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This Human practice link was about alcohol and we ran a glucose experiment. However this link inspired us to think about Diabetes | This Human practice link was about alcohol and we ran a glucose experiment. However this link inspired us to think about Diabetes | ||
− | It has been found blood glucose concentration is similar to sweat glucose concentration (cite 1). The concentration above which you would start to expect diabetes is 11.4mM (cite 2). In the lab we had expression at much lower concentrations <a href="vlah">link to this data</a>. | + | It has been found blood glucose concentration is similar to sweat glucose concentration (cite 1). The concentration above which you would start to expect diabetes is 11.4mM (cite 2). In the lab we had expression at much lower concentrations <a href="vlah">link to this data</a>. |
− | </br></br> | + | </br></br>This analysis is included as a proof of concept of the system and for diabetes bio-sensors. </p> |
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Revision as of 21:39, 19 October 2016
Cost Analysis
Contents
Overview and Motivation Methodology Results ConclusionsOverview 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 | cite 0 | 17.4 mM |
Wallach, Jacques. Interpretation of Diagnostic Tests. Disorders due to Physical and Chemical agents. 8thed. Lipincott Williams and Wilkins: 2007.(cite0) 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.(cite2) 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.. (cite1) This Human practice link was about alcohol and we ran a glucose experiment. However this link inspired us to think about Diabetes It has been found blood glucose concentration is similar to sweat glucose concentration (cite 1). The concentration above which you would start to expect diabetes is 11.4mM (cite 2). In the lab we had expression at much lower concentrations link to this data. This analysis is included as a proof of concept of the system and for diabetes bio-sensors.
Return to top of pageMethodology
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 equivalent to the sweat alcohol concentration of someone who was on the legal limit of drink driving.
$$ [E_{total}] = [GOx] + [HRP]$$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 enzyme costs are negligible
$$Cost_{total} = Cost_{GOx} [GOx] + Cost_{HRP} [HRP]$$The highest total cost from all the ensemble runs was treated as the final value andTotal Cost vs Fraction of GOx was then plotted
Return to top of pageResults
![Probability density function for the Km of Horseradish Peroxidase](https://static.igem.org/mediawiki/2016/3/37/T--Manchester--CostGOx001.jpg)
Figure 1 shows the patch costs when the total amount of enzyme in the system is 0.01 mM
![Probability density function for the Km of Horseradish Peroxidase](https://static.igem.org/mediawiki/2016/7/7e/T--Manchester--CostGOx0015.jpg)
Figure 2 shows the patch costs when the total amount of enzyme in the system is 0.015 mM
![Probability density function for the Km of Horseradish Peroxidase](https://static.igem.org/mediawiki/2016/0/0c/T--Manchester--CostGOx002.jpg)
Figure 3 shows the patch costs when the total amount of enzyme in the system is 0.02 mM
![Probability density function for the Km of Horseradish Peroxidase](https://static.igem.org/mediawiki/2016/5/58/T--Manchester--CostGOx0025.jpg)
Figure 4 shows the patch 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 fairly small at low amounts of total enzyme and increases with increasing total amount of enzyme. Depending on the tolerances of the patch production it may be favourable to chose a large total amount of enzyme to increase the acceptable range so that the patch remains within specification. It can also be seen that the optimum solution is always the maximum acceptable amount of GOx.
Return to top of page Return to overview