# Team:Manchester/Model/Costing

Manchester iGEM 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 Literature Value[1] 17.4 mM

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[2]. The concentration above which you would start to expect diabetes is 11.4mM[3]. In the lab we had expression at much lower concentrations link to this data.

This analysis is included as a proof of in this case for a diabetes bio-sensor. In general for detecting compounds in sweat using oxidase type reactions.

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Methodology

1) Generate probability distributions for each kinetic parameter required from our collected data.

2) Simulate the model with different sets of kinetic values that are sampled from probability distributions. In our study, 200 samples for each reaction were modelled, i.e. the model was simulated with 200 different sets of kinetic values.

3) The model was run for a range of different enzyme ratios, maintaining a constant total amount of enzyme

4) 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

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Results

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

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Conclusions

From the graphs you can clearly see that for the total amounts of enzymes tested 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.

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# References

• 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.