Team:Oxford/biosensor characterisation
From 2014.igem.org
(Difference between revisions)
Line 219: | Line 219: | ||
To decide if GFP was produced, we looked at the percentage of “reactions” which were productions, and then we compared this to a second random number (again taken from a uniform distribution from 0 to 1). If the random number was lower, then a GFP was created. If it was higher, then a GFP was degraded. In this way we make a weighted random choice about whether GFP was created or degraded. | To decide if GFP was produced, we looked at the percentage of “reactions” which were productions, and then we compared this to a second random number (again taken from a uniform distribution from 0 to 1). If the random number was lower, then a GFP was created. If it was higher, then a GFP was degraded. In this way we make a weighted random choice about whether GFP was created or degraded. | ||
- | |||
- | |||
<br><br> | <br><br> | ||
<img src="https://static.igem.org/mediawiki/2014/e/e1/Oxford_Matt_equations_3.jpg" style="float:left;position:relative; height:8%; width:47%;" /> | <img src="https://static.igem.org/mediawiki/2014/e/e1/Oxford_Matt_equations_3.jpg" style="float:left;position:relative; height:8%; width:47%;" /> | ||
+ | |||
+ | <br><br> | ||
+ | We only stored the time and amount of GFP when there was a reaction, to save on computation. However this made calculating the mean of realisation harder, but we got over the problem by…. | ||
+ | |||
<br><br> | <br><br> | ||
Stochastic modelling is useful because it can show us the stochastic effects which are often seen in real bacteria. By calculating the variation of the mean of multiple GFP producing bacteria, we can also work out the standard deviation. Then if we assume that the system varies with respect to the normal distribution, we can produce error bounds for the production of GFP. Such that we can say, 90% of the time we can expect the production of GFP from a single bacterium to be within these 2 curves. This could be useful for seeing if results are unexpected, or, if there are multiple outliers, that our model is incorrect. If we average more and more bacteria then the mean curve tend towards the deterministic response. This is to be expected as we are now looking at the system as a whole and fluctuations in the production from individual bacteria are averaged out. In terms of their use, when looking at small amounts of bacterium the stochastic model would be better, because real random fluctuations can be seen. For larger bacterium groups, the deterministic response models the growth very well. The stochastic model can also model large groups but requires large number of realisations which causes simulations to take a lot longer to run. | Stochastic modelling is useful because it can show us the stochastic effects which are often seen in real bacteria. By calculating the variation of the mean of multiple GFP producing bacteria, we can also work out the standard deviation. Then if we assume that the system varies with respect to the normal distribution, we can produce error bounds for the production of GFP. Such that we can say, 90% of the time we can expect the production of GFP from a single bacterium to be within these 2 curves. This could be useful for seeing if results are unexpected, or, if there are multiple outliers, that our model is incorrect. If we average more and more bacteria then the mean curve tend towards the deterministic response. This is to be expected as we are now looking at the system as a whole and fluctuations in the production from individual bacteria are averaged out. In terms of their use, when looking at small amounts of bacterium the stochastic model would be better, because real random fluctuations can be seen. For larger bacterium groups, the deterministic response models the growth very well. The stochastic model can also model large groups but requires large number of realisations which causes simulations to take a lot longer to run. |
Revision as of 18:22, 17 October 2014