mole}{ min \times \text{(mg total cellular protein)}} \times \frac{65 \text{(mg total cellular protein)}}{10^{-3} \text{(L cellular protein)}}
=>   \frac{ 1 \times 10^{-3} \times 10^{-3} mole}{ min \times \text{(mg total cellular protein)}} \times \frac{65 \text{(mg total cellular protein)}}{10^{-3} \text{(L cellular protein)}}
Striking out the necessary elements we get  \frac{65 \times 10^{-3} mole}{L \times min}
=>  65 mM \times min^{-1}


Quantifying the flux

We used the Kinetic Flux Profiling method to quantify the flux values.

  • First we determine the unlabeled from for each metabolites

\text{Fraction Unlabeled} = \frac{\text{Peak Height}_{\text{unlabeled form}}}{\text{Sum of peak height}_{\text{every isotopic form}}}

  • If we can calculate the apparent first order rate constant K_X then the flux can be calculated as f_X = K_X \times X^T [X^T is the intracellular concentration of X ].
  • With X^T at steady-state, we have

\frac{dX^{U}}{dt} = -f_X (X^{U}/X^{T})

The analytical solution is \frac{X^U}{X^T} = exp(-f_Xt/X^T)

setting K_X = \frac{f_X}{X^T}, we get \frac{X^U}{X^T} = exp(-K_Xt)

Example:
To explain the procedure we took the example of Pyruvate in the presence of Serine and Glycine. \frac{X^U}{X^T} = \frac{5 \times 10^4}{ 1.8 \times 10^5}

So,
\frac{5 \times 10^4}{ 1.8 \times 10^5} = exp(-K_Xt)
=> 0.277 = exp(-K_Xt)
=> log_e(0.277) = -K_Xt
=> K_x = \frac{log_e(0.277)}{t}
K_X = 0.0017 min^{-1}

Now we know, f_X = K_X \times X^T. The intracellular concentration of X^T = 3 \text{mM or}\frac{mmol}{vol}.

So, f_x = K_X \times X^T
f_x = 0.0017 \times 3 \frac{mmol}{vol \times min}

References

  1. 1.0 1.1 Marín-Hernández A, Gallardo-Pérez JC, Rodríguez-Enríquez S et al (2011). Modeling cancer glycolysis. Biochim Biophys Acta, 1807:755–767 (doi)
  2. Turnaev II, Ibragimova SS, Usuda Y et al (2006). Mathematical modeling of serine and glycine synthesis regulation in Escherichia coli. Proceedings of the fifth international conference on bioinformatics of genome regulation and structure 2:78–83
  3. Smallbone K, Stanford NJ (2013). Kinetic modeling of metabolic pathways: Application to serine biosynthesis. In: Systems Metabolic Engineering, Humana Press. pp. 113–121
  4. Palm, D.C. (2013). The regulatory design of glycogen metabolism in mammalian skeletal muscle (Ph.D.). University of Stellenbosch
  5. Ettore Murabito (2010). Application of differential metabolic control analysis to identify new targets in cancer treatment (Ph.D.). University of Manchester