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<p>The model was run for a range of different enzyme ratios, maintaining a constant total amount of enzyme</p> | <p>The model was run for a range of different enzyme ratios, maintaining a constant total amount of enzyme</p> | ||
− | <p>The concentration of Glucose used in the model was set to the concentration | + | <p>The concentration of Glucose used in the model was set to the concentration deemed to be in the diabetic range (11.4mM<sup>[3]</sup>). </p> |
$$ [E_{total}] = [GOx] + [HRP]$$ | $$ [E_{total}] = [GOx] + [HRP]$$ | ||
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$$[ABTS_{Oxidised}]_{t_{max}} > [ABTS_{Oxidised}]_{min}$$ | $$[ABTS_{Oxidised}]_{t_{max}} > [ABTS_{Oxidised}]_{min}$$ | ||
− | <p>The cost of the simulation was estimated assuming the cost of everything but the | + | <p>The cost of the simulation was estimated assuming the cost of everything but the enzymes were negligible</p> |
$$Cost_{total} = Cost_{GOx} [GOx] + Cost_{HRP} [HRP]$$ | $$Cost_{total} = Cost_{GOx} [GOx] + Cost_{HRP} [HRP]$$ | ||
− | <p>The highest total cost from all the ensemble runs was treated as the final value and Total Cost vs Fraction of GOx was then plotted</p> | + | <p>The highest total cost from all the ensemble runs was treated as the final value and Total Cost vs Fraction of GOx was then plotted, note that 200 samples where used</p> |
+ | |||
</br> | </br> | ||
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<div class="column half_size"> | <div class="column half_size"> | ||
<img class="full" src="https://static.igem.org/mediawiki/2016/3/37/T--Manchester--CostGOx001.jpg" alt="Probability density function for the Km of Horseradish Peroxidase" /> | <img class="full" src="https://static.igem.org/mediawiki/2016/3/37/T--Manchester--CostGOx001.jpg" alt="Probability density function for the Km of Horseradish Peroxidase" /> | ||
− | <p>Figure 1 shows the | + | <p>Figure 1 shows the total costs when the total amount of enzyme in the system is 0.01 mM </p> |
</div> | </div> | ||
<div class="column half_size"> | <div class="column half_size"> | ||
<img class="full" src="https://static.igem.org/mediawiki/2016/7/7e/T--Manchester--CostGOx0015.jpg" alt="Probability density function for the Km of Horseradish Peroxidase" /> | <img class="full" src="https://static.igem.org/mediawiki/2016/7/7e/T--Manchester--CostGOx0015.jpg" alt="Probability density function for the Km of Horseradish Peroxidase" /> | ||
− | <p>Figure 2 shows the | + | <p>Figure 2 shows the total costs when the total amount of enzyme in the system is 0.015 mM </p> |
</div> | </div> | ||
<div class="column half_size"> | <div class="column half_size"> | ||
<img class="full" src="https://static.igem.org/mediawiki/2016/0/0c/T--Manchester--CostGOx002.jpg" alt="Probability density function for the Km of Horseradish Peroxidase" /> | <img class="full" src="https://static.igem.org/mediawiki/2016/0/0c/T--Manchester--CostGOx002.jpg" alt="Probability density function for the Km of Horseradish Peroxidase" /> | ||
− | <p>Figure 3 shows the | + | <p>Figure 3 shows the total costs when the total amount of enzyme in the system is 0.02 mM </p> |
</div> | </div> | ||
<div class="column half_size"> | <div class="column half_size"> | ||
<img class="full" src="https://static.igem.org/mediawiki/2016/5/58/T--Manchester--CostGOx0025.jpg" alt="Probability density function for the Km of Horseradish Peroxidase" /> | <img class="full" src="https://static.igem.org/mediawiki/2016/5/58/T--Manchester--CostGOx0025.jpg" alt="Probability density function for the Km of Horseradish Peroxidase" /> | ||
− | <p>Figure 4 shows the | + | <p>Figure 4 shows the total costs when the total amount of enzyme in the system is 0.025 mM </p> |
</div> | </div> | ||
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<p> | <p> | ||
− | From the graphs you can clearly see that the minimum cost can be achieved with the range GOx fraction is approximate 0.6 and the total amount of enzyme is 0.01 mM. The ranges identified as of interest should be tested experimentally to further validate these results. The range that satisfies the constraints is | + | From the graphs you can clearly see that the minimum cost can be achieved with the range GOx fraction is approximate 0.6 and the total amount of enzyme is 0.01 mM. The ranges identified as of interest should be tested experimentally to further validate these results. The range that satisfies the constraints is small at low amounts of total enzyme and increases with increasing total amount of enzyme. Depending on the tolerances of the patch production process it may be favourable to chose a large total amount of enzyme to increase the acceptable range so that the patch remains within specification, accounting for manufacturing. </br> It can also be seen that the optimum solution is always the maximum acceptable amount of GOx. |
</p> | </p> | ||
Revision as of 23:07, 19 October 2016
Cost Analysis
Overview and Motivation
During our human practices discussion with the Police it was brought to our attention that not all officers carry a breathalyser device and they are rather bulky: our patch could be an alternative, compact solution that all officers could carry provided it was suitable.
This obviously introduces constraints to the device if it is to be suitable for this purpose.
Constraint Summary | Constraint | Reasoning | Value |
---|---|---|---|
Maximum expression time | The AlcoPatch would need to equally as fast as current methods, if not faster. This would increase the likelihood of uptake as it is an improvement on the current portable methods of blood alcohol detection | Requirement suggested during the discussion | 120 seconds |
Minimum expression amount | Expression needs to be high enough that the result can be seen since the device it designed to be portable and used 'roadside' where lighting conditions may not be ideal | reference[1] | 17.4 mM |
Methodology
The model was run for a range of different enzyme ratios, maintaining a constant total amount of enzyme
The concentration of Glucose used in the model was set to the concentration deemed to be in the diabetic range (11.4mM[3]).
$$ [E_{total}] = [GOx] + [HRP]$$All simulations that didn't violate the constraints were recorded
$$[ABTS_{Oxidised}]_{t_{max}} > [ABTS_{Oxidised}]_{min}$$The cost of the simulation was estimated assuming the cost of everything but the enzymes were negligible
$$Cost_{total} = Cost_{GOx} [GOx] + Cost_{HRP} [HRP]$$The highest total cost from all the ensemble runs was treated as the final value and Total Cost vs Fraction of GOx was then plotted, note that 200 samples where used
Return to top of pageResults
Figure 1 shows the total costs when the total amount of enzyme in the system is 0.01 mM
Figure 2 shows the total costs when the total amount of enzyme in the system is 0.015 mM
Figure 3 shows the total costs when the total amount of enzyme in the system is 0.02 mM
Figure 4 shows the total costs when the total amount of enzyme in the system is 0.025 mM
Conclusions
From the graphs you can clearly see that the minimum cost can be achieved with the range GOx fraction is approximate 0.6 and the total amount of enzyme is 0.01 mM. The ranges identified as of interest should be tested experimentally to further validate these results. The range that satisfies the constraints is small at low amounts of total enzyme and increases with increasing total amount of enzyme. Depending on the tolerances of the patch production process it may be favourable to chose a large total amount of enzyme to increase the acceptable range so that the patch remains within specification, accounting for manufacturing. It can also be seen that the optimum solution is always the maximum acceptable amount of GOx.
Return to top of page Return to overviewReferences
- Wallach, Jacques. Interpretation of Diagnostic Tests. Disorders due to Physical and Chemical agents. 8thed. Lipincott Williams and Wilkins: 2007
- Moyer, J., Wilson, D., Finkelshtein, I., Wong, B. and Potts, R., 2012. Correlation between sweat glucose and blood glucose in subjects with diabetes. Diabetes technology & therapeutics, 14(5), pp.398-402..
- Goldberg, R.B., 2009. Cytokine and cytokine-like inflammation markers, endothelial dysfunction, and imbalanced coagulation in development of diabetes and its complications. The Journal of Clinical Endocrinology & Metabolism, 94(9), pp.3171-3182.